yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-r05-b9049e57
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T02:04:46.547Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
## Step 2 · user · 2026-09-29T02:04:46.589Z
# Improve a production music-discovery two-tower
You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team's next launch gate.
Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.
The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.
## Production Context
The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.
The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.
## What You Have
All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` window. For the final submission, that
validation window becomes observed history, and another 30-minute gap precedes
the hidden seven-day outcome window.
The package includes:
- `/task/current_model/train_current_model.py`: the production training and
ranking script. It can reproduce the public validation ranking and produce a
hidden-test submission after refitting.
- `/task/current_model/two_tower_model.py`: model primitives used by the
production script.
- `/task/evaluate_public.py`: the public validation evaluator for development.
- `/task/data/validation_eligible_candidates.npz` and
`/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for
the validation and hidden-test cutoffs.
- `/task/data/validation_preference_state.parquet` and
`/task/data/test_preference_state.parquet`: active explicit preference state
at the corresponding cutoffs.
- `/task/requirements.txt`: the exact offline Python distribution inventory.
You can reproduce the production public backtest with:
```sh
python /task/current_model/train_current_model.py \
--stage validation \
--public-dir /task \
--output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet
```
## Data
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action came
through a recommendation surface. A listen is a conventional positive at 50%
completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. For model development and selection, train only from `train_*` and score the
subsequent `val_*` outcomes.
2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
Validation candidates exclude all `train_*` encounters. Test candidates exclude
all `train_*` and `val_*` encounters. Future test interactions are hidden and
used only by the separate Harbor verifier.
## Discovery Objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.
## Deliverable
Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.
The ranking must:
- include the columns `uid`, `item_id`, and `rank`; only these columns are
evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
candidate set.
Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model-provider
connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
the deadline.
## Step 3 · agent · 2026-09-29T02:04:50.875Z
### Recorded reasoning summary
Processing Task Parameters
I am carefully reviewing the task details to grasp the environment, requirements, and constraints fully. My immediate next step is to initiate the narrative flow by calling `update_topic`.
### Recorded reasoning summary
Defining Task Scope
I've analyzed the core deliverables: a submission file, the training script with its dependencies, and a README detailing changes and performance. My current focus is on understanding the production pipeline's constraints, particularly the two-tower serving and read-only environment, before proceeding to strategy.
### update_topic · 2026-09-29T02:04:50.875Z
{
"title": "Research and Baseline Reproduction",
"strategic_intent": "Investigate the codebase, explore files under /task, reproduce the baseline public backtest, and understand the model architecture and negative sampling.",
"summary": "Starting research phase. We will inspect /task/current_model/train_current_model.py, two_tower_model.py, evaluate_public.py, and related data files. We will also run the baseline reproduction to get the baseline validation score and check runtime."
}
### Tool output · 2026-09-29T02:04:50.884Z
Current topic: "Research and Baseline Reproduction"
Topic summary: Starting research phase. We will inspect /task/current_model/train_current_model.py, two_tower_model.py, evaluate_public.py, and related data files. We will also run the baseline reproduction to get the baseline validation score and check runtime.
Strategic Intent: Investigate the codebase, explore files under /task, reproduce the baseline public backtest, and understand the model architecture and negative sampling.
## Step 4 · agent · 2026-09-29T02:04:52.573Z
### list_directory · 2026-09-29T02:04:52.573Z
{
"dir_path": "/task"
}
### Tool error · 2026-09-29T02:04:52.576Z
Path not in workspace: Attempted path "/task" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app
### list_directory · 2026-09-29T02:04:52.573Z
{
"dir_path": "/task/current_model"
}
### Tool error · 2026-09-29T02:04:52.576Z
Path not in workspace: Attempted path "/task/current_model" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app
## Step 5 · agent · 2026-09-29T02:04:55.189Z
### Recorded reasoning summary
Exploring Task Directory
I'm investigating the reach of `list_directory`, specifically its constraints to `/app` or `/home/agent/.gemini/tmp/app`. My current focus is on testing whether `run_shell_command` can access the `/task` directory. To do this, I've initiated a `ls -la /task` command.
### run_shell_command · 2026-09-29T02:04:55.189Z
{
"command": "ls -la /task",
"description": "List files in /task directory"
}
### Tool output · 2026-09-29T02:04:55.217Z
Output: total 40
dr-xr-xr-x 1 root root 4096 Sep 29 02:01 .
drwxr-xr-x 1 root root 4096 Sep 29 02:04 ..
-r--r--r-- 1 root root 0 Sep 29 01:59 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:59 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 02:00 data
-r--r--r-- 1 root root 5774 Sep 29 01:59 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:59 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:59 requirements.txt
Process Group PGID: 1612
## Step 6 · agent · 2026-09-29T02:04:56.985Z
### read_file · 2026-09-29T02:04:56.985Z
{
"file_path": "/task/evaluate_public.py",
"start_line": 1,
"end_line": 50
}
### Tool error · 2026-09-29T02:04:56.987Z
Path not in workspace: Attempted path "/task/evaluate_public.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app
## Step 7 · agent · 2026-09-29T02:05:05.559Z
### Recorded reasoning summary
Analyzing Task Data
I'm reviewing the provided task data, specifically the `/task` directory. It appears I can only access files within the `/app` or `/home/agent/.gemini/tmp/app` workspaces. Therefore, I need to copy any necessary files from `/task` into one of these designated locations to proceed with analysis.
### run_shell_command · 2026-09-29T02:05:05.559Z
{
"command": "ls -la /task/current_model && ls -lh /task/data",
"description": "Inspect contents of /task/current_model and /task/data"
}
### Tool output · 2026-09-29T02:05:05.578Z
Output: total 104
dr-xr-xr-x 1 root root 4096 Sep 29 01:59 .
dr-xr-xr-x 1 root root 4096 Sep 29 02:01 ..
-r--r--r-- 1 root root 80058 Sep 29 01:59 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 01:59 two_tower_model.py
total 340M
-r--r--r-- 1 root root 342K Sep 29 01:59 candidate_catalog.parquet
-r--r--r-- 1 root root 83K Sep 29 01:59 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 01:59 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 02:00 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 01:59 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 01:59 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 01:59 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 01:59 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 01:59 train_unlikes.parquet
-r--r--r-- 1 root root 36K Sep 29 01:59 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 01:59 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 01:59 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 01:59 val_undislikes.parquet
-r--r--r-- 1 root root 71K Sep 29 01:59 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 01:59 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 02:00 validation_preference_state.parquet
Process Group PGID: 1615
## Step 8 · agent · 2026-09-29T02:05:26.198Z
### Recorded reasoning summary
Examining Scripts
I am currently reviewing the contents of `evaluate_public.py` and `ranking_evaluation.py`. My next step involves copying `train_current_model.py` and `two_tower_model.py` from `/task/current_model/` to `/app/solution/`.
I will copy the production model source files from `/task/current_model/` into `/app/solution/` so that we can inspect, edit, and run them within the workspace.
### run_shell_command · 2026-09-29T02:05:26.198Z
{
"description": "Copy current_model files to /app/solution/",
"command": "cp /task/current_model/* /app/solution/"
}
### Tool output · 2026-09-29T02:05:26.211Z
Output: (empty)
Process Group PGID: 1619
## Step 9 · agent · 2026-09-29T02:05:28.620Z
### run_shell_command · 2026-09-29T02:05:28.620Z
{
"description": "Check evaluate_public.py contents",
"command": "head -n 50 /task/evaluate_public.py"
}
### Tool output · 2026-09-29T02:05:28.631Z
Output: #!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
Process Group PGID: 1622
## Step 10 · agent · 2026-09-29T02:05:31.126Z
### run_shell_command · 2026-09-29T02:05:31.126Z
{
"description": "Check CPU count, available RAM, and Python version",
"command": "nproc && free -h && python3 --version"
}
### Tool output · 2026-09-29T02:05:31.139Z
Output: 8
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1625
## Step 11 · agent · 2026-09-29T02:05:37.722Z
### run_shell_command · 2026-09-29T02:05:37.722Z
{
"description": "Check total memory and python version",
"command": "cat /proc/meminfo | grep MemTotal && python3 --version"
}
### Tool output · 2026-09-29T02:05:37.738Z
Output: MemTotal: 15988968 kB
Python 3.12.13
Process Group PGID: 1629
## Step 12 · agent · 2026-09-29T02:05:40.665Z
### Recorded reasoning summary
Running Baseline Reproduction
I am currently executing the baseline reproduction command as outlined. The system has 8 CPU cores and approximately 16 GB of RAM available for this process. I will report back once this initial step is complete.
### run_shell_command · 2026-09-29T02:05:40.665Z
{
"description": "Count lines in model files",
"command": "wc -l /app/solution/train_current_model.py /app/solution/two_tower_model.py"
}
### Tool output · 2026-09-29T02:05:40.677Z
Output: 2164 /app/solution/train_current_model.py
433 /app/solution/two_tower_model.py
2597 total
Process Group PGID: 1634
## Step 13 · agent · 2026-09-29T02:05:42.587Z
### read_file · 2026-09-29T02:05:42.587Z
{
"end_line": 150,
"start_line": 1,
"file_path": "/app/solution/two_tower_model.py"
}
### Tool output · 2026-09-29T02:05:42.593Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn
DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100
@dataclass(frozen=True)
class TowerConfig:
sequence_length: int = 100
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
seed: int = 29
@dataclass(frozen=True)
class ListenEvents:
timestamps: np.ndarray
items: np.ndarray
organic: np.ndarray
completion_pct: np.ndarray
@dataclass(frozen=True)
class ExplicitEvents:
timestamps: np.ndarray
items: np.ndarray
channel: np.ndarray
active: np.ndarray
organic_like: np.ndarray
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
"""Return four standardized features built only from the public catalog."""
positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
features = np.column_stack(
[
np.log1p(positives),
np.log1p(organic),
organic / np.maximum(positives, 1.0),
1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
]
)
means = features.mean(axis=0, keepdims=True)
scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
standardized = ((features - means) / scales).astype(np.float32)
if not np.isfinite(standardized).all():
raise RuntimeError("catalog features contain non-finite values")
return standardized
def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
if len(parts) == 1:
return parts[0]
timestamps = np.concatenate([part.timestamps for part in parts])
items = np.concatenate([part.items for part in parts])
organic = np.concatenate([part.organic for part in parts])
completion = np.concatenate([part.completion_pct for part in parts])
order = np.argsort(timestamps, kind="stable")
return ListenEvents(
timestamps=timestamps[order],
items=items[order],
organic=organic[order],
completion_pct=completion[order],
)
def iter_listen_users(
path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Stream one uid-sorted public listen file one user at a time."""
parquet = pq.ParquetFile(path)
pending_uid: int | None = None
pending: list[ListenEvents] = []
previous_uid = -1
for batch in parquet.iter_batches(
batch_size=750_000,
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
):
frame = batch.to_pandas()
mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
if not np.any(keep):
continue
uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
items = mapped[keep].astype(np.int32, copy=False)
organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
if np.any(uids[1:] < uids[:-1]):
raise RuntimeError(f"{path.name} is not sorted by uid")
boundaries = np.r_[
0,
np.flatnonzero(uids[1:] != uids[:-1]) + 1,
len(uids),
]
for start, end in zip(boundaries[:-1], boundaries[1:]):
uid = int(uids[start])
part = ListenEvents(
timestamps[start:end].copy(),
items[start:end].copy(),
organic[start:end].copy(),
completion[start:end].copy(),
)
if pending_uid is None:
pending_uid = uid
elif uid != pending_uid:
if pending_uid < previous_uid:
raise RuntimeError(f"{path.name} user order regressed")
yield pending_uid, _combine_listen_chunks(pending)
previous_uid = pending_uid
pending_uid = uid
## Step 14 · agent · 2026-09-29T02:05:44.225Z
### read_file · 2026-09-29T02:05:44.225Z
{
"start_line": 151,
"end_line": 300,
"file_path": "/app/solution/two_tower_model.py"
}
### Tool output · 2026-09-29T02:05:44.229Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-300 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 301.
--- FILE CONTENT (truncated) ---
pending = []
pending.append(part)
if pending_uid is not None:
yield pending_uid, _combine_listen_chunks(pending)
def merge_listen_streams(
paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Merge uid-sorted public listen files without materializing them."""
streams = [iter(iter_listen_users(path, item_index)) for path in paths]
heads: list[tuple[int, ListenEvents] | None] = [
next(stream, None) for stream in streams
]
while any(head is not None for head in heads):
uid = min(head[0] for head in heads if head is not None)
parts: list[ListenEvents] = []
for position, head in enumerate(heads):
if head is not None and head[0] == uid:
parts.append(head[1])
heads[position] = next(streams[position], None)
yield uid, _combine_listen_chunks(parts)
def load_explicit_events(
data: Path,
prefixes: tuple[str, ...],
item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
"""Load public explicit actions as chronological state changes."""
specifications = (
("likes", 0, 1, 0),
("unlikes", 0, 0, 1),
("dislikes", 1, 1, 0),
("undislikes", 1, 0, 1),
)
parts: list[pd.DataFrame] = []
for family, channel, active, tie_priority in specifications:
for prefix in prefixes:
frame = pd.read_parquet(
data / f"{prefix}_{family}.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
mapped = item_index.get_indexer(
frame.item_id.to_numpy(dtype=np.int64, copy=False)
)
keep = mapped >= 0
if not np.any(keep):
continue
retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
retained["item"] = mapped[keep].astype(np.int32, copy=False)
retained["channel"] = np.int8(channel)
retained["active"] = np.int8(active)
retained["tie_priority"] = np.int8(tie_priority)
retained["organic_like"] = np.int8(
family == "likes"
) * retained.is_organic.astype(np.int8)
parts.append(retained.drop(columns="is_organic"))
if not parts:
return {}
events = pd.concat(parts, ignore_index=True)
events = events.sort_values(
["uid", "timestamp", "tie_priority"], kind="mergesort"
).reset_index(drop=True)
result: dict[int, ExplicitEvents] = {}
for uid, rows in events.groupby("uid", sort=False):
result[int(uid)] = ExplicitEvents(
timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
items=rows.item.to_numpy(dtype=np.int32, copy=True),
channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
active=rows.active.to_numpy(dtype=np.int8, copy=True),
organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
)
log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
return result
def empty_explicit_events() -> ExplicitEvents:
return ExplicitEvents(
timestamps=np.empty(0, dtype=np.int64),
items=np.empty(0, dtype=np.int32),
channel=np.empty(0, dtype=np.int8),
active=np.empty(0, dtype=np.int8),
organic_like=np.empty(0, dtype=np.int8),
)
def pack_active_items(
active: dict[int, int], width: int, padding_item: int
) -> np.ndarray:
packed = np.full(width, padding_item, dtype=np.uint16)
if not active:
return packed
ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
packed[: len(ordered)] = np.fromiter(
(item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
)
return packed
def load_inference_state(
path: Path,
item_index: pd.Index,
uids: np.ndarray,
n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Pack the task-provided active explicit state."""
state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
state = state.loc[keep, ["uid", "state"]].copy()
state["item"] = mapped[keep].astype(np.int32, copy=False)
user_rows = pd.Index(uids)
liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
like_counts = np.zeros(len(uids), dtype=np.int32)
dislike_counts = np.zeros(len(uids), dtype=np.int32)
for value, width, destination, counts in (
("liked", LIKE_ITEMS, liked, like_counts),
("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
):
selected = state[state.state == value]
for uid, rows in selected.groupby("uid", sort=False):
row = int(user_rows.get_indexer([int(uid)])[0])
if row < 0:
continue
items = np.sort(rows.item.unique())
counts[row] = len(items)
retained = items[:width]
destination[row, : len(retained)] = retained.astype(np.uint16)
return liked, disliked, like_counts, dislike_counts
class SequentialTwoTower(nn.Module):
"""Order-sensitive user tower and independent catalog-aware item tower."""
def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
super().__init__()
n_items, n_item_features = item_features.shape
dim = config.embedding_dim
self.n_items = n_items
self.context_embedding = nn.Embedding(
n_items + 1, dim, padding_idx=n_items
)
self.event_projection = nn.Sequential(
nn.Linear(dim + 3, config.hidden_dim),
nn.GELU(),
)
self.sequence_encoder = nn.GRU(
input_size=config.hidden_dim,
## Step 15 · agent · 2026-09-29T02:05:45.775Z
### read_file · 2026-09-29T02:05:45.775Z
{
"start_line": 301,
"end_line": 434,
"file_path": "/app/solution/two_tower_model.py"
}
### Tool output · 2026-09-29T02:05:45.778Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 301-434 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 435.
--- FILE CONTENT (truncated) ---
hidden_size=dim,
num_layers=1,
batch_first=True,
)
self.user_tower = nn.Sequential(
nn.Linear(dim * 3 + 6, config.user_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_1, config.user_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_2, dim),
)
self.candidate_embedding = nn.Embedding(n_items, dim)
self.register_buffer(
"item_features", torch.from_numpy(item_features), persistent=True
)
self.item_tower = nn.Sequential(
nn.Linear(dim + n_item_features, config.item_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_1, config.item_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_2, dim),
)
nn.init.normal_(self.context_embedding.weight, std=0.02)
nn.init.normal_(self.candidate_embedding.weight, std=0.02)
with torch.no_grad():
self.context_embedding.weight[n_items].zero_()
def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
mask = items.ne(self.n_items)
vectors = self.context_embedding(items)
return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
dim=1, keepdim=True
).clamp_min(1)
def encode_items(self, items: torch.Tensor) -> torch.Tensor:
inputs = torch.cat(
[self.candidate_embedding(items), self.item_features[items]], dim=1
)
return self.item_tower(inputs)
def invalid_negative_mask(
negatives: torch.Tensor,
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
) -> torch.Tensor:
"""Identify targets, known positives, active likes, and row duplicates."""
invalid = negatives.eq(targets[:, None])
invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= negatives[:, column, None].eq(
negatives[:, :column]
).any(dim=1)
return invalid
def draw_filtered_mixed_negatives(
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
n_items: int,
uniform_count: int,
inbatch_count: int,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw uniform and unique-target in-batch negatives."""
batch_size = len(targets)
uniform = torch.randint(
n_items,
(batch_size, uniform_count),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
-1.0,
)
columns = torch.topk(priorities, k=corrected_count, dim=1).indices
inbatch = unique_targets[columns]
if corrected_count < inbatch_count:
filler = torch.randint(
n_items,
(batch_size, inbatch_count - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
:, uniform_count : uniform_count + corrected_count
] = item_correction[inbatch[:, :corrected_count]]
negatives = negatives_cpu.to(device)
correction = correction_cpu.to(device)
for _ in range(32):
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if not bool(invalid.any()):
return negatives, correction
replacements = torch.randint(
n_items,
(int(invalid.sum().detach().cpu()),),
generator=generator,
device="cpu",
).to(device)
negatives[invalid] = replacements
correction[invalid] = 1.0
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if bool(invalid.any()):
raise RuntimeError("mixed negative resampling failed false-negative filter")
return negatives, correction
## Step 16 · agent · 2026-09-29T02:05:47.852Z
### grep_search · 2026-09-29T02:05:47.852Z
{
"dir_path": "/app/solution",
"pattern": "^(def |class )"
}
### Tool output · 2026-09-29T02:05:47.869Z
Found 58 matches for pattern "^(def |class )" in path "/app/solution":
---
File: train_current_model.py
L41: class Config:
L76: class CandidateSet:
L99: class SequenceData:
L127: class TargetPartition:
L140: def log(message: str) -> None:
L144: def seed_everything(seed: int) -> None:
L150: def model_config(config: Config) -> base.TowerConfig:
L174: def append_state_snapshot(
L190: def causal_activity_features(
L247: def build_sequence_parts(
L314: def final_user_features(
L341: def prepare_data(
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L481: def save_cache(
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L511: def load_or_prepare(
L534: def build_item_correction(
L578: def build_known_positive_filter(
L609: def invalid_full_history_negatives(
L630: def draw_full_history_filtered_negatives(
L724: def draw_training_negatives(
L763: def event_sequence(
L774: def pool_explicit_states(
L798: def user_queries(
L813: def candidate_vectors(
L828: def history_windows(
L845: def corrected_negative_loss(
L867: def retained_target_window_starts(
L888: def chronological_target_partition(
L914: def load_organic_like_outcomes(
L955: def build_target_weights(
L1035: def build_dense_block(
L1111: def score_target_microbatches(
L1198: def release_accelerator_cache(device: torch.device) -> None:
L1203: def run_training_epoch(
L1364: def evaluate_validation_bce(
L1488: def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
L1495: def save_epoch_checkpoint(
L1521: def train_model(
L1782: def inference_queries(
L1861: def rank_candidates(
L1912: def write_ranking(frame: pd.DataFrame, output: Path) -> None:
L1922: def main() -> int:
---
File: two_tower_model.py
L27: class TowerConfig:
L49: class ListenEvents:
L57: class ExplicitEvents:
L65: def log(message: str) -> None:
L69: def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
L90: def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
L106: def iter_listen_users(
L157: def merge_listen_streams(
L175: def load_explicit_events(
L228: def empty_explicit_events() -> ExplicitEvents:
L238: def pack_active_items(
L251: def load_inference_state(
L284: class SequentialTwoTower(nn.Module):
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---
## Step 17 · agent · 2026-09-29T02:05:52.165Z
### Recorded reasoning summary
Exploring Model Training Logic
I'm currently examining the functions within `train_current_model.py`, specifically focusing on `build_item_correction`, `build_known_positive_filter`, `invalid_full_history_negatives`, `draw_full_history_filtered_negatives`, and `draw_training_negatives`. My aim is to understand their roles in the model's training pipeline.
### Recorded reasoning summary
Testing Baseline Execution
I'm initiating a run of the baseline model to confirm its operational integrity, benchmark its execution duration, and establish a baseline NDCG@10 score. This initial test will inform subsequent investigations into negative sampling strategies.
### run_shell_command · 2026-09-29T02:05:52.165Z
{
"description": "Run baseline model training on validation stage",
"command": "python3 /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T02:11:55.975Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.0s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=197.4s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.3 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/production_validation.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 359.426,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "organic-strong"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5398380485827885,
"positive_logit": 0.7427799622103317,
"negative_logit": -0.622224706198751,
"pair_accuracy": 0.754773301847281,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 210.331716518,
"validation_loss": 0.4966362168774782,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.366156885999999
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697498202323914,
"item_norm_mean": 1.7570279836654663,
"item_norm_std": 0.8279486298561096
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1639
## Step 18 · agent · 2026-09-29T02:12:16.408Z
### run_shell_command · 2026-09-29T02:12:16.408Z
{
"description": "Evaluate baseline validation ranking",
"command": "python3 /task/evaluate_public.py /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T02:12:19.782Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1671
## Step 19 · agent · 2026-09-29T02:12:25.371Z
### Recorded reasoning summary
Analyzing Performance Data
My current focus is on understanding the initial performance benchmark. I've noted the baseline quality score and the number of users evaluated. My next step is to integrate the time budget constraints into my planning to ensure efficient validation and a proper final refit.
### Recorded reasoning summary
Investigating Current Score
I'm prioritizing understanding why the current score is 0.02128 by re-reading the problem description carefully. My immediate goal is to determine if a baseline test submission can be generated to ensure a valid submission is always on hand. I will also investigate the duration of the test stage.
### Recorded reasoning summary
Refining Negative Sampling
I'm now digging into how negative examples are selected and utilized within the existing training objective. My aim is to pinpoint limitations in the current sampling strategy and enhance it while remaining within the CPU budget. I'll validate any changes using the public backtest.
### read_file · 2026-09-29T02:12:25.371Z
{
"start_line": 530,
"end_line": 600,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:12:25.376Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 530-600 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 601.
--- FILE CONTENT (truncated) ---
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""Estimate in-batch propensities from the selected training targets."""
counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
for user_row, count in enumerate(target_counts):
if not count:
continue
offset = int(prepared.offsets[user_row])
target_start = int(target_starts[user_row])
counts += np.bincount(
prepared.sequence_items[
offset
+ target_start
+ 1 : offset
+ target_start
+ int(count)
+ 1
].astype(np.int64),
minlength=len(prepared.item_ids),
)
total = float(counts.sum())
probabilities = (counts + 1.0) / (total + len(counts))
raw = np.power(
(1.0 / len(counts)) / probabilities,
config.inbatch_correction_power,
)
normalization = float(np.sum(counts * raw) / max(total, 1.0))
correction = np.clip(
raw / max(normalization, 1.0e-12),
config.inbatch_correction_min,
config.inbatch_correction_max,
).astype(np.float32)
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
log(
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
)
return torch.from_numpy(correction)
def build_known_positive_filter(
prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
"""Pack each user's complete public-training positive-item set."""
width = (len(prepared.item_ids) + 7) // 8
packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
retained = 0
for user_row, target_count in enumerate(training_counts):
if not target_count:
continue
offset = int(prepared.offsets[user_row])
# T adjacent targets consume sequence positions 0 through T. The
# chronological BCE suffix starts at T + 1 and is intentionally absent.
items = np.unique(
prepared.sequence_items[
offset : offset + int(target_count) + 1
].astype(np.int64)
)
byte_columns = items >> 3
bit_values = np.left_shift(
np.uint8(1), (items & 7).astype(np.uint8)
)
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
## Step 20 · agent · 2026-09-29T02:12:29.441Z
### Recorded reasoning summary
Analyzing Code Block
I'm currently examining line 590, specifically the `build_known_positive_filter` function. I'm focused on its role in packing a user's complete public-training positive-item set and how it utilizes `prepared` and `training_counts`.
### read_file · 2026-09-29T02:12:29.441Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 750,
"start_line": 601
}
### Tool output · 2026-09-29T02:12:29.448Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 601-750 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 751.
--- FILE CONTENT (truncated) ---
retained += len(items)
log(
f"full-history false-negative filter: users={len(prepared.user_ids):,} "
f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
)
return packed
def invalid_full_history_negatives(
negatives: np.ndarray,
targets: np.ndarray,
user_rows: np.ndarray,
known_positive_packed: np.ndarray,
liked_items: np.ndarray,
) -> np.ndarray:
"""Return row-level invalid negatives using the complete training prefix."""
byte_columns = negatives >> 3
bit_columns = negatives & 7
known = known_positive_packed[user_rows[:, None], byte_columns]
invalid = ((known >> bit_columns) & 1).astype(bool)
invalid |= negatives == targets[:, None]
invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= (
negatives[:, column, None] == negatives[:, :column]
).any(axis=1)
return invalid
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
def draw_training_negatives(
positive_items: torch.Tensor,
known_history: torch.Tensor,
liked_items: torch.Tensor,
user_rows: np.ndarray,
known_positive_packed: np.ndarray | None,
prepared: SequenceData,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
if config.negative_filter_scope == "full-training-history":
if known_positive_packed is None:
raise RuntimeError("full-history negative filter was not built")
return draw_full_history_filtered_negatives(
positive_items,
user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
config,
item_correction,
generator,
device,
)
return base.draw_filtered_mixed_negatives(
## Step 21 · agent · 2026-09-29T02:12:34.621Z
### Recorded reasoning summary
Analyzing Code Snippets
I'm currently scrutinizing lines 587-596. My focus is on understanding the `build_known_positive_filter` function and its role in packing each user's public-training positive-item set. This involves a close examination of the `prepared` sequence data and `training_counts` array.
### read_file · 2026-09-29T02:12:34.621Z
{
"start_line": 820,
"file_path": "/app/solution/train_current_model.py",
"end_line": 950
}
### Tool output · 2026-09-29T02:12:34.627Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 820-950 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 951.
--- FILE CONTENT (truncated) ---
encoded = model.encode_items(unique_items)
positive_vectors = encoded[inverse[: len(positives)]]
negative_vectors = encoded[inverse[len(positives) :]].reshape(
len(positives), negatives.shape[1], -1
)
return positive_vectors, negative_vectors
def history_windows(
sequence: np.ndarray,
start: int,
count: int,
width: int,
padding_item: int,
) -> np.ndarray:
base_start = max(0, start - width + 1)
segment = sequence[base_start : start + count]
padded = np.concatenate(
[np.full(width - 1, padding_item, dtype=np.uint16), segment]
)
windows = np.lib.stride_tricks.sliding_window_view(padded, width)
offset = start - base_start
return windows[offset : offset + count].copy()
def corrected_negative_loss(
logits: torch.Tensor,
correction: torch.Tensor,
config: Config,
) -> torch.Tensor:
raw = F.softplus(logits)
total = config.uniform_negatives + config.inbatch_negatives
result = torch.zeros(len(logits), device=logits.device)
if config.uniform_negatives:
result += config.uniform_negatives * raw[
:, : config.uniform_negatives
].mean(dim=1)
if config.inbatch_negatives:
selected = slice(config.uniform_negatives, total)
weights = correction[:, selected]
result += config.inbatch_negatives * (
(raw[:, selected] * weights).sum(dim=1)
/ weights.sum(dim=1).clamp_min(1.0e-6)
)
return result / total
def retained_target_window_starts(
total_targets: np.ndarray,
validation_counts: np.ndarray,
training_counts: np.ndarray,
) -> np.ndarray:
"""Return the discarded prefix before the retained chronological suffix."""
training_region_ends = total_targets - validation_counts
window_starts = training_region_ends - training_counts
if (
np.any(window_starts < 0)
or np.any(training_counts < 0)
or np.any(validation_counts < 0)
):
raise RuntimeError("chronological target partition produced negative counts")
if not np.array_equal(
window_starts + training_counts + validation_counts, total_targets
):
raise RuntimeError("chronological target window is not a final contiguous suffix")
return window_starts
def chronological_target_partition(
prepared: SequenceData,
validation_fraction: float,
max_train_targets_per_user: int,
max_validation_targets_per_user: int,
) -> TargetPartition:
"""Select a recent contiguous window with a final out-of-time BCE suffix."""
totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
validation = np.floor(totals * validation_fraction).astype(np.int64)
validation[(totals >= 2) & (validation == 0)] = 1
if max_validation_targets_per_user:
validation = np.minimum(validation, max_validation_targets_per_user)
history_training = totals - validation
training = history_training.copy()
if max_train_targets_per_user:
training = np.minimum(training, max_train_targets_per_user)
window_starts = retained_target_window_starts(totals, validation, training)
return TargetPartition(
window_starts=window_starts,
training_counts=training,
validation_counts=validation,
history_training_counts=history_training,
)
def load_organic_like_outcomes(
public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
"""Load mapped organic-like timestamps from public fitting history."""
prefixes = ("train",) if stage == "validation" else ("train", "val")
frames = [
pd.read_parquet(
public / "data" / f"{prefix}_likes.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
for prefix in prefixes
]
likes = pd.concat(frames, ignore_index=True)
likes = likes[likes.is_organic == 1].copy()
mapped = pd.Index(item_ids).get_indexer(
likes.item_id.to_numpy(dtype=np.int64)
)
likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
nested: dict[int, dict[int, list[int]]] = {}
for raw_uid, raw_timestamp, raw_item in likes.itertuples(
index=False, name=None
):
nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
int(raw_timestamp)
)
result = {
uid: {
item: np.asarray(timestamps, dtype=np.int64)
for item, timestamps in items.items()
}
for uid, items in nested.items()
}
log(
f"loaded organic-like outcomes: rows={len(likes):,} "
f"users={len(result):,}"
## Step 22 · agent · 2026-09-29T02:12:36.361Z
### read_file · 2026-09-29T02:12:36.361Z
{
"end_line": 1150,
"file_path": "/app/solution/train_current_model.py",
"start_line": 951
}
### Tool output · 2026-09-29T02:12:36.367Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 951-1150 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1151.
--- FILE CONTENT (truncated) ---
)
return result
def build_target_weights(
public: Path,
stage: str,
prepared: SequenceData,
training_counts: np.ndarray,
config: Config,
) -> np.ndarray:
"""Build nonzero per-token weights without dropping adjacent targets."""
if config.target_weighting == "uniform":
weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
else:
weights = np.full(
len(prepared.sequence_items),
config.ordinary_target_weight,
dtype=np.float32,
)
organic_strong = (
(prepared.event_features[:, 0] > 0.5)
& (prepared.event_features[:, 1] >= 0.8)
)
weights[organic_strong] = config.organic_strong_target_weight
if config.target_weighting == "discovery-graded":
like_outcomes = load_organic_like_outcomes(
public, stage, prepared.item_ids
)
history_end = (
base.TRAIN_END if stage == "validation" else base.VALIDATION_END
)
horizon = config.discovery_horizon_days * base.DAY
grade_three = 0
for user_row, uid in enumerate(prepared.user_ids):
start = int(prepared.offsets[user_row])
end = int(prepared.offsets[user_row + 1])
if end - start < 2:
continue
last_training_target = start + int(training_counts[user_row])
first_validation_target = last_training_target + 1
split_timestamp = (
int(prepared.sequence_timestamps[first_validation_target])
if first_validation_target < end
else history_end
)
user_likes = like_outcomes.get(int(uid), {})
seen = {int(prepared.sequence_items[start])}
for position in range(start + 1, end):
item = int(prepared.sequence_items[position])
novel = item not in seen
seen.add(item)
if not novel:
# Repeats remain training examples, but they cannot be
# eligible discovery outcomes at the corresponding
# cutoff and therefore keep only the ordinary weight.
weights[position] = config.ordinary_target_weight
continue
timestamps = user_likes.get(item)
if timestamps is None:
continue
timestamp = int(prepared.sequence_timestamps[position])
upper = min(
timestamp + horizon,
split_timestamp
if position <= last_training_target
else history_end,
)
outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
weights[position] = config.organic_like_target_weight
grade_three += 1
log(
f"grade-3 first-encounter targets={grade_three:,} "
f"horizon_days={config.discovery_horizon_days}"
)
log(
f"target weighting={config.target_weighting} "
f"min={weights.min():.3f} mean={weights.mean():.3f} "
f"max={weights.max():.3f}"
)
return weights
def build_dense_block(
prepared: SequenceData,
target_weights: np.ndarray,
users: np.ndarray,
window_starts: np.ndarray,
process_counts: np.ndarray,
score_starts: np.ndarray,
start: int,
steps: int,
config: Config,
) -> tuple[np.ndarray, ...]:
"""Build one causal sequence block and mark the target rows to score."""
batch_size = len(users)
padding_item = len(prepared.item_ids)
input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
targets = np.zeros((batch_size, steps), dtype=np.uint16)
weights = np.ones((batch_size, steps), dtype=np.float32)
scored = np.zeros((batch_size, steps), dtype=bool)
histories = np.full(
(batch_size, steps, config.bptt_steps),
padding_item,
dtype=np.uint16,
)
processed = np.zeros(batch_size, dtype=np.int32)
lengths = prepared.lengths
for row, user_row in enumerate(users):
count = min(steps, max(int(process_counts[row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[user_row])
window_start = int(window_starts[row])
absolute_start = window_start + start
positions = slice(
offset + absolute_start,
offset + absolute_start + count,
)
target_positions = slice(
offset + absolute_start + 1,
offset + absolute_start + count + 1,
)
input_items[row, :count] = prepared.sequence_items[positions]
event_features[row, :count] = prepared.event_features[positions]
activity_features[row, :count] = prepared.activity_features[positions]
state_ids[row, :count] = prepared.state_ids[positions]
targets[row, :count] = prepared.sequence_items[target_positions]
weights[row, :count] = target_weights[target_positions]
score_from = min(count, max(int(score_starts[row]) - start, 0))
scored[row, score_from:count] = True
processed[row] = count
user_sequence = prepared.sequence_items[
offset : offset + int(lengths[user_row])
]
histories[row, :count] = history_windows(
user_sequence,
absolute_start,
count,
config.bptt_steps,
padding_item,
)
return (
input_items,
event_features,
activity_features,
state_ids,
targets,
scored,
histories,
processed,
weights,
)
def score_target_microbatches(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
sequence_vectors: torch.Tensor,
flat_states: np.ndarray,
flat_activity: np.ndarray,
positive_items: torch.Tensor,
negatives: torch.Tensor,
negative_weights: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
"""Compute one logical block's exact BCE in bounded-memory slices."""
target_count = len(positive_items)
total_weight = float(sample_weights.sum())
if total_weight <= 0:
raise RuntimeError("target block has no positive sample weight")
sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
statistics = {
"loss_sum": 0.0,
"positive_sum": 0.0,
"negative_sum": 0.0,
"pair_correct": 0.0,
"weight_sum": 0.0,
}
for begin in range(0, target_count, config.loss_microbatch_size):
end = min(begin + config.loss_microbatch_size, target_count)
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
## Step 23 · agent · 2026-09-29T02:12:40.454Z
### read_file · 2026-09-29T02:12:40.454Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1151,
"end_line": 1350
}
### Tool output · 2026-09-29T02:12:40.462Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1151-1350 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1351.
--- FILE CONTENT (truncated) ---
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
def run_training_epoch(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
generator: torch.Generator,
device: torch.device,
epoch: int,
) -> dict[str, float]:
model.train()
train_users = np.flatnonzero(target_counts > 0)
sorted_users = train_users[
np.argsort(target_counts[train_users], kind="stable")
]
groups = [
sorted_users[start : start + config.user_batch_size]
for start in range(0, len(sorted_users), config.user_batch_size)
]
group_order = torch.randperm(len(groups), generator=generator).numpy()
negative_count = config.uniform_negatives + config.inbatch_negatives
total_loss = 0.0
total_targets = 0
total_weight = 0.0
total_positive = 0.0
total_negative = 0.0
total_pair_correct = 0.0
batches = 0
started = time.monotonic()
for group_number in group_order:
users = groups[int(group_number)]
group_counts = target_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_counts.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_counts,
np.zeros(len(users), dtype=np.int64),
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0).detach()
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
## Step 24 · agent · 2026-09-29T02:12:42.254Z
### read_file · 2026-09-29T02:12:42.254Z
{
"end_line": 80,
"file_path": "/app/solution/train_current_model.py",
"start_line": 40
}
### Tool output · 2026-09-29T02:12:42.261Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 40-80 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 81.
--- FILE CONTENT (truncated) ---
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
item_ids: np.ndarray
packed: np.ndarray
## Step 25 · agent · 2026-09-29T02:12:44.286Z
### read_file · 2026-09-29T02:12:44.286Z
{
"end_line": 2050,
"start_line": 1920,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:12:44.291Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1920-2050 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2051.
--- FILE CONTENT (truncated) ---
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
## Step 26 · agent · 2026-09-29T02:12:46.098Z
### read_file · 2026-09-29T02:12:46.098Z
{
"end_line": 2165,
"file_path": "/app/solution/train_current_model.py",
"start_line": 2051
}
### Tool output · 2026-09-29T02:12:46.103Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2051-2165 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2166.
--- FILE CONTENT (truncated) ---
if device.type == "mps" and not torch.backends.mps.is_available():
parser.error("MPS was requested but is unavailable")
started = time.monotonic()
prepared, eligibility = load_or_prepare(
public,
args.stage,
config,
args.prepared_cache.resolve() if args.prepared_cache else None,
)
target_partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
if args.prepare_only:
print(
json.dumps(
{
"stage": args.stage,
"sequence_users": len(prepared.user_ids),
"sequence_tokens": len(prepared.sequence_items),
"next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(
target_partition.window_starts.sum()
),
"target_users": len(eligibility.uids),
"runtime_seconds": round(time.monotonic() - started, 3),
"config": asdict(config),
},
indent=2,
)
)
return 0
model, training_history = train_model(
public,
args.stage,
prepared,
config,
device,
args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
)
ranking, embedding_diagnostics = rank_candidates(
model,
prepared,
eligibility,
config,
device,
args.ranking_batch_size,
)
write_ranking(ranking, args.output.resolve())
print(
json.dumps(
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": args.stage,
"output": str(args.output.resolve()),
"rows": len(ranking),
"users": int(ranking.uid.nunique()),
"runtime_seconds": round(time.monotonic() - started, 3),
"public_inputs_only": True,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": (
"every-adjacent-target-within-recent-chronological-window"
),
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": False,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": (
"24-uniform-plus-8-propensity-corrected-inbatch"
),
"false_negative_filter": config.negative_filter_scope,
"target_weighting": config.target_weighting,
},
"config": asdict(config),
"sequence_tokens": len(prepared.sequence_items),
"all_next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(target_partition.window_starts.sum()),
"training_history": training_history,
"embedding_diagnostics": embedding_diagnostics,
"torch_version": torch.__version__,
"device": str(device),
},
indent=2,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
## Step 27 · agent · 2026-09-29T02:12:48.167Z
### read_file · 2026-09-29T02:12:48.167Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 535,
"start_line": 470
}
### Tool output · 2026-09-29T02:12:48.173Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 470-535 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 536.
--- FILE CONTENT (truncated) ---
def cache_metadata(stage: str, config: Config) -> dict[str, object]:
return {
"cache_version": CACHE_VERSION,
"stage": stage,
"positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
"profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
"event_time_feature": "exp-negative-gap-over-60-days",
}
def save_cache(
path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
np.savez(
path,
metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
**prepared.__dict__,
)
log(f"saved public-derived full-sequence cache: {path}")
def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
with np.load(path, allow_pickle=False) as artifact:
actual = json.loads(str(artifact["metadata"].item()))
expected = cache_metadata(stage, config)
if actual != expected:
raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
values = {
field: artifact[field].copy()
for field in SequenceData.__dataclass_fields__
}
prepared = SequenceData(**values)
log(
f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
f"targets={prepared.target_count:,}"
)
return prepared
def load_or_prepare(
public: Path,
stage: str,
config: Config,
cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
candidate_name = (
"validation_eligible_candidates.npz"
if stage == "validation"
else "test_eligible_candidates.npz"
)
eligibility = CandidateSet.read(public / "data" / candidate_name)
if cache is not None and cache.is_file():
prepared = load_cache(cache, stage, config)
if not np.array_equal(prepared.item_ids, eligibility.item_ids):
raise RuntimeError("cached catalog differs from candidate artifact")
return prepared, eligibility
prepared, built_eligibility = prepare_data(public, stage, config)
if cache is not None:
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
## Step 28 · agent · 2026-09-29T02:12:51.556Z
### Recorded reasoning summary
Optimizing Data Preparation
I've observed that the data preparation step, specifically `prepare_data`, is consuming around 45 seconds during the validation phase. My current thinking is to leverage a `--prepared-cache` to store this prepared data, thereby significantly reducing subsequent run times. I'm also considering the implications of this caching for the submission stage where both training and validation data are combined.
### read_file · 2026-09-29T02:12:51.556Z
{
"end_line": 470,
"start_line": 340,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:12:51.562Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 340-470 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 471.
--- FILE CONTENT (truncated) ---
def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
if n_items >= np.iinfo(np.uint16).max:
raise RuntimeError("uint16 item storage is too small for this catalog")
if stage == "validation":
prefixes = ("train",)
history_end = base.TRAIN_END
candidate_name = "validation_eligible_candidates.npz"
state_name = "validation_preference_state.parquet"
else:
prefixes = ("train", "val")
history_end = base.VALIDATION_END
candidate_name = "test_eligible_candidates.npz"
state_name = "test_preference_state.parquet"
eligibility = CandidateSet.read(data / candidate_name)
if not np.array_equal(eligibility.item_ids, item_ids):
raise RuntimeError("catalog and candidate artifact item order differ")
explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
inference_likes, inference_dislikes, like_counts, dislike_counts = (
base.load_inference_state(
data / state_name,
item_index,
eligibility.uids,
n_items,
)
)
inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
# Snapshot zero is the shared empty explicit state.
liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
disliked_states = [
np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
]
state_counts: list[tuple[int, int]] = [(0, 0)]
user_ids: list[int] = []
offsets = [0]
timestamp_parts: list[np.ndarray] = []
item_parts: list[np.ndarray] = []
event_parts: list[np.ndarray] = []
activity_parts: list[np.ndarray] = []
state_parts: list[np.ndarray] = []
listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
started = time.monotonic()
for seen_users, (uid, listens) in enumerate(
base.merge_listen_streams(listen_paths, item_index), start=1
):
parts = build_sequence_parts(
listens,
explicit_by_user.get(uid, base.empty_explicit_events()),
n_items,
liked_states,
disliked_states,
state_counts,
)
if parts is None:
continue
sequence_row = len(user_ids)
user_ids.append(uid)
timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
item_parts.append(parts[1])
event_parts.append(parts[2])
activity_parts.append(parts[3])
state_parts.append(parts[4])
offsets.append(offsets[-1] + len(parts[1]))
inference_row = inference_rows.get(uid)
if inference_row is not None:
inference_sequence_rows[inference_row] = sequence_row
inference_user_features[inference_row] = final_user_features(
parts[0],
parts[1],
parts[2],
history_end,
int(like_counts[inference_row]),
int(dislike_counts[inference_row]),
)
if seen_users % 1_000 == 0:
log(
f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
f"states={len(liked_states):,} "
f"elapsed={time.monotonic() - started:.1f}s"
)
missing = np.flatnonzero(inference_sequence_rows < 0)
if len(missing):
inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
inference_user_features[missing, 5] = (
np.log1p(dislike_counts[missing]) / 4.0
)
log(f"target users without qualifying catalog sequence={len(missing):,}")
prepared = SequenceData(
item_ids=item_ids,
item_features=base.build_item_features(catalog),
user_ids=np.asarray(user_ids, dtype=np.int64),
offsets=np.asarray(offsets, dtype=np.int64),
sequence_timestamps=np.concatenate(timestamp_parts),
sequence_items=np.concatenate(item_parts),
event_features=np.concatenate(event_parts),
activity_features=np.concatenate(activity_parts),
state_ids=np.concatenate(state_parts),
liked_states=np.stack(liked_states),
disliked_states=np.stack(disliked_states),
state_counts=np.asarray(state_counts, dtype=np.int32),
inference_user_rows=inference_sequence_rows,
inference_liked_items=inference_likes,
inference_disliked_items=inference_dislikes,
inference_user_features=inference_user_features,
)
log(
f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
f"tokens={len(prepared.sequence_items):,} "
f"next_item_targets={prepared.target_count:,} "
f"states={len(prepared.liked_states):,}"
)
return prepared, eligibility
## Step 29 · agent · 2026-09-29T02:12:53.874Z
### Recorded reasoning summary
Preparing Data Efficiently
I'm focusing on optimizing data preparation by leveraging a deterministic cache. This should significantly speed up validation experiments by avoiding repeated parsing of parquet files, allowing for near-instantaneous loading from the cache.
### run_shell_command · 2026-09-29T02:12:53.874Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --prepare-only",
"description": "Generate validation sequence data cache"
}
### Tool output · 2026-09-29T02:12:57.820Z
Output: usage: train_current_model.py [-h] [--stage {validation,submission}] --output
OUTPUT --public-dir PUBLIC_DIR
[--prepared-cache PREPARED_CACHE]
[--device {cpu,mps}] [--threads THREADS]
[--bptt-steps BPTT_STEPS]
[--user-batch-size USER_BATCH_SIZE]
[--epochs EPOCHS]
[--learning-rate LEARNING_RATE]
[--loss-microbatch-size LOSS_MICROBATCH_SIZE]
[--negative-filter-scope {full-training-history,last-100}]
[--target-weighting {discovery-graded,organic-strong,uniform}]
[--ordinary-target-weight ORDINARY_TARGET_WEIGHT]
[--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]
[--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]
[--discovery-horizon-days DISCOVERY_HORIZON_DAYS]
[--validation-fraction VALIDATION_FRACTION]
[--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]
[--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]
[--early-stopping-patience EARLY_STOPPING_PATIENCE]
[--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]
[--checkpoint-dir CHECKPOINT_DIR]
[--resume-checkpoint RESUME_CHECKPOINT]
[--seed SEED]
[--ranking-batch-size RANKING_BATCH_SIZE]
[--prepare-only]
train_current_model.py: error: the following arguments are required: --output
Exit Code: 2
Process Group PGID: 1691
## Step 30 · agent · 2026-09-29T02:12:59.921Z
### run_shell_command · 2026-09-29T02:12:59.921Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/dummy.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --prepare-only",
"description": "Build validation cache"
}
### Tool output · 2026-09-29T02:13:54.853Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.0s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
{
"stage": "validation",
"sequence_users": 9135,
"sequence_tokens": 20056329,
"next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"target_users": 8456,
"runtime_seconds": 50.886,
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
}
}
Process Group PGID: 1702
## Step 31 · agent · 2026-09-29T02:13:59.362Z
### Recorded reasoning summary
Investigating Optimization Strategy
My focus has shifted to optimizing our approach. With efficient data loading now secured via the validation cache, I'm eager to delve into the intricacies of negative sampling and the loss function. This next phase is critical for refining our strategic intent.
### update_topic · 2026-09-29T02:13:59.362Z
{
"strategic_intent": "Analyze the negative sampling strategy, loss function, and objective alignment to diagnose the retrieval quality bottleneck.",
"summary": "We verified the baseline score (NDCG@10 = 0.02128) and created a prepared sequence cache for fast iteration. Now we will analyze the current training objective, negative sampling strategy, and evaluation metric alignment.",
"title": "Diagnosing Negative Sampling and Training Objective"
}
### Tool output · 2026-09-29T02:14:01.676Z
Current topic: "Diagnosing Negative Sampling and Training Objective"
Topic summary: We verified the baseline score (NDCG@10 = 0.02128) and created a prepared sequence cache for fast iteration. Now we will analyze the current training objective, negative sampling strategy, and evaluation metric alignment.
Strategic Intent: Analyze the negative sampling strategy, loss function, and objective alignment to diagnose the retrieval quality bottleneck.
## Step 32 · agent · 2026-09-29T02:14:07.305Z
### Recorded reasoning summary
Investigating Sampling Strategies
My focus is now on dissecting how this mature baseline trainer, with its inherent internal validation, selects and utilizes negative examples. I aim to pinpoint any limitations within its current negative-sampling methodology to improve its effectiveness.
### run_shell_command · 2026-09-29T02:14:07.305Z
{
"command": "head -n 80 /task/ranking_evaluation.py",
"description": "Inspect ranking_evaluation.py"
}
### Tool output · 2026-09-29T02:14:07.320Z
Output: """Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
Process Group PGID: 1726
## Step 33 · agent · 2026-09-29T02:14:09.488Z
### run_shell_command · 2026-09-29T02:14:09.488Z
{
"description": "Inspect evaluate_public.py scoring details",
"command": "tail -n 80 /task/evaluate_public.py"
}
### Tool output · 2026-09-29T02:14:09.503Z
Output: ][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py "
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1729
## Step 34 · agent · 2026-09-29T02:14:11.980Z
### read_file · 2026-09-29T02:14:11.980Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1920,
"start_line": 1780
}
### Tool output · 2026-09-29T02:14:11.989Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1780-1920 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1921.
--- FILE CONTENT (truncated) ---
@torch.inference_mode()
def inference_queries(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
device: torch.device,
) -> torch.Tensor:
sequence_rows = prepared.inference_user_rows
lengths = np.where(
sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
)
ordered = np.argsort(lengths, kind="stable")
result = torch.zeros(
len(sequence_rows), config.embedding_dim, dtype=torch.float32
)
for group_start in range(0, len(ordered), config.user_batch_size):
output_rows = ordered[group_start : group_start + config.user_batch_size]
rows = sequence_rows[output_rows]
group_lengths = lengths[output_rows]
hidden = torch.zeros(
1, len(output_rows), config.embedding_dim, device=device
)
for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
steps = min(
config.bptt_steps,
int(group_lengths.max(initial=0)) - start,
)
items = np.full(
(len(output_rows), steps),
len(prepared.item_ids),
dtype=np.uint16,
)
features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
counts = np.zeros(len(output_rows), dtype=np.int32)
for local_row, sequence_row in enumerate(rows):
if sequence_row < 0:
continue
count = min(steps, max(int(group_lengths[local_row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[sequence_row])
selected = slice(offset + start, offset + start + count)
items[local_row, :count] = prepared.sequence_items[selected]
features[local_row, :count] = prepared.event_features[selected]
counts[local_row] = count
outputs, _ = event_sequence(
model,
torch.from_numpy(items.astype(np.int64)).to(device),
torch.from_numpy(features.astype(np.float32)).to(device),
hidden,
)
next_rows: list[torch.Tensor] = []
for local_row, count in enumerate(counts):
next_rows.append(
outputs[local_row, count - 1]
if count
else hidden[0, local_row]
)
hidden = torch.stack(next_rows).unsqueeze(0)
liked_items = torch.from_numpy(
prepared.inference_liked_items[output_rows].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.inference_disliked_items[output_rows].astype(np.int64)
).to(device)
queries = user_queries(
model,
hidden.squeeze(0),
model._mean_context_channel(liked_items),
model._mean_context_channel(disliked_items),
torch.from_numpy(
prepared.inference_user_features[output_rows].astype(np.float32)
).to(device),
)
result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
return result
@torch.inference_mode()
def rank_candidates(
model: base.SequentialTwoTower,
prepared: SequenceData,
eligibility: CandidateSet,
config: Config,
device: torch.device,
ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
model.eval()
queries = inference_queries(model, prepared, config, device)
item_parts: list[torch.Tensor] = []
for start in range(0, len(prepared.item_ids), 2_048):
rows = torch.arange(
start,
min(start + 2_048, len(prepared.item_ids)),
dtype=torch.long,
device=device,
)
item_parts.append(model.encode_items(rows).cpu())
items = torch.cat(item_parts)
rows: list[tuple[int, int, int]] = []
for start in range(0, len(eligibility.uids), ranking_batch_size):
end = min(start + ranking_batch_size, len(eligibility.uids))
scores = queries[start:end] @ items.T
eligible = np.unpackbits(
eligibility.packed[start:end],
axis=1,
count=len(prepared.item_ids),
bitorder="little",
).astype(bool, copy=False)
scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
for local_row, columns in enumerate(selected):
uid = int(eligibility.uids[start + local_row])
rows.extend(
(uid, int(prepared.item_ids[column]), rank)
for rank, column in enumerate(columns, start=1)
)
if end == len(eligibility.uids) or end % 1_024 == 0:
log(f"ranked users={end:,}/{len(eligibility.uids):,}")
query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
diagnostics = {
"query_norm_mean": float(query_norm.mean()),
"query_norm_std": float(query_norm.std()),
"item_norm_mean": float(item_norm.mean()),
"item_norm_std": float(item_norm.std()),
}
return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
def write_ranking(frame: pd.DataFrame, output: Path) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
if output.suffix == ".parquet":
frame.to_parquet(output, index=False, compression="zstd")
elif output.suffix == ".csv":
frame.to_csv(output, index=False)
else:
raise ValueError("--output must end in .parquet or .csv")
## Step 35 · agent · 2026-09-29T02:14:38.124Z
### Recorded reasoning summary
Calculating Scores
I've been focusing on the inference step, specifically how scores are computed. The process involves a matrix multiplication of queries and items, followed by masking out ineligible items with negative infinity. Finally, I extract the top-k indices from the resulting scores.
### Recorded reasoning summary
Verifying Score Calculations
I've confirmed that raw dot products are indeed utilized for score calculations, aligning with both training and inference processes. My next step involves a deep dive into the training objective, negative sampling, loss function, and the intricacies of false-negative filtering.
**Analyzing Loss Function**
I am currently scrutinizing the `corrected_negative_loss` function. My focus is on understanding how it handles both uniform and in-batch negatives, particularly how the `correction` tensor is applied to adjust the logits.
### Recorded reasoning summary
Investigating Loss Formulation
I'm re-examining the Binary Cross-Entropy loss formulation. It appears the implementation uses `softplus` for both positive and negative terms, and I'm digging into how it handles multiple negative samples, particularly with the `corrected_negative_loss` function.
### Recorded reasoning summary
Revising Loss Calculation
I've discovered an averaging issue within the negative loss calculation. It appears the mean is being taken over negative samples rather than a sum, significantly impacting gradient ratios. This deviates from standard BCE and prompts further investigation into optimal loss functions for retrieval.
### Recorded reasoning summary
Questioning Loss Choice
I'm questioning whether Binary Cross-Entropy is indeed the standard for modern two-tower retrieval models, considering alternatives like InfoNCE. I need to re-evaluate the prompt's constraints on the training objective to confirm flexibility.
### Recorded reasoning summary
Exploring Negative Sampling
I am now focusing on the core of the problem: the training objective and its negative sampling strategy. My goal is to identify limitations within the current approach and devise improvements that fit within the CPU budget. I'll be validating these changes against the public backtest.
### Recorded reasoning summary
Investigating Negative Selection
I'm now scrutinizing how negative examples are selected, specifically within `draw_full_history_filtered_neg`. My aim is to pinpoint any limitations in the current strategy and explore improvements that respect the CPU budget, keeping the upcoming public backtest validation in mind.
### Recorded reasoning summary
Refining Negative Sampling
I'm now examining the specific implementations of negative sampling, focusing on `draw_full_history_filtered_negatives`. My goal is to analyze its current strategies and identify potential optimizations that align with CPU constraints for the upcoming backtest validation.
### Recorded reasoning summary
Revising Negative Sampling
I'm now re-examining the negative sampling logic. I noticed an issue where `unique_targets` are filtered incorrectly. My focus is on ensuring all target IDs are properly accounted for and that the negative sampling strategy aligns with the overall objective.
### Recorded reasoning summary
Revising Filter Logic
I'm now revisiting the `build_known_positive_filter` function to fully grasp its positive item filtering mechanism. My goal is to ensure its accuracy and completeness for subsequent negative sampling considerations.
### Recorded reasoning summary
Revising Target Partitioning
I'm delving into the `chronological_target_partition` function. I've noted comments suggesting the absence of a chronological BCE suffix, which contradicts my understanding of the partitioning logic. I need to reconcile these observations and understand the implications for training data.
### Recorded reasoning summary
Clarifying Chronological Partitioning
I'm analyzing the `chronological_target_partition` function, specifically the interaction between `totals`, `validation`, and `training` counts. I'm also looking into `build_dense_block` to understand how these counts translate to actual `positions` and `target_positions`.
### Recorded reasoning summary
Clarifying Filtering Logic
I'm re-examining the `build_known_positive_filter` function. It appears to be filtering based on offset and counts, potentially including older, discarded sequence items rather than the actual positive targets from the current training window. This needs immediate correction to ensure accurate training data.
### Recorded reasoning summary
Correcting Filtering Logic
I've identified a critical flaw in `build_known_positive_filter`. The current logic incorrectly assumes the training sequence spans `0` to `target_count + 1`, neglecting the actual sequence length. This is misinterpreting positive items, and I need to align it with the true user history range.
### read_file · 2026-09-29T02:14:38.124Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 240,
"end_line": 320
}
### Tool output · 2026-09-29T02:14:38.131Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 240-320 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 321.
--- FILE CONTENT (truncated) ---
(completion_sum - before_completion)
/ np.maximum(counts, 1)
/ 100.0
).astype(np.float16)
return result
def build_sequence_parts(
listens: base.ListenEvents,
explicit: base.ExplicitEvents,
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
items = listens.items[qualifying].astype(np.uint16, copy=False)
organic = listens.organic[qualifying].astype(np.float32, copy=False)
completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
if not len(items):
return None
event_features = np.zeros((len(items), 3), dtype=np.float16)
event_features[:, 0] = organic.astype(np.float16)
event_features[:, 1] = (
np.minimum(completion, 100.0) / 100.0
).astype(np.float16)
if len(items) > 1:
gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
event_features[1:, 2] = np.exp(
-gaps.astype(np.float64) / (60.0 * base.DAY)
).astype(np.float16)
activity = causal_activity_features(
timestamps, items, organic, completion
)
states = np.zeros(len(items), dtype=np.uint32)
active_likes: dict[int, int] = {}
active_dislikes: dict[int, int] = {}
explicit_position = 0
current_state = 0
for row, timestamp in enumerate(timestamps):
changed = False
while (
explicit_position < len(explicit.timestamps)
and int(explicit.timestamps[explicit_position]) < int(timestamp)
):
item = int(explicit.items[explicit_position])
event_timestamp = int(explicit.timestamps[explicit_position])
destination = (
active_likes
if int(explicit.channel[explicit_position]) == 0
else active_dislikes
)
if int(explicit.active[explicit_position]) == 1:
destination[item] = event_timestamp
else:
destination.pop(item, None)
explicit_position += 1
changed = True
if changed:
current_state = append_state_snapshot(
active_likes,
active_dislikes,
n_items,
liked_states,
disliked_states,
state_counts,
)
states[row] = np.uint32(current_state)
return timestamps, items.copy(), event_features, activity, states
def final_user_features(
timestamps: np.ndarray,
items: np.ndarray,
event_features: np.ndarray,
history_end: int,
like_count: int,
dislike_count: int,
## Step 36 · agent · 2026-09-29T02:14:50.187Z
### Recorded reasoning summary
Refining Sequence Generation
I'm focusing on refining the sequence generation logic within `build_sequence_parts`. Specifically, I'm ensuring that the `qualifying` boolean mask correctly isolates all positive listens, identified by `completion_pct` meeting or exceeding `base.LISTEN_POSITIVE_THRESHOLD`. This filtered subset of listens will form the basis of our user sequences.
### Recorded reasoning summary
Analyzing Partitioning Strategy
I'm currently examining the `target_partition` function and its impact on data splitting. The logic for calculating `validation` and `training` portions, especially with large `items` lengths and the `max_train_targets_per_user` constraint, is a key focus. I'm also reviewing how `build_dense_block` uses these calculated windows and offsets for feature extraction.
### Recorded reasoning summary
Confirming Training Data Boundaries
I've confirmed that the model is trained on the last `max_train_targets_per_user` targets immediately preceding the validation set. This is achieved by calculating the training data range from `offset + window_start + 1` up to `offset + window_start + count + 1`, where `window_start` is derived from `training_region_ends - training`.
### Recorded reasoning summary
Revising Negative Sampling Logic
I'm thoroughly re-examining the negative sampling process, particularly how it interacts with the training and validation boundaries. It appears the current filtering logic for known positives is only considering a very limited initial segment of user history, potentially missing crucial interactions that should be excluded from negative sampling in later parts of the sequence. This needs immediate correction to ensure accurate model training.
### Recorded reasoning summary
Fixing Negative Sampling Flaws
I'm correcting a critical flaw where negative sampling was improperly excluding recently interacted items as potential negatives. This oversight was incorrectly penalizing the model for items the user actually liked. I'm now ensuring that all known positives, both historical and recent, are accurately excluded from negative candidate pools.
### grep_search · 2026-09-29T02:14:50.187Z
{
"pattern": "build_known_positive_filter",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T02:14:50.203Z
Found 2 matches for pattern "build_known_positive_filter" in path "/app/solution":
---
File: train_current_model.py
L563- )
L564- normalization = float(np.sum(counts * raw) / max(total, 1.0))
L565- correction = np.clip(
L566- raw / max(normalization, 1.0e-12),
L567- config.inbatch_correction_min,
L568- config.inbatch_correction_max,
L569- ).astype(np.float32)
L570- mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
L571- log(
L572- f"inbatch correction: min={correction.min():.3f} "
L573- f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
L574- )
L575- return torch.from_numpy(correction)
L576-
L577-
L578: def build_known_positive_filter(
L579- prepared: SequenceData, training_counts: np.ndarray
L580- ) -> np.ndarray:
L581- """Pack each user's complete public-training positive-item set."""
L582- width = (len(prepared.item_ids) + 7) // 8
L583- packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
L584- retained = 0
L585- for user_row, target_count in enumerate(training_counts):
L586- if not target_count:
L587- continue
L588- offset = int(prepared.offsets[user_row])
L589- # T adjacent targets consume sequence positions 0 through T. The
L590- # chronological BCE suffix starts at T + 1 and is intentionally absent.
L591- items = np.unique(
L592- prepared.sequence_items[
L593- offset : offset + int(target_count) + 1
L1551- generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
L1552- item_correction = build_item_correction(
L1553- prepared,
L1554- config,
L1555- partition.window_starts,
L1556- training_counts,
L1557- )
L1558- target_weights = build_target_weights(
L1559- public,
L1560- stage,
L1561- prepared,
L1562- partition.history_training_counts,
L1563- config,
L1564- )
L1565- known_positive_packed = (
L1566: build_known_positive_filter(prepared, partition.history_training_counts)
L1567- if config.negative_filter_scope == "full-training-history"
L1568- else None
L1569- )
L1570- history: list[dict[str, float]] = []
L1571- best_loss = float("inf")
L1572- best_epoch = 0
L1573- best_state: dict[str, torch.Tensor] | None = None
L1574- epochs_without_improvement = 0
L1575- start_epoch = 0
L1576-
L1577- if resume_checkpoint is not None:
L1578- payload = torch.load(
L1579- resume_checkpoint, map_location=device, weights_only=False
L1580- )
L1581- stored_config = payload.get("config", {})
---
## Step 37 · agent · 2026-09-29T02:15:03.701Z
### Recorded reasoning summary
Processing Partition Data
I've confirmed that `partition.history_training_counts`, derived from `totals - validation`, was successfully passed into the `build_known_positive_filter` function. This indicates progress in how we're handling the historical training counts for partition analysis.
### read_file · 2026-09-29T02:15:03.701Z
{
"start_line": 1364,
"end_line": 1480,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:15:03.707Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1364-1480 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1481.
--- FILE CONTENT (truncated) ---
def evaluate_validation_bce(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
np.argsort(totals[users_with_validation], kind="stable")
]
groups = [
ordered[start : start + config.user_batch_size]
for start in range(0, len(ordered), config.user_batch_size)
]
generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
total_loss = 0.0
total_targets = 0
total_weight = 0.0
started = time.monotonic()
for group_number, users in enumerate(groups, start=1):
group_totals = totals[users]
group_starts = training_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_totals.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_totals,
group_starts,
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0)
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
statistics, _ = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=False,
)
total_loss += statistics["loss_sum"]
total_targets += len(positive_items)
total_weight += statistics["weight_sum"]
if group_number % 25 == 0:
release_accelerator_cache(device)
return {
## Step 38 · agent · 2026-09-29T02:15:07.619Z
### read_file · 2026-09-29T02:15:07.619Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1700,
"start_line": 1520
}
### Tool output · 2026-09-29T02:15:07.626Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1520-1700 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1701.
--- FILE CONTENT (truncated) ---
def train_model(
public: Path,
stage: str,
prepared: SequenceData,
config: Config,
device: torch.device,
checkpoint_dir: Path | None,
resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
training_counts = partition.training_counts
validation_counts = partition.validation_counts
log(
f"chronological BCE window: train={int(training_counts.sum()):,} "
f"validation={int(validation_counts.sum()):,} "
f"selected={partition.selected_count:,} "
f"discarded_old_targets={int(partition.window_starts.sum()):,} "
f"all={prepared.target_count:,}"
)
model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
device
)
optimizer = torch.optim.AdamW(
model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
)
generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
item_correction = build_item_correction(
prepared,
config,
partition.window_starts,
training_counts,
)
target_weights = build_target_weights(
public,
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
"negative_filter_scope", "last-100"
)
stored_target_weighting = stored_config.get(
"target_weighting", "uniform"
)
stored_weight_values = (
stored_config.get("ordinary_target_weight", 1.0),
stored_config.get("organic_strong_target_weight", 1.0),
stored_config.get("organic_like_target_weight", 3.0),
stored_config.get("discovery_horizon_days", 7),
)
current_weight_values = (
config.ordinary_target_weight,
config.organic_strong_target_weight,
config.organic_like_target_weight,
config.discovery_horizon_days,
)
stored_window_values = (
stored_config.get("validation_fraction", 0.10),
stored_config.get("max_train_targets_per_user", 0),
stored_config.get("max_validation_targets_per_user", 0),
)
current_window_values = (
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
objective_changed = (
stored_filter_scope != config.negative_filter_scope
or stored_target_weighting != config.target_weighting
or stored_weight_values != current_weight_values
or stored_window_values != current_window_values
)
model.load_state_dict(payload["model_state"])
if "optimizer_state" in payload and not objective_changed:
optimizer.load_state_dict(payload["optimizer_state"])
for group in optimizer.param_groups:
group["lr"] = config.learning_rate
group["weight_decay"] = config.weight_decay
log(f"restored optimizer state from {resume_checkpoint}")
else:
log(
f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
)
if "generator_state" in payload and not objective_changed:
generator.set_state(payload["generator_state"])
start_epoch = int(payload["epoch"])
if start_epoch >= config.epochs:
raise RuntimeError(
f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
)
prior = {
str(name): float(value)
for name, value in payload.get("diagnostics", {}).items()
}
if objective_changed:
prior.pop("validation_loss", None)
log(
"BCE objective changed: filter "
f"{stored_filter_scope}->{config.negative_filter_scope}, "
"target weighting "
f"{stored_target_weighting}->{config.target_weighting}, "
f"target window {stored_window_values}->{current_window_values}; "
"recomputing the resume checkpoint's validation BCE"
)
if "validation_loss" not in prior:
prior.update(
evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
)
history.append(prior)
best_loss = float(prior["validation_loss"])
best_epoch = start_epoch
best_state = snapshot_model(model)
log(
f"resumed validation-BCE training: epoch={start_epoch} "
f"loss={best_loss:.6f}"
)
for epoch in range(start_epoch + 1, config.epochs + 1):
diagnostics = run_training_epoch(
model,
optimizer,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
item_correction,
known_positive_packed,
## Step 39 · agent · 2026-09-29T02:15:09.481Z
### read_file · 2026-09-29T02:15:09.481Z
{
"start_line": 1700,
"end_line": 1780,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:15:09.487Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1700-1780 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1781.
--- FILE CONTENT (truncated) ---
known_positive_packed,
generator,
device,
epoch,
)
validation = evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
diagnostics.update(validation)
history.append(diagnostics)
current_state = snapshot_model(model)
if checkpoint_dir is not None:
save_epoch_checkpoint(
checkpoint_dir,
epoch,
current_state,
config,
diagnostics,
optimizer,
generator,
)
improved = (
diagnostics["validation_loss"]
< best_loss - config.early_stopping_min_delta
)
if improved:
best_loss = diagnostics["validation_loss"]
best_epoch = epoch
best_state = current_state
epochs_without_improvement = 0
if checkpoint_dir is not None:
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": current_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
checkpoint_dir / "best.pt",
)
else:
epochs_without_improvement += 1
log(
f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
f"validation_loss={diagnostics['validation_loss']:.5f} "
f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
f"train_targets={int(diagnostics['targets']):,} "
f"validation_targets={int(diagnostics['validation_targets']):,} "
f"train_seconds={diagnostics['seconds']:.1f} "
f"validation_seconds={diagnostics['validation_seconds']:.1f}"
)
release_accelerator_cache(device)
if epochs_without_improvement >= config.early_stopping_patience:
log(
f"early stopping after epoch={epoch}; "
f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
)
break
if best_state is None:
raise RuntimeError("training did not produce a valid checkpoint")
model.load_state_dict(best_state)
model.to(device).eval()
log(
f"restored best validation-BCE checkpoint: epoch={best_epoch} "
f"loss={best_loss:.6f}"
)
return model, history
## Step 40 · agent · 2026-09-29T02:15:24.815Z
### Recorded reasoning summary
Exploring Epoch Defaults
I'm investigating the default setting of `config.epochs` to 1. My initial hypothesis is that this is a performance optimization, as a single epoch currently consumes approximately 210 seconds, and with early stopping set at a patience of 1, the process would terminate after this initial evaluation.
### Recorded reasoning summary
Analyzing Negative Sampling
I'm now delving into the specifics of how negative examples are selected within `train_current_model.py`. My current focus is on dissecting the `draw_full_history_filtered_negatives` function to understand the exact mechanisms for both uniform and in-batch negative sampling.
### Recorded reasoning summary
Refining Negative Sampling
I'm examining the `inbatch_negatives` logic. I've identified that `targets` originates from `positive_items`, which are derived from the BPTT steps within a user's training block, specifically the positive item IDs. This clarification is crucial for understanding the context of in-batch negative selection.
### Recorded reasoning summary
Clarifying In-Batch Negatives
I've confirmed that `targets` contains up to 6,400 items per BPTT block. Importantly, the system samples 8 items from `unique_targets` for in-batch negatives. I'm now investigating how this differs from standard in-batch negative sampling in recommender systems, specifically focusing on the correction mechanism applied by `build_item_correction`.
### Recorded reasoning summary
Adjusting Loss Calculations
I'm currently investigating the `inbatch_correction_power` parameter, noting its default value of 0.5. My focus is on how this value, along with the `raw` and `correction` tensors derived from item probabilities and counts, impacts the `corrected_negative_loss` calculation. This appears to be a key area influencing the model's learning from negative samples.
### Recorded reasoning summary
Investigating Loss Weights
I'm now examining how the `raw` and `correction` tensors are used in the loss calculation. It appears `raw`, representing the BCE loss for negative items, is multiplied by `correction` weights. I'm digging into the origin of these `correction` weights to understand their impact on the `corrected_negative_loss`.
### Recorded reasoning summary
Correcting Loss Weighting
I'm realizing my previous assumption about inverse propensity weighting was incomplete. The correction logic seems to be inversely related to item popularity for in-batch negatives. High probability items, sampled more frequently, get smaller weights, and low probability items, sampled less often, receive larger weights. This aligns more with bias correction than simple inverse propensity.
### run_shell_command · 2026-09-29T02:15:24.815Z
{
"description": "Check candidate counts",
"command": "python3 -c \"\nimport numpy as np\nwith np.load('/task/data/validation_eligible_candidates.npz') as f:\n uids = f['uids']\n item_ids = f['item_ids']\n packed = f['packed_eligible']\n print(f'users: {len(uids)}, items: {len(item_ids)}')\n # count eligible per user\n sample_mask = np.unpackbits(packed[0], count=len(item_ids), bitorder='little')\n print('User 0 eligible items:', sample_mask.sum())\n\""
}
### Tool output · 2026-09-29T02:15:25.316Z
Output: users: 8456, items: 30000
User 0 eligible items: 29318
Process Group PGID: 1734
## Step 41 · agent · 2026-09-29T02:15:28.477Z
### Recorded reasoning summary
Reviewing Catalog Data
I'm analyzing the vast catalog size, noting approximately 30,000 items. My focus is on determining eligible items per user, which appears to be around 29,318, excluding those already interacted with. Next, I need to pinpoint the true positive items within the validation window for each user.
### run_shell_command · 2026-09-29T02:15:28.477Z
{
"description": "Inspect validation positive distribution",
"command": "python3 -c \"\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\neligibility = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 positive:', len(graded))\npos_counts = [len(v) for v in graded.values()]\nprint('Average positives per positive user:', np.mean(pos_counts), 'median:', np.median(pos_counts), 'max:', np.max(pos_counts))\ngrade3_counts = [sum(1 for g in v.values() if g == 3) for v in graded.values()]\nprint('Average grade 3 per user:', np.mean(grade3_counts))\n\""
}
### Tool output · 2026-09-29T02:15:29.970Z
Output: Traceback (most recent call last):
File "", line 4, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1744
## Step 42 · agent · 2026-09-29T02:15:42.950Z
### run_shell_command · 2026-09-29T02:15:42.950Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\neligibility = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 positive:', len(graded))\npos_counts = [len(v) for v in graded.values()]\nprint('Average positives per positive user:', np.mean(pos_counts), 'median:', np.median(pos_counts), 'max:', np.max(pos_counts))\ngrade3_counts = [sum(1 for g in v.values() if g == 3) for v in graded.values()]\nprint('Average grade 3 per user:', np.mean(grade3_counts))\n\"",
"description": "Inspect validation positive distribution"
}
### Tool output · 2026-09-29T02:15:45.009Z
Output: Number of users with at least 1 positive: 4263
Average positives per positive user: 8.859723199624677 median: 4.0 max: 442
Average grade 3 per user: 1.0361247947454844
Process Group PGID: 1755
## Step 43 · agent · 2026-09-29T02:15:48.185Z
### Recorded reasoning summary
Analyzing Baseline Predictions
I'm currently examining the baseline model's top 10 predictions from `/app/solution/production_validation.parquet`. My focus is on understanding the characteristics of these predicted items – their popularity, any repetition, and how they align with the 4263 users who have at least one positive.
### run_shell_command · 2026-09-29T02:15:48.185Z
{
"description": "Check item distribution in baseline predictions",
"command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/production_validation.parquet')\nprint('Total rows:', len(df))\nprint('Unique items recommended:', df.item_id.nunique())\ntop_recs = df[df['rank'] <= 10].item_id.value_counts()\nprint('Top 10 recommended items (frequency across users in top 10):')\nprint(top_recs.head(15))\nprint('Number of unique items in top 10:', df[df['rank'] <= 10].item_id.nunique())\n\""
}
### Tool output · 2026-09-29T02:15:49.765Z
Output: Total rows: 845600
Unique items recommended: 2575
Top 10 recommended items (frequency across users in top 10):
item_id
6006390 2917
8213481 2800
6896222 2712
8743390 2653
5436843 2587
2651314 2580
8722394 2542
3542184 2427
5862961 2292
906358 2066
5927996 1872
9378983 1537
2185191 1522
6901374 1463
3033749 1333
Name: count, dtype: int64
Number of unique items in top 10: 592
Process Group PGID: 1775
## Step 44 · agent · 2026-09-29T02:15:53.659Z
### Recorded reasoning summary
Analyzing User Recommendations
I'm focusing on the recommendation performance of specific items. Item 6006390 is standing out significantly, appearing in the top 10 for a substantial portion of users. It's interesting to see how few unique items actually make it into these top recommendations overall.
### run_shell_command · 2026-09-29T02:15:53.659Z
{
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntop_items = [6006390, 8213481, 6896222, 8743390, 5436843, 2651314, 8722394, 3542184]\nprint(cat[cat.item_id.isin(top_items)][['item_id', 'popularity_rank', 'positive_listens', 'organic_positive_listens']])\n\"",
"description": "Check popularity of baseline top recommendations"
}
### Tool output · 2026-09-29T02:15:55.212Z
Output: item_id popularity_rank positive_listens organic_positive_listens
3 3542184 4 24075 17699
6 8213481 7 22505 17071
10 2651314 11 16286 11801
13 5436843 14 14897 12127
21 8743390 22 13518 8480
74 6006390 75 8970 6813
633 8722394 634 3464 1695
817 6896222 818 3020 2017
Process Group PGID: 1793
## Step 45 · agent · 2026-09-29T02:15:58.322Z
### Recorded reasoning summary
Analyzing Popularity Dynamics
I'm currently investigating the popularity of top recommended items, comparing them against items explicitly identified as true positives within the `graded` set. My focus is on discerning whether these true positives lean towards general popularity or represent more personalized, niche preferences, and crucially, whether this popularity bias is negatively impacting NDCG@10 scores.
### run_shell_command · 2026-09-29T02:15:58.322Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\neligibility = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\nranks = []\nfor uid, items in graded.items():\n for item in items:\n if item in cat.index:\n ranks.append(cat.loc[item, 'popularity_rank'])\n\nprint('True positives popularity rank:')\nprint(pd.Series(ranks).describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]))\n\"",
"description": "Check popularity of true validation positive items"
}
### Tool output · 2026-09-29T02:16:00.643Z
Output: True positives popularity rank:
count 37769.000000
mean 8463.087744
std 8117.953262
min 1.000000
10% 428.000000
25% 1733.000000
50% 5622.000000
75% 13374.000000
90% 21680.200000
max 30000.000000
dtype: float64
Process Group PGID: 1810
## Step 46 · agent · 2026-09-29T02:16:42.530Z
### Recorded reasoning summary
Analyzing Discovery Data
My current focus is on the "Made for You" music discovery surface. I'm observing that true discovery positives have a median popularity rank of 5,622, with the 75th percentile at 13,374. Users are exploring a wide range of tracks here.
### Recorded reasoning summary
Investigating Recommendation Logic
I'm digging into why the current model is universally recommending highly popular tracks despite the observed distribution of user discovery. My next step is to meticulously examine the training objective and negative sampling mechanics to pinpoint the root cause of this behavior.
### Recorded reasoning summary
Refining Loss Function
I'm currently examining the `corrected_negative_loss` function, specifically how `uniform_negatives` and `inbatch_negatives` are weighted and combined. My goal is to understand how this impacts the overall loss calculation and the model's tendency to favor popular tracks.
### Recorded reasoning summary
Re-evaluating Negative Sampling
I'm scrutinizing the `corrected_negative_loss` implementation, particularly the sampling of `uniform_negatives`. It appears this method, by drawing from obscure tracks, isn't effectively teaching the model to differentiate relevant from popular items. My next step is to deeply investigate how in-batch negatives are being sampled to understand their contribution.
### Recorded reasoning summary
Adjusting In-Batch Sampling
I've identified a potential issue in how `unique_targets` are being processed for in-batch negatives. The current method might be inadvertently including some popular items and not effectively distinguishing them from truly relevant ones. I'm investigating the `torch.unique` and subsequent sampling logic.
### Recorded reasoning summary
Fixing In-Batch Logic
I've discovered the core issue: `torch.unique` is applied before sampling in-batch negatives, destroying frequency weighting. This means popular items aren't being sampled proportionally, undermining the intended effect. I'm now investigating how to correct this to ensure proper in-batch negative sampling.
### Recorded reasoning summary
Correcting Loss Weighting
I've uncovered a significant issue where the `corrected_negative_loss` incorrectly downweights popular items sampled as in-batch negatives. This is due to the `weights` calculation multiplying their loss by 0.25, while rare items get amplified. This, combined with the previous `unique_targets` flattening, severely distorts the learning signal.
### Recorded reasoning summary
Verifying Theory
I'm now cross-referencing my findings with established research, specifically "Sampling-bias-corrected neural modeling for large-scale recommendation." My goal is to ensure my understanding of the theoretical underpinnings of negative sampling and bias correction aligns precisely with the implementation.
### Recorded reasoning summary
Validating Training Objectives
I'm now comparing the theoretical advantages of logit correction, as found in research, against the current implementation's use of Binary Cross-Entropy (BCE) loss. My next step is to determine if the codebase supports alternative objectives like Softmax or InfoNCE, which might better handle popularity bias.
### Recorded reasoning summary
Revising Loss Formulation
I'm now exploring how to revise the loss formulation. My current thinking is to remove the erroneous weight calculation and ensure that positive items are not inadvertently treated as negatives within the in-batch sampling. I'm also considering how to better implement logit correction.
### Recorded reasoning summary
Testing New Recommendations
I'm now validating the corrected loss formulation against the public backtest to ensure it trains and ranks efficiently within the CPU environment while respecting serving and data boundaries. This step is crucial for confirming the practical impact of the changes.
### Recorded reasoning summary
Replacing Invalid Negatives
I'm now actively replacing invalid in-batch negatives. The code iterates, identifies problematic samples, and replaces them with new random draws from the item pool. This process ensures we're always learning from genuinely distinct negative examples, refining the model’s understanding.
### Recorded reasoning summary
Replacing Invalid Negatives
I'm actively replacing invalid in-batch negatives by iterating and drawing new random samples from the item pool. This ensures the model learns from genuinely distinct negative examples. I'm also re-evaluating the number of uniform negatives sampled, as the current count seems excessively high given the catalog size.
### Recorded reasoning summary
Investigating Batch Size
I'm questioning the fixed number of 8 in-batch negatives. Given the potential for 500-1000 unique targets and a much larger overall item pool, this feels overly restrictive and likely insufficient for robust learning. I'm also questioning the fixed 24 uniform negatives.
### Recorded reasoning summary
Evaluating Unique Item Encoding
I'm re-examining the `candidate_vectors` function. I've realized that `torch.unique` is applied before encoding, meaning I might be encoding the same item multiple times if it appears in both positives and negatives, or across different batch examples. This is inefficient and could lead to inconsistent vector representations.
### Recorded reasoning summary
Implementing In-Batch Negatives
I'm realizing the potential of true in-batch negatives. Instead of fixed uniform or in-batch samples, I can leverage all other items within a microbatch as negatives for a given query. This simplifies computation, reduces the need for explicit sampling, and increases the number of effective negatives per query, leading to a more robust training signal.
### Recorded reasoning summary
Processing User Data
I'm now observing how user data is batched and processed. For each group of 64 users, up to 100 BPTT steps occur, resulting in a large number of positive targets. I'm examining the `target_weighting` configuration, specifically the default `"organic-strong"`, and its impact on the weights assigned to different types of targets.
### Recorded reasoning summary
Analyzing Target Weights
I'm focusing on how organic listen percentages and likes are weighted within the loss function. My current analysis reveals that organic likes, representing grade 3 relevance, have a significantly higher gain (7x) in the graded NDCG@10 metric compared to 80% organic listens (grade 1). This disparity needs careful consideration in my formulation.
### Recorded reasoning summary
Revising Target Weighting
I'm now examining the impact of different `target_weighting` configurations on the loss. My analysis suggests that the default `"organic-strong"` is not optimal for discovery and that moving towards a `"discovery-graded"` approach is necessary to improve performance. I'm also re-evaluating the weight assigned to organic likes versus organic listens.
### Recorded reasoning summary
Refining Negative Sampling Strategy
I'm now focusing on the `_sample_negatives` function, specifically how `uniform` and `inbatch` negatives are drawn. My initial analysis suggests the current approach to `inbatch` sampling might not be optimal given the number of `unique_targets` and the overall `n_items`. I'm investigating the logic for `corrected_count` and `priorities` to ensure we're effectively sampling relevant negatives.
### grep_search · 2026-09-29T02:16:42.530Z
{
"dir_path": "/app/solution",
"pattern": "dislike"
}
### Tool output · 2026-09-29T02:16:44.950Z
Found 40 matches for pattern "dislike" in path "/app/solution":
---
File: train_current_model.py
L110: disliked_states: np.ndarray
L114: inference_disliked_items: np.ndarray
L176: active_dislikes: dict[int, int],
L179: disliked_states: list[np.ndarray],
L183: disliked_states.append(
L184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186: state_counts.append((len(active_likes), len(active_dislikes)))
L252: disliked_states: list[np.ndarray],
L279: active_dislikes: dict[int, int] = {}
L293: else active_dislikes
L304: active_dislikes,
L307: disliked_states,
L320: dislike_count: int,
L337: result[5] = np.log1p(dislike_count) / 4.0
L369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383: disliked_states = [
L384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405: disliked_states,
L427: int(dislike_counts[inference_row]),
L440: np.log1p(dislike_counts[missing]) / 4.0
L455: disliked_states=np.stack(disliked_states),
L459: inference_disliked_items=inference_dislikes,
L784: disliked_items = torch.from_numpy(
L785: prepared.disliked_states[unique_states].astype(np.int64)
L788: disliked_vectors = model._mean_context_channel(disliked_items)
L793: disliked_vectors[inverse_rows],
L802: disliked_vectors: torch.Tensor,
L807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159: disliked_vectors,
L1844: disliked_items = torch.from_numpy(
L1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851: model._mean_context_channel(disliked_items),
---
File: two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184: ("dislikes", 1, 1, 0),
L185: ("undislikes", 1, 0, 1),
L265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281: return liked, disliked, like_counts, dislike_counts
---
## Step 47 · agent · 2026-09-29T02:16:48.935Z
### Recorded reasoning summary
Analyzing Input Features
I'm currently examining how dislikes are handled within the user tower. My current understanding is that they're solely an input feature, never treated as hard negatives. I'm now shifting focus to the `listens` data to see its role.
### run_shell_command · 2026-09-29T02:16:48.935Z
{
"description": "Inspect train_listens stats",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet')\nprint('Schema:', table.schema)\nprint('Total rows:', table.num_rows)\n# sample played_ratio_pct\nratios = table['played_ratio_pct'].slice(0, 1000000).to_numpy()\nprint('played_ratio_pct < 20%:', (ratios < 20).mean())\nprint('played_ratio_pct < 50%:', (ratios < 50).mean())\nprint('played_ratio_pct >= 80%:', (ratios >= 80).mean())\n\""
}
### Tool output · 2026-09-29T02:16:51.021Z
Output: Schema: uid: uint32
timestamp: uint32
item_id: uint32
is_organic: uint8
played_ratio_pct: uint16
track_length_seconds: uint32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797
Total rows: 43484159
played_ratio_pct < 20%: 0.313242
played_ratio_pct < 50%: 0.375987
played_ratio_pct >= 80%: 0.585347
Process Group PGID: 1832
## Step 48 · agent · 2026-09-29T02:17:31.538Z
### Recorded reasoning summary
Analyzing Skip Patterns
I'm diving deeper into the skip data, specifically the 37.6% of listens that are under 50%. I'm re-examining `invalid_negative_mask` and `invalid_full_history_negatives` for any insights into these rapid disengagements.
### Recorded reasoning summary
Refining Negative Filtering
I've identified a potential issue with how `liked_items` is utilized within `invalid_full_history_negatives`. It seems the current implementation, with `LIKE_ITEMS` set to 48, might be implicitly considering a broader set of "liked" items than intended, potentially impacting the accuracy of my negative sample filtering, especially for those short listens.
### Recorded reasoning summary
Investigating Item Inclusion
I'm realizing `liked_states` isn't in `invalid_negative_mask`, which is logical as disliked items are indeed negatives. However, I'm now questioning the role of `known_positive_packed` within `build_known_positive_filter`, as its interaction with user and item data requires further scrutiny.
### Recorded reasoning summary
Re-evaluating Positive Data
I'm scrutinizing `build_known_positive_filter` and its inclusion of `prepared.sequence_items`. This now appears to encompass all positive listens, prompting me to consider how items present in `train_likes` but absent from `listens` are being handled.
### Recorded reasoning summary
Examining Candidate Exclusion
I'm realizing that at test and validation time, all "train\_\*" encounters, including listens, likes, and dislikes, are excluded from candidate eligibility. This is a significant constraint.
### Recorded reasoning summary
Analyzing Negative Sampling
I'm finding the current negative sampling strategy flawed. The uniform negatives are too easy, and the in-batch negatives aren't sampled by popularity as I'd assumed, meaning every unique item in the batch has an equal chance of being picked.
### Recorded reasoning summary
Revising Sampling Methods
I'm realizing my current negative sampling strategy is fundamentally flawed. Uniform negatives are too simplistic, and in-batch negatives aren't sampled by popularity as anticipated, leading to a model that fails to suppress popular, irrelevant items.
### Recorded reasoning summary
Adjusting Loss Weighting
I'm recalibrating the in-batch negative sampling. The current approach over-penalizes rare items and under-penalizes popular ones by clipping their correction factors. I'm refining the `correction` calculation to better reflect item popularity, aiming for a more balanced loss.
### Recorded reasoning summary
Correcting Loss Application
I've discovered a critical flaw in how in-batch negatives are weighted within the loss calculation. Popular items are being excessively down-weighted when they appear as negatives, effectively reducing their penalty and hindering model performance. This needs immediate correction.
### Recorded reasoning summary
Correcting Gradient Impact
I'm realizing that by heavily penalizing popular items less when they act as negatives, the model learns to universally favor popular items due to their near immunity to negative gradients. This is a significant issue for personalized recommendations.
### Recorded reasoning summary
Correcting Popularity Bias
I'm realizing my previous adjustments still allow popular items, even as negatives, to dominate the loss too much. I need to implement true logit correction for sampled softmax to ensure popular and unpopular items are treated fairly, preventing the model from universally favoring popular candidates.
### Recorded reasoning summary
Correcting Logit Scale
I'm revising the logit scaling for in-batch negatives. Instead of simply subtracting the log probability, I'm implementing a correction that subtracts $\log(p_i)$ for both positive and negative items to properly account for popularity bias in the sampled softmax.
### Recorded reasoning summary
Revising Negative Sampling Logic
I'm exploring how negative items are sampled, considering if popularity-based sampling proportional to frequency, like $(\text{frequency}_i)^\alpha$, might improve the model's ability to distinguish specific user preferences from general popularity. I am now inspecting `train_current_model.py` to understand the intended configuration options for this sampling strategy.
### Recorded reasoning summary
Exploring Sampling Weights
I am now investigating the `inbatch_correction_power` and `inbatch_correction_min/max` parameters within `TrainConfig`, as these seem directly related to how popularity influences negative sampling. I am also looking at `ordinary_target_weight`, `organic_strong_target_weight`, and `organic_like_target_weight` to understand how different types of positive interactions are weighted.
### Recorded reasoning summary
Adjusting Sampling Parameters
I am now examining the `inbatch_correction_power`, `inbatch_correction_min`, and `inbatch_correction_max` parameters to fine-tune the influence of item popularity on negative sampling. Additionally, I am assessing `ordinary_target_weight`, `organic_strong_target_weight`, and `organic_like_target_weight` to understand how various positive interactions are weighted.
### Recorded reasoning summary
Investigating Parameter Sources
I'm realizing that several key negative sampling parameters like `inbatch_correction_power` are hardcoded within the `Config` class rather than being exposed as CLI arguments. This limits direct control over their tuning during experimentation, prompting a closer look at how they are intended to be configured in the production environment.
### Recorded reasoning summary
Analyzing Selection Strategy
I am now intently focused on *how* negative examples are selected. My recent investigations into sampling parameters and their hardcoded nature within the `Config` class have highlighted this as a critical area needing scrutiny.
### Recorded reasoning summary
Refining Selection Strategy
My focus is now on the selection mechanisms within `draw_training_negatives`. I'm scrutinizing the overwhelming proportion of uniform negatives and how the in-batch sampling is flattened by `torch.unique`, thus losing the intended popularity weighting critical for effective negative example generation.
### Recorded reasoning summary
Revising Negative Sampling Logic
I'm exploring how negative items are sampled, considering if popularity-based sampling proportional to frequency, like $(\text{frequency}_i)^\alpha$, might improve the model's ability to distinguish specific user preferences from general popularity. I am now inspecting `train_current_model.py` to understand the intended configuration options for this sampling strategy.
### Recorded reasoning summary
Analyzing Sequence Inclusion
I'm realizing the `prepared.sequence_items` slice includes `target_count + 1` items, not just the targets. This means the filter is built using an extra item that might not be a true positive. I'm going to check `target_count`'s definition.
### run_shell_command · 2026-09-29T02:17:31.538Z
{
"description": "Check lengths vs history_training",
"command": "python3 -c \"\nimport numpy as np\ncache = np.load('/app/solution/validation_cache.npz')\nlengths = cache['lengths']\nprint('lengths min, mean, max:', lengths.min(), lengths.mean(), lengths.max())\ntotals = np.maximum(lengths - 1, 0)\nval_fraction = 0.10\nvalidation = np.floor(totals * val_fraction).astype(np.int64)\nvalidation[(totals >= 2) & (validation == 0)] = 1\nvalidation = np.minimum(validation, 16)\nhistory_training = totals - validation\nprint('history_training min, mean, max:', history_training.min(), history_training.mean(), history_training.max())\nprint('Difference lengths - history_training:', (lengths - history_training).min(), (lengths - history_training).max())\n\""
}
### Tool output · 2026-09-29T02:17:31.930Z
Output: Traceback (most recent call last):
File "", line 4, in
File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 261, in __getitem__
raise KeyError(f"{key} is not a file in the archive")
KeyError: 'lengths is not a file in the archive'
Exit Code: 1
Process Group PGID: 1853
## Step 49 · agent · 2026-09-29T02:17:37.233Z
### run_shell_command · 2026-09-29T02:17:37.233Z
{
"description": "Check keys in validation_cache.npz",
"command": "python3 -c \"\nimport numpy as np\ncache = np.load('/app/solution/validation_cache.npz')\nprint(cache.files)\n\""
}
### Tool output · 2026-09-29T02:17:37.628Z
Output: ['metadata', 'item_ids', 'item_features', 'user_ids', 'offsets', 'sequence_timestamps', 'sequence_items', 'event_features', 'activity_features', 'state_ids', 'liked_states', 'disliked_states', 'state_counts', 'inference_user_rows', 'inference_liked_items', 'inference_disliked_items', 'inference_user_features']
Process Group PGID: 1863
## Step 50 · agent · 2026-09-29T02:17:40.915Z
### run_shell_command · 2026-09-29T02:17:40.915Z
{
"description": "Check lengths vs history_training using offsets",
"command": "python3 -c \"\nimport numpy as np\ncache = np.load('/app/solution/validation_cache.npz')\noffsets = cache['offsets']\nlengths = offsets[1:] - offsets[:-1]\nprint('lengths min, mean, max:', lengths.min(), lengths.mean(), lengths.max())\ntotals = np.maximum(lengths - 1, 0)\nval_fraction = 0.10\nvalidation = np.floor(totals * val_fraction).astype(np.int64)\nvalidation[(totals >= 2) & (validation == 0)] = 1\nvalidation = np.minimum(validation, 16)\nhistory_training = totals - validation\nprint('history_training min, mean, max:', history_training.min(), history_training.mean(), history_training.max())\nprint('Difference lengths - history_training:', (lengths - history_training).min(), (lengths - history_training).max())\n\""
}
### Tool output · 2026-09-29T02:17:41.306Z
Output: lengths min, mean, max: 1 2195.5477832512315 25779
history_training min, mean, max: 0 2180.3055281882866 25762
Difference lengths - history_training: 1 17
Process Group PGID: 1873
## Step 51 · agent · 2026-09-29T02:18:45.976Z
### Recorded reasoning summary
Refining Sequence Logic
I'm currently focused on the exact indexing for sequence lengths, ensuring they align correctly with validation counts and the chronological BCE suffix. This involves a precise mapping of T adjacent targets to sequence positions.
### Recorded reasoning summary
Revising Validation Logic
I'm re-evaluating how validation data is excluded. It seems my previous indexing might be too broad, potentially filtering out necessary training targets. I'm now scrutinizing the offset and count logic to ensure only truly historical data is disregarded.
### Recorded reasoning summary
Clarifying Negative Sample Logic
I'm analyzing how `known` and `invalid` flags interact to define negative samples. Specifically, I'm verifying that items encountered in `train_*` sequences are correctly excluded as candidates during inference, and how this impacts the training objective by ensuring past positive interactions aren't mistakenly penalized.
### Recorded reasoning summary
Analyzing Loss Function
I'm dissecting the `corrected_negative_loss` function line by line. My focus is on how `logits` are transformed by `softplus` and how `uniform_negatives` and `inbatch_negatives` contribute to the final loss calculation, particularly the weighting and averaging mechanisms.
### Recorded reasoning summary
Analyzing Loss Contribution
I'm detailing how uniform and in-batch negatives are aggregated. Specifically, I've noted how the `mean` for uniform negatives becomes a sum, and the in-batch negatives are a weighted average, both then scaled by their respective configuration weights before final averaging.
### Recorded reasoning summary
Adjusting Loss Gradients
I've identified a critical discrepancy: the gradient for positive logits is 32 times larger than for any individual uniform negative logit. This imbalance likely stems from the division by two before averaging positive and negative losses. I'm considering adjustments to the loss calculation to rectify this.
### Recorded reasoning summary
Correcting Loss Scaling
I'm realizing the negative loss is being averaged over 32 negatives, effectively giving each negative sample only 1/32nd of the weight of a single negative observation. This drastically imbalances gradients, pushing positive embeddings too hard while barely nudging negatives. I'm re-evaluating this scaling.
### Recorded reasoning summary
Investigating Popular Item Bias
I'm hypothesizing that popular items are over-attracting positive embeddings and under-repelling negative ones due to gradient scaling. To test this, I'll analyze dot products of popular items with random queries and examine baseline model outputs for evidence of this drift.
### Recorded reasoning summary
Investigating Popular Item Bias
My analysis confirms a severe bias towards a small set of highly popular items, dominating top-10 slots and pushing truly personalized discoveries down the ranking. This directly undermines NDCG@10 by preventing relevant items from appearing within the scored range.
### Recorded reasoning summary
Addressing Popularity Bias
I'm now analyzing how sampling and loss calculation contribute to popularity bias. My current focus is on the `corrected_negative_loss`, where popular items are significantly downweighted as negatives, receiving a much smaller penalty. Additionally, uniform negatives are not frequency-aware, and the overall negative loss scaling seems to amplify this issue.
### Recorded reasoning summary
Evaluating Sampling Strategies
I am exploring different negative sampling techniques to mitigate popularity bias. My current focus is on whether to employ popularity-aware sampling, BPR, or a modified BCE loss, and I am verifying if architectural constraints permit these changes.
### Recorded reasoning summary
Refining Sampling Techniques
I am currently evaluating popularity-aware sampling strategies and modified BCE loss functions to counteract the observed popularity bias. My primary concern is ensuring these adjustments are feasible within the existing CPU budget and architectural constraints, while also verifying their efficacy through the public backtest.
### Recorded reasoning summary
Analyzing Training Time
I'm investigating the training epoch's duration, specifically the bottlenecks within `run_training_epoch`. I've identified the batch processing and the sheer volume of `max_train_targets_per_user` as significant contributors to the observed 210-second runtime.
### Recorded reasoning summary
Analyzing InfoNCE Gradient
I'm examining the gradients derived from the InfoNCE loss. Specifically, I'm focused on how the probability distribution over positive and negative items, influenced by temperature scaling, dictates the magnitude and direction of gradient updates for item logits.
### Recorded reasoning summary
Testing Logit Correction
I'm exploring the application of logit correction to InfoNCE, inspired by Google's Sampling-Bias-Corrected Two-Tower model. This involves adjusting negative logits based on their sampling distribution, aiming to refine gradient updates and potentially optimize NDCG@10. I'm reviewing the baseline's approach to in-batch negative correction for comparison.
### Recorded reasoning summary
Fixing Correction Logic
I've identified a critical conceptual error in the implementation of propensity correction. It appears the correction was inverted, leading to rare items being overly penalized and popular items being immune to negative feedback. I'm now focused on rectifying this and exploring InfoNCE as an alternative.
### Recorded reasoning summary
Revising Correction Logic
I've discovered my propensity correction was inverted, penalizing rare items too much and making popular items immune to negative feedback. I'm fixing this and now considering InfoNCE as a superior alternative.
### Recorded reasoning summary
Analyzing Loss Aggregation
I'm reviewing how losses are aggregated and weighted. It appears positive and negative losses are averaged, then scaled by weights. I'm also examining how statistics like `pair_correct` are calculated and how `target_weighting` options might influence overall outcomes.
### Recorded reasoning summary
Analyzing Sequence Data
I'm examining how sequence data is processed, specifically focusing on the `discovery_horizon_days` and `base.DAY` multiplication. I'm also scrutinizing how repeated items within a sequence are handled and weighted, ensuring they don't receive undue influence as novel discoveries.
### Recorded reasoning summary
Adjusting Target Weights
I'm re-examining the target weighting system, noticing that "grade-3" novel organic likes within 7 days are assigned a weight of 3.0, while ordinary targets get 0.25. This strong weighting reflects the discovery objective, but I'm questioning why "organic-strong" was the default. I am now scrutinizing the negative sampling strategy as described.
### Recorded reasoning summary
Refining Sampling Logic
I'm now scrutinizing the exact logic for drawing negatives, particularly `draw_full_history_filtered_negatives`. My focus is on how in-batch negatives are selected from unique targets, ensuring the count is correctly bounded and sampled efficiently.
### Recorded reasoning summary
Fixing In-Batch Negatives
I've discovered that my implementation of in-batch negative sampling was fundamentally flawed. By taking unique targets and sampling uniformly from them, I wasn't actually performing in-batch negative sampling as intended. I am now correcting this logic.
### Recorded reasoning summary
Correcting In-Batch Sampling
I've uncovered that my previous in-batch negative sampling logic was flawed. Sampling uniformly from unique targets meant popular items weren't favored, unlike the true popularity distribution. I am now correcting this to accurately reflect item frequencies within the batch.
### Recorded reasoning summary
Investigating Correction Inversion
I'm questioning whether my prior implementation of inverse propensity weighting for in-batch negatives was inverted. It seems popular items are receiving significantly reduced negative loss, while rare items are penalized disproportionately, suggesting a potential inversion in my correction logic.
### Recorded reasoning summary
Understanding Correction Rationale
I'm realizing that inverse propensity weighting for in-batch negatives is crucial to avoid learning lift instead of true probability. My prior correction logic was inverted, making popular items immune to negative penalties and penalizing rare items excessively. The goal is to learn $P(\text{user likes item } i)$, not its lift over popularity.
### Recorded reasoning summary
Adjusting Popularity Correction
I'm realizing my previous inverse propensity weighting for in-batch negatives was inverted, severely downweighting popular items. By setting `inbatch_correction_power` to 0.0, I'm effectively disabling this correction, allowing popular items to receive their appropriate negative penalty.
### Recorded reasoning summary
Revising Sampling Strategy
I'm now examining the precise popularity distribution of the entire catalog, as items are pre-sorted by rank. This offers an opportunity to explore mixed negative sampling techniques, combining uniform and popularity-biased negatives, and to compare InfoNCE/Sampled Softmax against BCE within `score_target_microbatches`.
### Recorded reasoning summary
Analyzing Cross-Entropy Implementation
I'm analyzing the `F.cross_entropy` function's application, noting that the positive item is implicitly treated as the target class at index 0. I'm examining how temperature scaling and selected weights then influence the final summed loss for optimization.
### Recorded reasoning summary
Analyzing Gradient Impact
I'm examining how the scores of negative items influence gradient updates. High-scoring negatives receive substantial downward pushes, while low-scoring ones are largely ignored, meaning only "hard" negatives drive learning. Pair accuracy calculation remains consistent with previous methods.
### Recorded reasoning summary
Adjusting Logit Penalties
I'm realizing that subtracting $\log q$ from logits, as suggested by research, penalizes popular items more during training. This forces the model to learn exceptional affinity for these items. Interestingly, this correction is removed at serving time, eliminating popularity bias then.
**Evaluating Loss Functions**
I'm questioning if the evaluator's validation loss, specifically `evaluate_validation_bce`, is only used for early stopping. This would mean its specific implementation might not directly impact the final model's learned behavior beyond convergence criteria.
### run_shell_command · 2026-09-29T02:18:45.976Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --output /app/solution/baseline_cached.parquet",
"description": "Test runtime with cached prepared data"
}
### Tool output · 2026-09-29T02:23:57.125Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=195.6s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.6 validation_seconds=15.1
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/baseline_cached.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 306.783,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "organic-strong"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5398380471836763,
"positive_logit": 0.7427799628950514,
"negative_logit": -0.6222247051825398,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 208.59694292999984,
"validation_loss": 0.49663621881443953,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.099420767000083
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497606277466,
"item_norm_mean": 1.7570279836654663,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1883
## Step 52 · agent · 2026-09-29T02:24:28.764Z
### Recorded reasoning summary
Evaluating Training Strategies
My focus has shifted to optimizing training time. I'm now assessing whether using a subset or the full dataset is more feasible within our remaining 45 minutes, considering each full run takes approximately five minutes.
### Recorded reasoning summary
Investigating Negatives
I'm now delving into the specifics of negative sampling and loss functions. My current thoughts are centered on understanding the implications of `--target-weighting discovery-graded` and how negative sampling strategies impact performance.
### Recorded reasoning summary
Exploring Positives' Impact
I'm now examining how positive training examples influence the model. Specifically, I'm analyzing the creation of `uniform` and `inbatch` negatives to understand their role in the overall training process and optimize their generation.
### Recorded reasoning summary
Refining In-Batch Negatives
I've identified a crucial detail regarding `inbatch` negative sampling. It seems I was incorrectly using `torch.unique(targets)` instead of drawing from the actual targets present in the batch, excluding the user's own. This distinction is critical for accurate popularity-based negative sampling.
### Recorded reasoning summary
Correcting In-Batch Sampling
I've realized my `inbatch` negative sampling isn't popularity-based as intended. The current method assigns equal probability to all unique targets in the batch, negating popularity bias. I'm now reviewing the `item_correction` function and related logic to implement the correct approach.
### Recorded reasoning summary
Adjusting Popularity Weights
I've discovered how popularity weighting actually functions within `inbatch` negative sampling. It appears popular items receive lower correction weights (around 0.25) while rare items get much higher ones (up to 4.0), the opposite of my initial assumption. This significantly impacts how these negatives are utilized.
### Recorded reasoning summary
Revising Popularity Weights
I'm reassessing the impact of item correction weights on gradients, specifically how low weights for popular items drastically diminish their influence. This seems counterintuitive and requires a deeper understanding of the original intent behind this weighting scheme.
### Recorded reasoning summary
Challenging Weighting Logic
I'm questioning the core assumption behind weighting popular negative samples. It appears downweighting them by their inverse popularity cancels the very signal we aim to exploit—that popularity doesn't equate to user preference. This leads to popular items being incorrectly pulled towards user embeddings.
### Recorded reasoning summary
Investigating Ranking Anomalies
I'm finding that highly popular items consistently score highest for all users during inference, regardless of individual preference. I’m now proposing to modify the negative sampling in `draw_full_history_filtered_negatives` to sample directly from targets, making it popularity-proportional, and to increase the sampling of in-batch or popularity-biased negatives.
### Recorded reasoning summary
Exploring Catalog Popularity
I'm now considering sampling negatives directly from the catalog based on `positive_listens`, potentially using exponents like 0.5 or 0.75. This approach aims to select "hard negatives" by sampling items with high listen counts that the specific user hasn't interacted with.
### Recorded reasoning summary
Revising Loss Function
I'm realizing InfoNCE is better suited for top-k retrieval than BCE, especially with averaged negatives. My current focus is understanding how `--target-weighting discovery-graded` might interact with this and if it offers a better approach for optimizing rankings.
### Recorded reasoning summary
Testing Discovery Grades
I am now preparing to validate the impact of aligning target weights with discovery grades and adjusting negative sampling strategies. My plan involves testing `discovery-graded` weighting against current methods, evaluating different in-batch and popularity-based negative sampling ratios, and removing the inverted downweighting of popular negatives.
### run_shell_command · 2026-09-29T02:24:28.764Z
{
"description": "Train model with discovery-graded target weighting",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp1_discovery_graded.parquet"
}
### Tool output · 2026-09-29T02:29:51.375Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=196.4s
epoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.5 validation_seconds=15.9
restored best validation-BCE checkpoint: epoch=1 loss=0.504831
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp1_discovery_graded.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 318.235,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5421154207001792,
"positive_logit": 0.7774311408769176,
"negative_logit": -0.6407561994245686,
"pair_accuracy": 0.7602795720701684,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.5214277829998,
"validation_loss": 0.5048305738154627,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.933567852999886
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7521849870681763,
"query_norm_std": 0.8638167977333069,
"item_norm_mean": 1.8543914556503296,
"item_norm_std": 0.8459439277648926
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1908
## Step 53 · agent · 2026-09-29T02:29:56.146Z
### run_shell_command · 2026-09-29T02:29:56.146Z
{
"description": "Evaluate discovery-graded validation ranking",
"command": "python3 /task/evaluate_public.py /app/solution/exp1_discovery_graded.parquet"
}
### Tool output · 2026-09-29T02:29:59.493Z
Output: {
"model": "/app/solution/exp1_discovery_graded.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 1939
## Step 54 · agent · 2026-09-29T02:30:02.202Z
### Recorded reasoning summary
Investigating Recommendation Impact
My latest findings show a promising 10.5% gain in quality_ndcg@10 after implementing `--target-weighting discovery-graded`. I'm now analyzing the actual recommendations to assess if this improvement came at the cost of increased popularity bias.
### run_shell_command · 2026-09-29T02:30:02.202Z
{
"description": "Check recommended item frequency",
"command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/exp1_discovery_graded.parquet')\nprint('Top 10 recommended items frequency:')\nprint(df[df['rank'] <= 10].item_id.value_counts().head(10))\nprint('Unique items in top 10:', df[df['rank'] <= 10].item_id.nunique())\n\""
}
### Tool output · 2026-09-29T02:30:03.772Z
Output: Top 10 recommended items frequency:
item_id
8722394 2676
5436843 2651
6006390 2590
8743390 2280
8213481 2245
6896222 2056
3542184 2046
5862961 1996
2651314 1909
2185191 1808
Name: count, dtype: int64
Unique items in top 10: 632
Process Group PGID: 1959
## Step 55 · agent · 2026-09-29T02:30:22.738Z
### Recorded reasoning summary
Addressing Popularity Bias
My current focus is on eradicating the persistent popularity bias, as the same few items continue to dominate recommendations across a vast user base. My next step involves a thorough investigation into the negative sampling strategies and the associated loss objective function.
### Recorded reasoning summary
Investigating Negative Sampling
I'm exploring how sampling negative items might be contributing to popularity bias. Specifically, I'm considering whether sampling directly from the batch's positive targets, which are inherently skewed towards popular items, exacerbates the problem, and I'm examining the implications of incorporating overall catalog popularity into this process.
### Recorded reasoning summary
Exploring Catalog Popularity Sampling
I'm now considering sampling negatives proportionally to catalog popularity, potentially using $P(i) \propto (\text{positive\_listens}_i)^\alpha$ with $\alpha$ between 0.5 and 1.0. This approach could ensure popular items are evaluated against all users, driving more accurate recommendations by penalizing non-interactions with top tracks.
### Recorded reasoning summary
Revising Negative Sampling
I'm now scrutinizing the loss function's impact, specifically questioning the downweighting of popular negative items and the division by 32. My next steps involve testing modifications like setting weights to 1.0 and exploring alternative loss formulations such as InfoNCE.
### Recorded reasoning summary
Refining Loss Formulation
I'm now diving into the specifics of InfoNCE loss, particularly the crucial temperature parameter ($\tau$). The baseline's small logit difference suggests saturation with BCE, and I'm exploring how $\tau=1.0$ might behave in this new loss context.
### Recorded reasoning summary
Adjusting Temperature Parameter
I'm now focusing on the temperature parameter ($\tau$) within the InfoNCE loss. Exploring values like $0.1$ or $0.2$ seems crucial for managing the softmax distribution and potentially overcoming the saturation observed with BCE loss, especially given typical values in similar models.
### Recorded reasoning summary
Testing Negative Sampling Variations
I'm now planning several experiments to isolate the impact of negative sampling strategies. This includes varying the proportion of in-batch versus uniformly sampled negatives, exploring direct sampling from `targets`, and testing the effect of setting `inbatch_correction_power` to zero to equalize weights.
### read_file · 2026-09-29T02:30:22.738Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 630,
"end_line": 720
}
### Tool output · 2026-09-29T02:30:22.746Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 630-720 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 721.
--- FILE CONTENT (truncated) ---
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
## Step 56 · agent · 2026-09-29T02:31:00.212Z
### Recorded reasoning summary
Refining negative sampling
I'm currently examining lines 645-667, specifically how unique targets are identified and the subsequent correction of negative sample counts. This adjustment appears crucial for maintaining a balanced training set.
### Recorded reasoning summary
Optimizing sampling logic
I'm re-evaluating the efficiency of sampling, particularly concerning large matrices. My focus is now on directly sampling from `targets_cpu` rather than generating and then selecting from a massive `priorities` tensor.
### Recorded reasoning summary
Exploring catalog popularity
I'm now considering supplementing in-batch negative sampling by drawing from overall catalog popularity. This involves precomputing a multinomial distribution based on listen counts, potentially weighted by $\alpha$, to introduce diverse negatives even when popular items aren't present in the current batch. This approach offers significant speedups and flexibility.
### Recorded reasoning summary
Integrating catalog popularity
I'm now exploring how to incorporate catalog popularity into negative sampling. The key insight is that in-batch negatives already reflect empirical data popularity. I'm investigating whether adding catalog popularity on top is beneficial or redundant.
### Recorded reasoning summary
Analyzing correction weighting
I'm scrutinizing the `corrected_negative_loss` function. I've realized the denominator `weights.sum(dim=1)` simplifies to the in-batch negative count when the correction is uniform. This implies that when `inbatch_correction_power` is zero, the term becomes a simple mean of raw logits.
**Investigating loss calculation**
I'm now questioning the calculation of the negative loss term. Specifically, I'm examining the division by `weights.sum(dim=1)`. If `weights` are uniform, this results in the mean of the raw logits for the selected in-batch negatives.
**Probing loss formulation**
I'm deeply analyzing the negative loss formulation. My current focus is on the impact of `weights.sum(dim=1)` in the denominator. It appears that when weights are uniform, this simplifies to the count of in-batch negatives, leading to an average rather than a sum.
**Evaluating loss averaging**
I'm now considering the implications of averaging the negative loss. The current implementation, with `weights.sum(dim=1)` in the denominator, suggests an average is being calculated when weights are uniform, not a sum.
**Testing alternative loss metrics**
I'm actively exploring alternative loss metrics, specifically InfoNCE and Sampled Softmax, as potential replacements or improvements to the current negative sampling strategy.
### Recorded reasoning summary
Testing InfoNCE Loss
I'm now investigating the potential integration of the InfoNCE loss function. My current focus is on how this would affect the gradient calculations within `score_target_microbatches` and whether it offers a viable alternative to the existing BCE-based approach.
### Recorded reasoning summary
Confirming gradient flow
I've confirmed that `query_vectors` and `all_logits` are differentiable via `F.cross_entropy`. Item vectors are also differentiable. Statistics aggregation appears to be correctly implemented using `.detach().cpu()`. I'm investigating the implications of `backward=False` in `evaluate_validation_bce` when using InfoNCE.
### Recorded reasoning summary
Adjusting InfoNCE Correction
I'm now reconsidering how sampling bias correction impacts InfoNCE. Subtracting $\log Q(i)$ effectively boosts logits for rarer items more than popular ones, potentially forcing the model to produce higher raw scores for less frequent items.
### Recorded reasoning summary
Revising correction logic
I'm revisiting the impact of $\log Q(i)$ subtraction for InfoNCE. It seems my previous reasoning was inverted; this correction actually boosts logits for less frequent items, potentially requiring higher raw scores for them, which needs careful verification against retrieval logic.
### Recorded reasoning summary
Analyzing InfoNCE bias
I'm scrutinizing how popular items impact InfoNCE. The current understanding is that even with popular negatives present, the model is *forced* to score the positive item higher than these negatives, not just assign high scores universally, which is a critical distinction I need to test.
### Recorded reasoning summary
Revising InfoNCE Bias
I'm now deeply examining how the $\log Q(i)$ bias correction affects InfoNCE. My current hypothesis is that this correction actually elevates scores for less frequent items, a detail I need to rigorously validate against retrieval principles.
### Recorded reasoning summary
Refining sampling approach
I'm now directly sampling in-batch negatives from `targets_cpu` for improved efficiency, noting its elegant simplicity. I've also identified a critical edge case where a sampled negative could be the positive target itself, and confirmed that subsequent filtering logic correctly handles these invalid samples.
### Recorded reasoning summary
Sampling efficiently
I'm now directly sampling in-batch negatives from `targets_cpu`, bypassing inefficient matrix operations. I've also confirmed that the logic correctly filters out invalid negatives, such as the target itself or duplicates, and efficiently replaces them. This approach runs exceptionally fast, achieving sub-millisecond execution times.
### Recorded reasoning summary
Testing InfoNCE Implementation
I'm now focused on the InfoNCE loss implementation, specifically how it interacts with temperature scaling and the `F.cross_entropy` function. I've also confirmed that the `sample_weights` are correctly applied to the calculated `sample_loss` before summing.
### Recorded reasoning summary
Analyzing InfoNCE Dominance
I'm delving into why InfoNCE excels in recommendation retrieval compared to BCE. It appears InfoNCE naturally optimizes for the relative ordering of scores, crucial for top-k ranking, whereas BCE focuses on independent probabilities and doesn't inherently enforce score margins.
### Recorded reasoning summary
Testing InfoNCE parameters
I'm now exploring optimal parameters for InfoNCE, specifically temperature and the number of in-batch versus uniform negatives. The goal is to leverage InfoNCE's strength in relative score ordering, and I'm investigating how different negative sampling configurations impact its performance and stability.
### Recorded reasoning summary
Evaluating sampling distribution
I am reassessing the distribution of negatives. I now realize that `targets_cpu` inherently reflects empirical popularity due to user listening habits. Therefore, sampling from `targets_cpu` is effectively empirical popularity sampling. I am now questioning the necessity of additional popularity-based negatives.
### update_topic · 2026-09-29T02:31:00.212Z
{
"summary": "In our diagnosis, we identified three fatal flaws in the baseline: 1) in-batch negatives were flattened via torch.unique destroying popularity frequency; 2) item_correction multiplied the negative BCE loss of popular items by 0.25 while scaling rare items to 4.0, making popular tracks immune to negative feedback; 3) BCE with averaged negative loss failed to penalize hard negatives, causing extreme popularity bias. We also verified that discovery-graded target weighting gave an initial boost to 0.0235. Now we will implement true in-batch sampling, unweighted negatives, and InfoNCE loss in train_current_model.py.",
"strategic_intent": "Implement true in-batch negative sampling, remove negative downweighting, add InfoNCE loss, and validate retrieval quality.",
"title": "Implementing Negative Sampling and InfoNCE Improvements"
}
### Tool output · 2026-09-29T02:31:00.216Z
Current topic: "Implementing Negative Sampling and InfoNCE Improvements"
Topic summary: In our diagnosis, we identified three fatal flaws in the baseline: 1) in-batch negatives were flattened via torch.unique destroying popularity frequency; 2) item_correction multiplied the negative BCE loss of popular items by 0.25 while scaling rare items to 4.0, making popular tracks immune to negative feedback; 3) BCE with averaged negative loss failed to penalize hard negatives, causing extreme popularity bias. We also verified that discovery-graded target weighting gave an initial boost to 0.0235. Now we will implement true in-batch sampling, unweighted negatives, and InfoNCE loss in train_current_model.py.
Strategic Intent: Implement true in-batch negative sampling, remove negative downweighting, add InfoNCE loss, and validate retrieval quality.
## Step 57 · agent · 2026-09-29T02:31:19.010Z
### Recorded reasoning summary
Investigating Training Configuration
I'm currently examining the `Config` class within `train_current_model.py`. My focus is on understanding the available fields, particularly `bptt_steps` and `user_batch_size`, to better grasp the training setup.
### Recorded reasoning summary
Analyzing Model Parameters
I'm now reviewing the specific numerical parameters within the configuration, such as `embedding_dim`, `hidden_dim`, and various `_hidden` sizes. I'm also noting the values for `epochs`, `learning_rate`, and gradient-related settings.
### Recorded reasoning summary
Configuring Loss Parameters
I'm now extending the `Config` class with `loss_type` and `temperature` fields. I'll then integrate these, along with `uniform_negatives`, `inbatch_negatives`, and `inbatch_correction_power`, as configurable CLI flags within `argparse`. I'm also cross-referencing existing default values and their implications.
### Recorded reasoning summary
Exploring Negative Sampling
I am currently investigating the `draw_full_history_filtered_negatives` function. My focus is on understanding its implementation and how it interacts with configurable flags to generate negative samples for training, specifically examining lines 630-715.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm now focused on refining the `inbatch_negatives` logic. Specifically, I'm analyzing how `corrected_count` is determined and how `torch.topk` is used to select negative samples when `inbatch_negatives` is enabled, ensuring proper generation of both corrected and filler negatives.
### Recorded reasoning summary
Adjusting Negative Sampling
I'm now re-evaluating the `inbatch` negative sampling when the `batch_size` is 1. I'm considering how to handle `targets_cpu` with only a single element to avoid issues with `torch.randint` when generating random indices for negative samples.
### Recorded reasoning summary
Validating Correction Logic
I'm confirming that picking index 0, the target itself, gets correctly flagged as invalid and replaced with a uniform random item. I'm also verifying the behavior when `inbatch_correction_power` is 0.0 or greater, ensuring the `item_correction` logic handles all cases.
### Recorded reasoning summary
Verifying Loss Computation
I am now scrutinizing the `loss_sum` calculation, ensuring its numerical stability and verifying the gradient propagation. I'm also checking the aggregation of statistics for positive, negative, and overall loss, as well as the pair correctness metric.
### Recorded reasoning summary
Calculating Loss Values
I am now verifying the loss calculations based on the provided code snippets. I'm specifically examining the `infonce` loss computation using `F.cross_entropy` and the alternative loss calculation involving `F.softplus` and `corrected_negative_loss`, ensuring the correct application of weights and scaling.
### Recorded reasoning summary
Reviewing Two-Tower Model
I'm now examining the `two_tower_model.py` file. My focus is on identifying any dependencies or definitions it provides that are utilized by the current training logic, particularly concerning negative sampling strategies beyond the default.
### read_file · 2026-09-29T02:31:19.010Z
{
"start_line": 40,
"file_path": "/app/solution/train_current_model.py",
"end_line": 75
}
### Tool output · 2026-09-29T02:31:19.017Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 40-75 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 76.
--- FILE CONTENT (truncated) ---
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
## Step 58 · agent · 2026-09-29T02:31:21.874Z
### Recorded reasoning summary
Exploring Configuration Options
I'm currently examining the `Config` section, specifically looking at `loss_type` and `temperature`, and comparing them against default settings for `target_weighting`, `uniform_negatives`, `inbatch_negatives`, and `inbatch_correction_power`.
### read_file · 2026-09-29T02:31:21.874Z
{
"end_line": 680,
"start_line": 630,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:31:21.881Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 630-680 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 681.
--- FILE CONTENT (truncated) ---
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
## Step 59 · agent · 2026-09-29T02:31:27.860Z
### Recorded reasoning summary
Refining Negative Sampling
I'm currently focused on optimizing the in-batch negative sampling strategy within `draw_full_history_filtered_negatives`. My aim is to efficiently generate `batch_size` random indices for each of the `config.inbatch_negatives` samples, ensuring we leverage the full batch for contrastive learning.
### Recorded reasoning summary
Improving Negative Sampling Logic
I'm reviewing the generation of in-batch negatives. It appears `targets_cpu` should indeed be indexed by randomly generated indices to form `inbatch`, which then gets concatenated with uniform negatives. I'm checking if `correction` is applied correctly to these in-batch negatives based on `item_correction`.
### read_file · 2026-09-29T02:31:27.860Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1195,
"start_line": 1160
}
### Tool output · 2026-09-29T02:31:27.866Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1160-1195 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1196.
--- FILE CONTENT (truncated) ---
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
## Step 60 · agent · 2026-09-29T02:31:33.038Z
### Recorded reasoning summary
Refining Logit Calculation
I'm currently examining the `score_target_microbatches` function, specifically how the positive and negative logits are being computed. My focus is on the `torch.einsum` operation for the negative logits to ensure its efficiency and correctness in handling the batch and dimension interactions.
### Recorded reasoning summary
Analyzing Loss Calculations
I've been digging into the `infonce` and other `loss_type` calculations within `score_target_microbatches`. My current focus is on ensuring the `torch.einsum` for negative logits and the subsequent `F.cross_entropy` and `F.softplus` operations are correctly implemented and efficient. I'll now transition to reviewing `argparse` in `main()` to confirm our configuration parameters are being handled as expected.
### read_file · 2026-09-29T02:31:33.038Z
{
"start_line": 1940,
"end_line": 2010,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:31:33.045Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1940-2010 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2011.
--- FILE CONTENT (truncated) ---
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
## Step 61 · agent · 2026-09-29T02:31:44.830Z
### Recorded reasoning summary
Implementing Argument Addition
I'm currently focused on integrating new parser arguments into the `main()` function. My latest work involves adding the `--uniform-negatives` argument, specifying its type as `int` and setting its default value.
### Recorded reasoning summary
Adding More Arguments
I've now incorporated `--inbatch-negatives`, `--loss-type`, `--temperature`, and `--inbatch-correction-power` into the parser. Additionally, I've implemented validation logic within `main()` for these and other related arguments, ensuring the `Config` object is populated correctly. I'm also reviewing how checkpoint resumption handles configuration comparison.
### Recorded reasoning summary
Verifying Argument Impact
I'm reviewing how my recently added parser arguments, specifically those related to negative sampling and model architecture, actually influence the `train_model` function. My current focus is on ensuring that changes in `architecture_fields` correctly update the `TowerConfig` object when `model_config` is called.
### Recorded reasoning summary
Updating Sampling Logic
I'm ensuring consistency by updating the in-batch sampling logic in `two_tower_model.py` to align with recent additions. I'll then apply this change using the `replace` tool across relevant files, carefully avoiding duplicate edits to any single file as per the tool's rules.
### replace · 2026-09-29T02:31:44.830Z
{
"old_string": " uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"allow_multiple": false,
"new_string": " uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count and batch_size > 0:\n rand_indices = torch.randint(\n batch_size,\n (batch_size, inbatch_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = targets.detach().cpu()[rand_indices]\n corrected_count = inbatch_count",
"file_path": "/app/solution/two_tower_model.py",
"instruction": "Update inbatch negative sampling in draw_filtered_mixed_negatives to sample directly from batch targets instead of uniform sampling from unique targets"
}
### Tool error · 2026-09-29T02:31:47.110Z
EACCES: permission denied, open '/app/solution/two_tower_model.py'
## Step 62 · agent · 2026-09-29T02:31:50.264Z
### Recorded reasoning summary
Investigating Permissions
It appears I'm encountering a permission issue with `two_tower_model.py`. I need to investigate the file's current permissions to understand why access is being denied, especially considering the recent copy operation from `/task/current_model/`.
### run_shell_command · 2026-09-29T02:31:50.264Z
{
"command": "ls -la /app/solution/",
"description": "Check permissions in /app/solution"
}
### Tool output · 2026-09-29T02:31:50.281Z
Output: total 526448
drwxr-xr-x 1 agent agent 4096 Sep 29 02:29 .
drwxr-xr-x 1 agent agent 4096 Sep 29 02:01 ..
-rw-r--r-- 1 agent agent 1244227 Sep 29 02:23 baseline_cached.parquet
-rw-r--r-- 1 agent agent 1249069 Sep 29 02:29 exp1_discovery_graded.parquet
-rw-r--r-- 1 agent agent 1244230 Sep 29 02:11 production_validation.parquet
-r--r--r-- 1 agent agent 80058 Sep 29 02:05 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 02:05 two_tower_model.py
-rw-r--r-- 1 agent agent 535225808 Sep 29 02:13 validation_cache.npz
Process Group PGID: 1978
## Step 63 · agent · 2026-09-29T02:31:53.994Z
I will add write permissions to `train_current_model.py` and `two_tower_model.py` in `/app/solution/` so they can be modified.
### run_shell_command · 2026-09-29T02:31:53.994Z
{
"description": "Add write permissions to python files in /app/solution",
"command": "chmod +w /app/solution/*.py"
}
### Tool output · 2026-09-29T02:31:54.009Z
Output: (empty)
Process Group PGID: 1981
## Step 64 · agent · 2026-09-29T02:31:58.703Z
### replace · 2026-09-29T02:31:58.703Z
{
"old_string": " uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"allow_multiple": false,
"instruction": "Update inbatch negative sampling in draw_filtered_mixed_negatives to sample directly from batch targets instead of uniform sampling from unique targets",
"file_path": "/app/solution/two_tower_model.py",
"new_string": " uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count and batch_size > 0:\n rand_indices = torch.randint(\n batch_size,\n (batch_size, inbatch_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = targets.detach().cpu()[rand_indices]\n corrected_count = inbatch_count"
}
### Tool output · 2026-09-29T02:31:58.720Z
Successfully modified file: /app/solution/two_tower_model.py (1 replacements). Here is the updated code:
...
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count and batch_size > 0:
rand_indices = torch.randint(
batch_size,
(batch_size, inbatch_count),
generator=generator,
device="cpu",
)
inbatch = targets.detach().cpu()[rand_indices]
corrected_count = inbatch_count
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
...
## Step 65 · agent · 2026-09-29T02:32:13.796Z
### Recorded reasoning summary
Refining two_tower_model.py
I've just updated the `two_tower_model.py` file. My next step is to clearly outline the changes made and detail my verification plan or the subsequent actions I intend to take.
### Recorded reasoning summary
Updating train_current_model.py
I've incorporated updates into `train_current_model.py`. The `Config` dataclass now includes parameters for `loss_type`, `temperature`, `target_weighting`, `uniform_negatives`, `inbatch_negatives`, and `inbatch_correction_power`. Additionally, I've adjusted the `draw_full_history_filtered_negatives` function to directly sample `inbatch` from `targets_cpu`.
### Recorded reasoning summary
Implementing infonce loss
I've implemented support for `loss_type == "infonce"` within the `_target_microbatches` function. This involves calculating scaled logits and using `F.cross_entropy` for the loss computation, leveraging `sample_weights` for weighted loss summation.
In `two_tower_model.py`, I updated `draw_filtered_mixed_negatives` so that in-batch negative candidates are sampled directly from the batch targets rather than flattened via unique items.
Next, I will update `Config`, `draw_full_history_filtered_negatives`, `score_target_microbatches`, and CLI parsing in `train_current_model.py`.
### replace · 2026-09-29T02:32:13.796Z
{
"old_string": " loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29",
"file_path": "/app/solution/train_current_model.py",
"allow_multiple": false,
"instruction": "Add loss_type and temperature to Config, update default negative sampling counts and target weighting",
"new_string": " loss_microbatch_size: int = 65_536\n uniform_negatives: int = 16\n inbatch_negatives: int = 16\n inbatch_correction_power: float = 0.0\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"discovery-graded\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n loss_type: str = \"infonce\"\n temperature: float = 0.10\n seed: int = 29"
}
### Tool output · 2026-09-29T02:32:13.812Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 16
inbatch_negatives: int = 16
inbatch_correction_power: float = 0.0
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "discovery-graded"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
loss_type: str = "infonce"
temperature: float = 0.10
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
...
## Step 66 · agent · 2026-09-29T02:32:18.333Z
### read_file · 2026-09-29T02:32:18.333Z
{
"end_line": 685,
"file_path": "/app/solution/train_current_model.py",
"start_line": 640
}
### Tool output · 2026-09-29T02:32:18.340Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 640-685 of 2167 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 686.
--- FILE CONTENT (truncated) ---
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
## Step 67 · agent · 2026-09-29T02:32:23.157Z
### replace · 2026-09-29T02:32:23.157Z
{
"allow_multiple": false,
"old_string": " uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"new_string": " uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives and batch_size > 0:\n rand_indices = torch.randint(\n batch_size,\n (batch_size, config.inbatch_negatives),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n inbatch = torch.from_numpy(targets_cpu[rand_indices])\n corrected_count = config.inbatch_negatives",
"file_path": "/app/solution/train_current_model.py",
"instruction": "Sample in-batch negatives directly from targets_cpu proportional to frequency"
}
### Tool output · 2026-09-29T02:32:23.175Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives and batch_size > 0:
rand_indices = torch.randint(
batch_size,
(batch_size, config.inbatch_negatives),
generator=generator,
device="cpu",
).numpy()
inbatch = torch.from_numpy(targets_cpu[rand_indices])
corrected_count = config.inbatch_negatives
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
...
## Step 68 · agent · 2026-09-29T02:32:26.080Z
### read_file · 2026-09-29T02:32:26.080Z
{
"end_line": 1185,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1145
}
### Tool output · 2026-09-29T02:32:26.087Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1145-1185 of 2149 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1186.
--- FILE CONTENT (truncated) ---
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
## Step 69 · agent · 2026-09-29T02:32:30.302Z
### replace · 2026-09-29T02:32:30.302Z
{
"new_string": " selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_type == \"infonce\":\n all_logits = torch.cat(\n [positive_logits.unsqueeze(1), negative_logits], dim=1\n )\n scaled_logits = all_logits / config.temperature\n target_zeros = torch.zeros(\n len(all_logits), dtype=torch.long, device=device\n )\n row_losses = F.cross_entropy(\n scaled_logits, target_zeros, reduction=\"none\"\n )\n loss_sum = (selected_weights * row_losses).sum()\n else:\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"instruction": "Support InfoNCE loss in score_target_microbatches",
"allow_multiple": false,
"old_string": " positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:32:30.319Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
if config.loss_type == "infonce":
all_logits = torch.cat(
[positive_logits.unsqueeze(1), negative_logits], dim=1
)
scaled_logits = all_logits / config.temperature
target_zeros = torch.zeros(
len(all_logits), dtype=torch.long, device=device
)
row_losses = F.cross_entropy(
scaled_logits, target_zeros, reduction="none"
)
loss_sum = (selected_weights * row_losses).sum()
else:
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
...
## Step 70 · agent · 2026-09-29T02:32:32.952Z
### read_file · 2026-09-29T02:32:32.952Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1920,
"end_line": 2040
}
### Tool output · 2026-09-29T02:32:32.958Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1920-2040 of 2162 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2041.
--- FILE CONTENT (truncated) ---
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
## Step 71 · agent · 2026-09-29T02:32:36.650Z
### read_file · 2026-09-29T02:32:36.650Z
{
"end_line": 2070,
"file_path": "/app/solution/train_current_model.py",
"start_line": 2035
}
### Tool output · 2026-09-29T02:32:36.656Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2035-2070 of 2162 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2071.
--- FILE CONTENT (truncated) ---
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
if device.type == "mps" and not torch.backends.mps.is_available():
parser.error("MPS was requested but is unavailable")
started = time.monotonic()
prepared, eligibility = load_or_prepare(
public,
args.stage,
config,
args.prepared_cache.resolve() if args.prepared_cache else None,
)
target_partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
if args.prepare_only:
print(
json.dumps(
{
"stage": args.stage,
"sequence_users": len(prepared.user_ids),
"sequence_tokens": len(prepared.sequence_items),
## Step 72 · agent · 2026-09-29T02:32:47.528Z
### replace · 2026-09-29T02:32:47.528Z
{
"new_string": " parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n parser.add_argument(\n \"--uniform-negatives\",\n type=int,\n default=Config.uniform_negatives,\n )\n parser.add_argument(\n \"--inbatch-negatives\",\n type=int,\n default=Config.inbatch_negatives,\n )\n parser.add_argument(\n \"--loss-type\",\n choices=(\"infonce\", \"bce\"),\n default=Config.loss_type,\n )\n parser.add_argument(\n \"--temperature\",\n type=float,\n default=Config.temperature,\n )\n parser.add_argument(\n \"--inbatch-correction-power\",\n type=float,\n default=Config.inbatch_correction_power,\n )\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n or args.temperature <= 0\n or args.uniform_negatives < 0\n or args.inbatch_negatives < 0\n ):\n parser.error(\"training counts, learning rate, temperature, and patience are invalid\")\n if args.uniform_negatives + args.inbatch_negatives < 1:\n parser.error(\"must have at least one negative sample\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n inbatch_correction_power=args.inbatch_correction_power,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n loss_type=args.loss_type,\n temperature=args.temperature,\n seed=args.seed,\n )",
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )",
"instruction": "Add loss-type, temperature, inbatch-negatives, uniform-negatives, and inbatch-correction-power CLI arguments"
}
### Tool output · 2026-09-29T02:32:47.547Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
parser.add_argument(
"--uniform-negatives",
type=int,
default=Config.uniform_negatives,
)
parser.add_argument(
"--inbatch-negatives",
type=int,
default=Config.inbatch_negatives,
)
parser.add_argument(
"--loss-type",
choices=("infonce", "bce"),
default=Config.loss_type,
)
parser.add_argument(
"--temperature",
type=float,
default=Config.temperature,
)
parser.add_argument(
"--inbatch-correction-power",
type=float,
default=Config.inbatch_correction_power,
)
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
...
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
or args.temperature <= 0
or args.uniform_negatives < 0
or args.inbatch_negatives < 0
):
parser.error("training counts, learning rate, temperature, and patience are invalid")
if args.uniform_negatives + args.inbatch_negatives < 1:
parser.error("must have at least one negative sample")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
...
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
uniform_negatives=args.uniform_negatives,
inbatch_negatives=args.inbatch_negatives,
inbatch_correction_power=args.inbatch_correction_power,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
...
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
loss_type=args.loss_type,
temperature=args.temperature,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
...
## Step 73 · agent · 2026-09-29T02:32:52.837Z
### run_shell_command · 2026-09-29T02:32:52.837Z
{
"command": "python3 -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py",
"description": "Check syntax of modified files"
}
### Tool output · 2026-09-29T02:32:52.988Z
Output: (empty)
Process Group PGID: 1984
## Step 74 · agent · 2026-09-29T02:32:57.820Z
### Recorded reasoning summary
Testing Validation Parameters
I'm currently running a validation test using `--prepared-cache /app/solution/validation_cache.npz`, `--loss-type infonce`, `--temperature 0.1`, `--inbatch-negatives 16`, `--uniform-negatives 16`, and `--target-weighting discovery-graded`. This should confirm proper syntax and initial performance.
### run_shell_command · 2026-09-29T02:32:57.820Z
{
"description": "Train model with InfoNCE loss (tau=0.1) and balanced mixed negatives",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --loss-type infonce \\\n --temperature 0.1 \\\n --inbatch-negatives 16 \\\n --uniform-negatives 16 \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp2_infonce_temp01.parquet"
}
### Tool output · 2026-09-29T02:37:45.403Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=2.870373 running_bce=3.030580 elapsed=168.5s
epoch=1/1 train_loss=3.01451 validation_loss=2.72296 pair_accuracy=0.749 train_targets=1,052,953 validation_targets=130,103 train_seconds=179.7 validation_seconds=12.9
restored best validation-BCE checkpoint: epoch=1 loss=2.722959
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp2_infonce_temp01.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 283.192,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 16,
"inbatch_negatives": 16,
"inbatch_correction_power": 0.0,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"loss_type": "infonce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 3.01451297931506,
"positive_logit": 0.030721335030194117,
"negative_logit": -0.0895066697864918,
"pair_accuracy": 0.7490015283208272,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 179.73861006900006,
"validation_loss": 2.722958876426524,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 12.901669591999962
}
],
"embedding_diagnostics": {
"query_norm_mean": 0.662067174911499,
"query_norm_std": 0.17041468620300293,
"item_norm_mean": 0.8260043859481812,
"item_norm_std": 0.1920192688703537
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1987
## Step 75 · agent · 2026-09-29T02:37:51.014Z
### run_shell_command · 2026-09-29T02:37:51.014Z
{
"description": "Evaluate InfoNCE ranking on validation public backtest",
"command": "python3 /task/evaluate_public.py /app/solution/exp2_infonce_temp01.parquet"
}
### Tool output · 2026-09-29T02:37:54.428Z
Output: {
"model": "/app/solution/exp2_infonce_temp01.parquet",
"scores": {
"quality_ndcg@10": 0.009139,
"quality_users": 4263
}
}
Process Group PGID: 2018
## Step 76 · agent · 2026-09-29T02:38:00.571Z
### Recorded reasoning summary
Analyzing InfoNCE Output
My focus is on understanding this seemingly low InfoNCE score of 0.009139, especially given $\tau = 0.1$. I'm digging into the diagnostics to pinpoint the cause, specifically the relationship between the positive and negative logits.
### run_shell_command · 2026-09-29T02:38:00.571Z
{
"command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/exp2_infonce_temp01.parquet')\nprint('Unique items in top 10:', df[df['rank'] <= 10].item_id.nunique())\nprint('Top 10 items frequency:')\nprint(df[df['rank'] <= 10].item_id.value_counts().head(10))\n\"",
"description": "Inspect InfoNCE recommendations"
}
### Tool output · 2026-09-29T02:38:04.084Z
Output: Unique items in top 10: 1784
Top 10 items frequency:
item_id
507106 1262
1027979 723
4986107 637
2746728 632
3541844 611
8800739 605
3221901 568
8410352 568
8410563 566
2317630 560
Name: count, dtype: int64
Process Group PGID: 2038
## Step 77 · agent · 2026-09-29T02:38:08.008Z
### Recorded reasoning summary
Testing BCE Approach
I'm now focusing on Binary Cross-Entropy (BCE) to address the signal collapse observed with InfoNCE. My current thinking is to integrate BCE with the newly implemented in-batch sampling, hoping this combination will yield a more robust outcome.
### run_shell_command · 2026-09-29T02:38:08.008Z
{
"description": "Train model with BCE loss and true in-batch sampling",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --loss-type bce \\\n --inbatch-negatives 16 \\\n --uniform-negatives 16 \\\n --inbatch-correction-power 0.0 \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp3_bce_inbatch16.parquet"
}
### Tool output · 2026-09-29T02:42:55.810Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.554008 running_bce=0.591378 elapsed=168.7s
epoch=1/1 train_loss=0.58790 validation_loss=0.52305 pair_accuracy=0.737 train_targets=1,052,953 validation_targets=130,103 train_seconds=180.1 validation_seconds=13.0
restored best validation-BCE checkpoint: epoch=1 loss=0.523049
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp3_bce_inbatch16.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 283.411,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 16,
"inbatch_negatives": 16,
"inbatch_correction_power": 0.0,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"loss_type": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5878969085176191,
"positive_logit": 0.4752256236032197,
"negative_logit": -0.5672485375640002,
"pair_accuracy": 0.7371169166619973,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 180.1402166779999,
"validation_loss": 0.5230490383229901,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 12.996861134000028
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.6657280921936035,
"query_norm_std": 0.7470276951789856,
"item_norm_mean": 2.005955457687378,
"item_norm_std": 0.7420915365219116
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 2056
## Step 78 · agent · 2026-09-29T02:43:01.668Z
### run_shell_command · 2026-09-29T02:43:01.668Z
{
"description": "Evaluate exp3 ranking",
"command": "python3 /task/evaluate_public.py /app/solution/exp3_bce_inbatch16.parquet"
}
### Tool output · 2026-09-29T02:43:05.037Z
Output: {
"model": "/app/solution/exp3_bce_inbatch16.parquet",
"scores": {
"quality_ndcg@10": 0.011252,
"quality_users": 4263
}
}
Process Group PGID: 2087
## Step 79 · agent · 2026-09-29T02:43:11.271Z
### run_shell_command · 2026-09-29T02:43:11.271Z
{
"description": "Check exp3 item frequencies",
"command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/exp3_bce_inbatch16.parquet')\nprint('Unique items in top 10:', df[df['rank'] <= 10].item_id.nunique())\nprint('Top 10 items frequency:')\nprint(df[df['rank'] <= 10].item_id.value_counts().head(10))\n\""
}
### Tool output · 2026-09-29T02:43:12.863Z
Output: Unique items in top 10: 1154
Top 10 items frequency:
item_id
8722394 765
5436843 659
5927996 653
2317630 646
5252571 612
3970945 597
5615742 588
6896222 588
2746728 577
175065 565
Name: count, dtype: int64
Process Group PGID: 2107
## Step 80 · agent · 2026-09-29T02:43:31.459Z
### Recorded reasoning summary
Analyzing Recommendation Performance
My recent focus has been on evaluating the impact of different recommendation strategies. Experiment 1 showed a slight improvement in NDCG@10 and fewer top recommendations, while Experiment 2 yielded a significant drop in performance. I'm still digging into why Experiment 2 underperformed so drastically.
### Recorded reasoning summary
Investigating NDCG Drop
I'm currently perplexed by Experiment 3, where sampling in-batch negatives directly from targets led to a drastic NDCG@10 drop. My initial hypothesis is that this popular item sampling suppressed the embeddings of frequently occurring items, hindering their recommendation. I need to verify this.
### Recorded reasoning summary
Revising Negative Sampling Strategy
I've uncovered a significant NDCG@10 drop to 0.0112, a stark contrast to the 0.0235 baseline. It appears my previous approach of sampling in-batch negatives without propensity correction excessively penalized moderately popular tracks, which users actually engage with. I'm revisiting the baseline's `inbatch_correction` logic.
### Recorded reasoning summary
Refining Negative Sampling
I'm scrutinizing the baseline trainer's negative sampling, particularly its use of `inbatch_negatives=8` with `unique_targets`. The parameters `inbatch_correction_power`, `min`, and `max` suggest a sophisticated correction mechanism I need to fully grasp and leverage for improved performance.
### Recorded reasoning summary
Understanding Baseline Correction
I'm dissecting the baseline's `corrected_negative_loss` function. My current focus is on how it calculates and applies corrections for in-batch negatives. The logic involving `corrected_count` and the sampling of `priorities` seems crucial to the performance I'm trying to replicate.
### Recorded reasoning summary
Decoding Correction Factors
I'm currently analyzing the `correction` matrix within the `corrected_negative_loss` function. My current focus is to understand its origin and how it's being applied to adjust the raw negative sampling. This seems to be the key to replicating the baseline's performance.
### Recorded reasoning summary
Re-evaluating Correction Logic
I'm scrutinizing the baseline's handling of invalid in-batch negatives. It appears rejected items are simply replaced with uniform random draws, and their correction factor is reset to 1.0. This might be a crucial oversight contributing to performance issues I'm observing.
### Recorded reasoning summary
Exploring Negative Sampling Options
I'm now considering other limitations in how negative examples are chosen and used for training. I'm particularly interested in whether explicit dislikes, short listens, skips, or even popularity rank play a role, and how varying the number of uniform versus in-batch negatives might affect outcomes.
### Recorded reasoning summary
Testing Negative Filter Scope
I'm investigating the impact of `context_items` within `invalid_negative_mask`. My suspicion is that it's being limited to the last 100 items, and I want to test if setting `--negative-filter-scope last-100` directly addresses this limitation.
### Recorded reasoning summary
Adjusting Target Weights
I've identified a significant improvement by using `discovery-graded` target weighting, boosting scores. I'm now exploring whether adjusting the weights for different grades, especially `organic_like_target_weight`, and potentially reducing `ordinary_target_weight`, can further enhance performance.
### run_shell_command · 2026-09-29T02:43:31.459Z
{
"description": "Diff our changes against current_model",
"command": "diff -u /task/current_model/train_current_model.py /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:43:31.477Z
Output: --- /task/current_model/train_current_model.py 2026-09-29 01:59:28.000000000 +0000
+++ /app/solution/train_current_model.py 2026-09-29 02:32:47.532785741 +0000
@@ -53,13 +53,13 @@
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
- uniform_negatives: int = 24
- inbatch_negatives: int = 8
- inbatch_correction_power: float = 0.5
+ uniform_negatives: int = 16
+ inbatch_negatives: int = 16
+ inbatch_correction_power: float = 0.0
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
- target_weighting: str = "organic-strong"
+ target_weighting: str = "discovery-graded"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
@@ -69,6 +69,8 @@
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
+ loss_type: str = "infonce"
+ temperature: float = 0.10
seed: int = 29
@@ -650,33 +652,15 @@
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
- if config.inbatch_negatives:
- unique_targets = torch.unique(targets.detach().cpu())
- corrected_count = min(
- config.inbatch_negatives, max(len(unique_targets) - 1, 0)
- )
- if corrected_count:
- priorities = torch.rand(
- (batch_size, len(unique_targets)), generator=generator
- )
- priorities.masked_fill_(
- unique_targets[None, :].eq(
- torch.from_numpy(targets_cpu)[:, None]
- ),
- -1.0,
- )
- columns = torch.topk(
- priorities, k=corrected_count, dim=1
- ).indices
- inbatch = unique_targets[columns]
- if corrected_count < config.inbatch_negatives:
- filler = torch.randint(
- n_items,
- (batch_size, config.inbatch_negatives - corrected_count),
- generator=generator,
- device="cpu",
- )
- inbatch = torch.cat([inbatch, filler], dim=1)
+ if config.inbatch_negatives and batch_size > 0:
+ rand_indices = torch.randint(
+ batch_size,
+ (batch_size, config.inbatch_negatives),
+ generator=generator,
+ device="cpu",
+ ).numpy()
+ inbatch = torch.from_numpy(targets_cpu[rand_indices])
+ corrected_count = config.inbatch_negatives
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
@@ -1168,16 +1152,29 @@
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
- positive_loss = F.softplus(-positive_logits)
- negative_loss = corrected_negative_loss(
- negative_logits, negative_weights[begin:end], config
- )
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
- loss_sum = (
- selected_weights * (positive_loss + negative_loss) / 2.0
- ).sum()
+ if config.loss_type == "infonce":
+ all_logits = torch.cat(
+ [positive_logits.unsqueeze(1), negative_logits], dim=1
+ )
+ scaled_logits = all_logits / config.temperature
+ target_zeros = torch.zeros(
+ len(all_logits), dtype=torch.long, device=device
+ )
+ row_losses = F.cross_entropy(
+ scaled_logits, target_zeros, reduction="none"
+ )
+ loss_sum = (selected_weights * row_losses).sum()
+ else:
+ positive_loss = F.softplus(-positive_logits)
+ negative_loss = corrected_negative_loss(
+ negative_logits, negative_weights[begin:end], config
+ )
+ loss_sum = (
+ selected_weights * (positive_loss + negative_loss) / 2.0
+ ).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
@@ -2002,6 +1999,31 @@
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
+ parser.add_argument(
+ "--uniform-negatives",
+ type=int,
+ default=Config.uniform_negatives,
+ )
+ parser.add_argument(
+ "--inbatch-negatives",
+ type=int,
+ default=Config.inbatch_negatives,
+ )
+ parser.add_argument(
+ "--loss-type",
+ choices=("infonce", "bce"),
+ default=Config.loss_type,
+ )
+ parser.add_argument(
+ "--temperature",
+ type=float,
+ default=Config.temperature,
+ )
+ parser.add_argument(
+ "--inbatch-correction-power",
+ type=float,
+ default=Config.inbatch_correction_power,
+ )
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
@@ -2018,8 +2040,13 @@
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
+ or args.temperature <= 0
+ or args.uniform_negatives < 0
+ or args.inbatch_negatives < 0
):
- parser.error("training counts, learning rate, and patience are invalid")
+ parser.error("training counts, learning rate, temperature, and patience are invalid")
+ if args.uniform_negatives + args.inbatch_negatives < 1:
+ parser.error("must have at least one negative sample")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
@@ -2031,6 +2058,9 @@
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
+ uniform_negatives=args.uniform_negatives,
+ inbatch_negatives=args.inbatch_negatives,
+ inbatch_correction_power=args.inbatch_correction_power,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
@@ -2042,6 +2072,8 @@
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
+ loss_type=args.loss_type,
+ temperature=args.temperature,
seed=args.seed,
)
torch.set_num_threads(args.threads)
Exit Code: 1
Process Group PGID: 2126
## Step 81 · agent · 2026-09-29T02:44:06.406Z
### Recorded reasoning summary
Analyzing draw_full_history_filtered_negatives
My focus has been on understanding the surprisingly low NDCG (0.011252) observed in Experiment 3 of `draw_full_history_filtered_negatives` with `inbatch_negatives = 16` and `uniform_negatives = 16`, specifically questioning the impact of `inbatch_correction_power = 0.0`.
### Recorded reasoning summary
Investigating In-Batch Correction Power
I'm analyzing the low NDCG in Experiment 3 and questioning the `inbatch_correction_power = 0.0` setting, especially given the high `inbatch_negatives`. I plan to re-examine the baseline's `inbatch_correction_power = 0.5` with `unique_targets` and explore variations in `inbatch_negatives` and `uniform_negatives`, including setting `inbatch_negatives = 0`.
### Recorded reasoning summary
Revisiting Baseline Scores
I'm re-evaluating Experiment 3's low NDCG, noting the baseline's original score was higher. I'll restore the baseline's `draw_full_history_filtered_negatives` and test parameter variations.
### Recorded reasoning summary
Exploring In-Batch Negative Sampling
I'm diving deeper into the `draw_full_history_filtered_negatives` function, focusing on how in-batch negatives are sampled. I'm particularly interested in the `corrected_count` logic and how it interacts with `unique_targets` to potentially influence sampling.
### Recorded reasoning summary
Testing In-Batch Negative Logic
I'm examining the code for `draw_full_history_filtered_negatives`. My focus is on the section that determines `inbatch` negatives, particularly how `unique_targets` and `corrected_count` are used to select and fill the negative samples.
### Recorded reasoning summary
Refining Negative Sampling Loop
I'm optimizing the loop for `invalid_full_history_negatives`. The current approach retries up to 32 times, replacing invalid negatives with uniform samples and adjusting their `correction` value. I'm assessing if further refinement or adjustments to this resampling process are necessary.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm closely examining the code, specifically lines 698-701, where rejected in-batch items are replaced with uniform draws and their correction value is set to 1.0. I am also reviewing `build_known_positive_filter` and lines 578-605 of `train_current_model.py` for context.
### Recorded reasoning summary
Reviewing Filter Construction
I'm scrutinizing the `build_known_positive_filter` function, particularly the `training_counts` argument. I need to understand its origin and how it directly influences the preparation of sequence items within the filter's construction process.
### Recorded reasoning summary
Considering Actual Negative Examples
I'm questioning if skipped or disliked items are truly used as negatives. Currently, only uniform and in-batch draws seem to be employed, and I need to confirm if explicit user actions like skips or dislikes are ever incorporated as negative examples in the training process.
### Recorded reasoning summary
Analyzing Negative Sampling Logic
I'm examining the training objective's approach to negative examples. I'm currently focused on the `draw_full_history_filtered_negatives` function and how `inbatch_correction_power` might impact performance, particularly when `inbatch_negatives` is high.
### Recorded reasoning summary
Analyzing In-Batch Correction
I'm examining how the in-batch correction is calculated. My focus is on the `counts`, `probabilities`, and the application of `inbatch_correction_power` to `raw` values before normalization and clipping.
### Recorded reasoning summary
Analyzing Correction Power Calculation
I'm examining the `build_item_correction` function and have traced `target_counts` back to a bincount of target items. I now understand the `raw` value is the ratio of uniform to in-batch probability, raised to the power of `inbatch_correction_power`.
### Recorded reasoning summary
Calculating Weighted Average
I've identified that the in-batch loss calculation involves a weighted average, specifically `((raw[:, selected] * weights).sum(dim=1) / weights.sum(dim=1))`. This highlights how the `correction` values directly influence the average loss for in-batch negatives.
### Recorded reasoning summary
Understanding Importance Sampling Cancellation
I've realized the in-batch loss calculation uses self-normalized importance sampling. This means the in-batch samples are weighted to estimate the uniform negative loss, effectively canceling out the in-batch sampling's intended purpose and making them approximate uniform negatives.
### Recorded reasoning summary
Correcting Loss Calculation
I've realized my previous understanding of in-batch loss calculation was flawed. The importance sampling weights are indeed used, but they don't necessarily convert in-batch negatives into uniform ones. Instead, they aim to correct for the popularity bias inherent in in-batch sampling, allowing the model to still learn from these harder, more relevant negative examples.
### Recorded reasoning summary
Analyzing Yi et al. Correction
I'm reviewing Yi et al. (2019) and their derivation of sampled softmax. The key insight is subtracting $\log p_i$ from the model logit during training. I need to verify if this implies ranking by the raw dot product at serving time, or if the correction inherently alters the objective.
### Recorded reasoning summary
Testing Parameter Isolation
I am systematically isolating the variables that led to Experiment 3's poor performance. My current focus is on reinstating the baseline's `draw_full_history_filtered_negatives` method to understand the specific impact of changing `inbatch_correction_power` and negative sampling configurations individually.
### run_shell_command · 2026-09-29T02:44:06.406Z
{
"description": "Inspect original draw_full_history_filtered_negatives",
"command": "sed -n '645,675p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T02:44:06.425Z
Output: uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
Process Group PGID: 2129
## Step 82 · agent · 2026-09-29T02:44:09.485Z
### Recorded reasoning summary
Restoring Model Logic
I'm focusing on restoring a specific line of code in `train_current_model.py`. The change involves bringing back the `torch.unique` function call that was previously used on `targets.detach().cpu()`.
### read_file · 2026-09-29T02:44:09.485Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 675,
"start_line": 645
}
### Tool output · 2026-09-29T02:44:09.494Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 645-675 of 2197 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 676.
--- FILE CONTENT (truncated) ---
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives and batch_size > 0:
rand_indices = torch.randint(
batch_size,
(batch_size, config.inbatch_negatives),
generator=generator,
device="cpu",
).numpy()
inbatch = torch.from_numpy(targets_cpu[rand_indices])
corrected_count = config.inbatch_negatives
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
## Step 83 · agent · 2026-09-29T02:44:13.952Z
### replace · 2026-09-29T02:44:13.952Z
{
"old_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives and batch_size > 0:\n rand_indices = torch.randint(\n batch_size,\n (batch_size, config.inbatch_negatives),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n inbatch = torch.from_numpy(targets_cpu[rand_indices])\n corrected_count = config.inbatch_negatives",
"file_path": "/app/solution/train_current_model.py",
"allow_multiple": false,
"new_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"instruction": "Restore unique_targets inbatch negative selection in draw_full_history_filtered_negatives"
}
### Tool output · 2026-09-29T02:44:13.970Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
...
## Step 84 · agent · 2026-09-29T02:44:23.571Z
### read_file · 2026-09-29T02:44:23.571Z
{
"file_path": "/app/solution/two_tower_model.py",
"start_line": 365,
"end_line": 400
}
### Tool output · 2026-09-29T02:44:23.578Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 365-400 of 422 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 401.
--- FILE CONTENT (truncated) ---
context_items: torch.Tensor,
liked_items: torch.Tensor,
n_items: int,
uniform_count: int,
inbatch_count: int,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw uniform and unique-target in-batch negatives."""
batch_size = len(targets)
uniform = torch.randint(
n_items,
(batch_size, uniform_count),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count and batch_size > 0:
rand_indices = torch.randint(
batch_size,
(batch_size, inbatch_count),
generator=generator,
device="cpu",
)
inbatch = targets.detach().cpu()[rand_indices]
corrected_count = inbatch_count
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
:, uniform_count : uniform_count + corrected_count
] = item_correction[inbatch[:, :corrected_count]]
negatives = negatives_cpu.to(device)
## Step 85 · agent · 2026-09-29T02:44:28.225Z
In `train_current_model.py`, I restored `unique_targets` in-batch negative sampling. Next, I will restore `unique_targets` in `two_tower_model.py`.
### replace · 2026-09-29T02:44:28.225Z
{
"new_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"allow_multiple": false,
"file_path": "/app/solution/two_tower_model.py",
"old_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count and batch_size > 0:\n rand_indices = torch.randint(\n batch_size,\n (batch_size, inbatch_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = targets.detach().cpu()[rand_indices]\n corrected_count = inbatch_count",
"instruction": "Restore unique_targets in draw_filtered_mixed_negatives"
}
### Tool output · 2026-09-29T02:44:28.237Z
Successfully modified file: /app/solution/two_tower_model.py (1 replacements). Here is the updated code:
...
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
-1.0,
)
columns = torch.topk(priorities, k=corrected_count, dim=1).indices
inbatch = unique_targets[columns]
if corrected_count < inbatch_count:
filler = torch.randint(
n_items,
(batch_size, inbatch_count - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
...
## Step 86 · agent · 2026-09-29T02:44:30.715Z
### run_shell_command · 2026-09-29T02:44:30.715Z
{
"description": "Check syntax of solution files",
"command": "python3 -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py"
}
### Tool output · 2026-09-29T02:44:30.867Z
Output: (empty)
Process Group PGID: 2132
## Step 87 · agent · 2026-09-29T02:44:37.612Z
### Recorded reasoning summary
Exploring Model Training Parameters
I'm currently investigating the impact of specific training configurations on model performance. My focus is on experiment 4, particularly the effects of `--target-weighting discovery-graded`, `--loss-type bce`, and various negative sampling strategies (`--uniform-negatives` and `--inbatch-negatives`), including the `--inbatch-correction` parameter.
### Recorded reasoning summary
Refining Objective Weighting
I'm now exploring adjustments to the `organic-like-target-weight` within the discovery objective, considering a gain ratio of 7.0 to match the DCG gain. I'm also evaluating a reduced `ordinary-target-weight` of 0.1 and varying `inbatch_correction_power`. Additionally, I'm testing different `inbatch_negatives` counts.
### run_shell_command · 2026-09-29T02:44:37.612Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --organic-like-target-weight 7.0 \\\n --loss-type bce \\\n --uniform-negatives 24 \\\n --inbatch-negatives 8 \\\n --inbatch-correction-power 0.5 \\\n --output /app/solution/exp4_graded7.parquet",
"description": "Train with organic-like-target-weight=7.0"
}
### Tool output · 2026-09-29T02:49:57.975Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.319 max=7.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.554236 running_bce=0.545155 elapsed=195.7s
epoch=1/1 train_loss=0.54317 validation_loss=0.50813 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.9 validation_seconds=15.3
restored best validation-BCE checkpoint: epoch=1 loss=0.508131
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp4_graded7.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 315.964,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 7.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"loss_type": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5431721092790005,
"positive_logit": 0.7727052889027716,
"negative_logit": -0.632630268761765,
"pair_accuracy": 0.7601297256382763,
"targets": 1052953.0,
"target_weight_sum": 360556.0,
"batches": 266.0,
"seconds": 208.870717368,
"validation_loss": 0.5081311107264754,
"validation_targets": 130103.0,
"validation_target_weight_sum": 43149.5,
"validation_seconds": 15.257218306999675
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.6113882064819336,
"query_norm_std": 0.7923927307128906,
"item_norm_mean": 1.9114322662353516,
"item_norm_std": 0.8259212970733643
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 2135
## Step 88 · agent · 2026-09-29T02:50:05.782Z
### run_shell_command · 2026-09-29T02:50:05.782Z
{
"command": "python3 /task/evaluate_public.py /app/solution/exp4_graded7.parquet",
"description": "Evaluate exp4 ranking"
}
### Tool output · 2026-09-29T02:50:09.125Z
Output: {
"model": "/app/solution/exp4_graded7.parquet",
"scores": {
"quality_ndcg@10": 0.022166,
"quality_users": 4263
}
}
Process Group PGID: 2166
## Step 89 · agent · 2026-09-29T02:50:16.597Z
### Recorded reasoning summary
Refining Organic Weight
I'm seeing that a target weight around 3.0 is showing better initial results than 7.0 for the organic targets. My current focus is on the nuances of negative sampling. For Exp 1, I'm specifically scrutinizing the impact of 24 uniform negatives, 8 in-batch negatives, and an in-batch correction power of 0.5.
### Recorded reasoning summary
Testing Negative Sampling Strategies
I'm now exploring variations in negative sampling, specifically adjusting the balance between in-batch and uniform negatives while keeping the total constant at 32. I'm also re-evaluating the `inbatch_correction_power`, especially its impact when set to 0.0, recalling a strong performance then.
### run_shell_command · 2026-09-29T02:50:16.597Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --loss-type bce \\\n --uniform-negatives 32 \\\n --inbatch-negatives 0 \\\n --output /app/solution/exp5_uniform32.parquet",
"description": "Train with only uniform negatives (32)"
}
### Tool output · 2026-09-29T02:55:19.808Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.542264 running_bce=0.527347 elapsed=180.8s
epoch=1/1 train_loss=0.52544 validation_loss=0.48448 pair_accuracy=0.798 train_targets=1,052,953 validation_targets=130,103 train_seconds=192.8 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.484480
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp5_uniform32.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 298.89,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 32,
"inbatch_negatives": 0,
"inbatch_correction_power": 0.0,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"loss_type": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5254377356205274,
"positive_logit": 0.9194933304166354,
"negative_logit": -0.8096884111770254,
"pair_accuracy": 0.7977172592223964,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 192.82824725499995,
"validation_loss": 0.4844796377623661,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.39293569900019
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.4759684801101685,
"query_norm_std": 0.6978866457939148,
"item_norm_mean": 1.9009125232696533,
"item_norm_std": 0.8285666704177856
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 2186
## Step 90 · agent · 2026-09-29T02:55:25.544Z
### run_shell_command · 2026-09-29T02:55:25.544Z
{
"description": "Evaluate exp5 ranking",
"command": "python3 /task/evaluate_public.py /app/solution/exp5_uniform32.parquet"
}
### Tool output · 2026-09-29T02:55:28.887Z
Output: {
"model": "/app/solution/exp5_uniform32.parquet",
"scores": {
"quality_ndcg@10": 0.022328,
"quality_users": 4263
}
}
Process Group PGID: 2217
## Step 91 · agent · 2026-09-29T02:55:35.927Z
### Recorded reasoning summary
Testing In-Batch Negatives
My recent experiments indicate that incorporating in-batch negatives significantly boosts performance, pushing the score from 0.0223 to 0.0235. I'm now exploring further refinements to leverage this promising observation.
### Recorded reasoning summary
Exploring Negative Uniformity
I'm now investigating the impact of increasing the total number of negatives, specifically exploring combinations like 48 or 64. The intuition is that a larger, more uniform negative pool might further enhance the retrieval model's understanding of the vast 30,000-item catalog. I'm also analyzing the runtime implications, observing that the `model.encode_items` step already processes a significant portion of the catalog.
### run_shell_command · 2026-09-29T02:55:35.927Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --loss-type bce \\\n --uniform-negatives 48 \\\n --inbatch-negatives 8 \\\n --inbatch-correction-power 0.5 \\\n --output /app/solution/exp6_uniform48_inbatch8.parquet",
"description": "Train with 48 uniform and 8 inbatch negatives"
}
### Tool output · 2026-09-29T03:01:46.985Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.546935 running_bce=0.538523 elapsed=237.4s
epoch=1/1 train_loss=0.53649 validation_loss=0.49680 pair_accuracy=0.775 train_targets=1,052,953 validation_targets=130,103 train_seconds=253.0 validation_seconds=19.7
restored best validation-BCE checkpoint: epoch=1 loss=0.496797
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp6_uniform48_inbatch8.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 363.611,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 48,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"loss_type": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5364929355689915,
"positive_logit": 0.8233693339937159,
"negative_logit": -0.6997988908465026,
"pair_accuracy": 0.7751145723367655,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 253.04984047499966,
"validation_loss": 0.4967968370128568,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 19.672006431000227
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.5436816215515137,
"query_norm_std": 0.6917167901992798,
"item_norm_mean": 1.8821020126342773,
"item_norm_std": 0.8109329342842102
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 2237
## Step 92 · agent · 2026-09-29T03:01:54.992Z
### run_shell_command · 2026-09-29T03:01:54.992Z
{
"command": "python3 /task/evaluate_public.py /app/solution/exp6_uniform48_inbatch8.parquet",
"description": "Evaluate exp6 ranking"
}
### Tool output · 2026-09-29T03:01:58.350Z
Output: {
"model": "/app/solution/exp6_uniform48_inbatch8.parquet",
"scores": {
"quality_ndcg@10": 0.024565,
"quality_users": 4263
}
}
Process Group PGID: 2268
## Step 93 · agent · 2026-09-29T03:02:09.342Z
### Recorded reasoning summary
Improving NDCG@10 Performance
My latest experiments have yielded encouraging results. Specifically, I observed a jump in NDCG@10 to 0.024565, a significant improvement over the baseline of 0.021280. This progress is largely attributed to experiment 6, which employed discovery-graded weighting with 48 uniform and 8 in-batch samples, surpassing experiment 1's 0.023522.
### Recorded reasoning summary
Exploring Hyperparameter Adjustments
I'm now investigating the impact of increasing `uniform_negatives` and tweaking `inbatch_negatives`. I'm also considering varying `inbatch_correction_power`, starting with `uniform_negatives = 64, inbatch_negatives = 8`. Given our remaining time budget, I need to prioritize running the final submission training.
### Recorded reasoning summary
Securing Submission File
I'm prioritizing generating a valid submission file immediately. This involves running `--stage submission` with current best settings to ensure `submission.parquet` is created, mitigating any timeout risks. I’ll then save this best valid submission to safeguard against deadline issues.
### Recorded reasoning summary
Validating Submission Criteria
I'm ensuring my submission adheres to strict formatting rules: integer entries, all target users included, exactly 100 distinct items and ranks per user, and only from eligible candidates. The current best settings are `--target-weighting discovery-graded`, `--uniform-negatives 48`, `--inbatch-negatives 8`, `--inbatch-correction-power 0.5`, and `--loss-type bce`. I will now proceed with the submission stage.
### read_file · 2026-09-29T03:02:09.342Z
{
"start_line": 45,
"file_path": "/app/solution/train_current_model.py",
"end_line": 75
}
### Tool output · 2026-09-29T03:02:09.354Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 45-75 of 2215 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 76.
--- FILE CONTENT (truncated) ---
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 16
inbatch_negatives: int = 16
inbatch_correction_power: float = 0.0
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "discovery-graded"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
loss_type: str = "infonce"
temperature: float = 0.10
seed: int = 29
## Step 94 · agent · 2026-09-29T03:02:19.984Z
### Recorded reasoning summary
Updating Configuration Defaults
I'm refining the default negative sampling parameters within `Config` in `train_current_model.py`. Specifically, I'm setting `uniform_negatives` to 48, `inbatch_negatives` to 8, and `inbatch_correction_power` to 0.5 to optimize our training process.
### replace · 2026-09-29T03:02:19.984Z
{
"old_string": " uniform_negatives: int = 16\n inbatch_negatives: int = 16\n inbatch_correction_power: float = 0.0\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"discovery-graded\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n loss_type: str = \"infonce\"\n temperature: float = 0.10\n seed: int = 29",
"new_string": " uniform_negatives: int = 48\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"discovery-graded\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n loss_type: str = \"bce\"\n temperature: float = 0.10\n seed: int = 29",
"allow_multiple": false,
"instruction": "Set best hyperparameters as Config defaults",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T03:02:20.001Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 48
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "discovery-graded"
ordinary_target_weight: float = 0.25
...
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
loss_type: str = "bce"
temperature: float = 0.10
seed: int = 29
@dataclass(frozen=True)
...
## Step 95 · agent · 2026-09-29T03:02:31.703Z
### Recorded reasoning summary
Verifying argparse defaults
I'm currently reviewing the `argparse` configurations within the `main()` function. My focus is on confirming that the default values assigned to `--uniform-negatives` and `--inbatch-negatives` correctly align with the `Config.uniform_negatives` and `Config.inbatch_negatives` settings, which are currently established at 48 and 8 respectively.
In `train_current_model.py`, I set the verified optimal hyperparameters as the default `Config` values (`uniform_negatives=48`, `inbatch_negatives=8`, `target_weighting=discovery-graded`, `inbatch_correction_power=0.5`, `loss_type=bce`).
Next, I will verify that running with defaults reproduces the improved backtest score.
## Recorded session usage
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