yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-r02-b9049e57
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T01:22:41.319Z
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 · 2026-09-29T01:22:41.362Z
# 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 · 2026-09-29T01:22:57.797Z
### Recorded reasoning summary
Improving Recommendation Model
I'm currently focused on enhancing our production music-discovery two-tower recommendation model. My approach prioritizes understanding the core mandates and guidelines, with a strong emphasis on operational intent and technical rationale to drive improvements.
### Recorded reasoning summary
Refining Candidate Selection
I'm now concentrating on refining how we select candidate items for users. My focus is on ensuring we strictly adhere to the provided constraints for candidate eligibility and then generating the final, ranked output file as specified.
### Recorded reasoning summary
Implementing Model Refinements
I've made concrete improvements to the training script and local modules within `/app/solution`. My documentation now includes crucial details on diagnosis, performance metrics, and submission commands. I'm ensuring all artifacts remain contained and the environment is treated as read-only, adhering strictly to operational constraints.
### update_topic · 2026-09-29T01:22:57.797Z
{
"summary": "Initiating investigation into the music recommendation two-tower model. We will inspect /task/current_model/ and /task/data/, run the baseline model training and public validation evaluation to establish the baseline NDCG@10 score, and understand the existing negative sampling and loss function.",
"strategic_intent": "Begin research by inspecting the task files, existing model architecture, and establishing the baseline validation score.",
"title": "Researching Baseline and Model Architecture"
}
### Tool output · 2026-09-29T01:22:57.809Z
Current topic: "Researching Baseline and Model Architecture"
Topic summary: Initiating investigation into the music recommendation two-tower model. We will inspect /task/current_model/ and /task/data/, run the baseline model training and public validation evaluation to establish the baseline NDCG@10 score, and understand the existing negative sampling and loss function.
Strategic Intent: Begin research by inspecting the task files, existing model architecture, and establishing the baseline validation score.
### list_directory · 2026-09-29T01:22:57.797Z
{
"dir_path": "/task"
}
### Tool error · 2026-09-29T01:22:57.809Z
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 · 2026-09-29T01:22:57.797Z
{
"dir_path": "/task/current_model"
}
### Tool error · 2026-09-29T01:22:57.809Z
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
### read_file · 2026-09-29T01:22:57.797Z
{
"file_path": "/task/evaluate_public.py",
"end_line": 100,
"start_line": 1
}
### Tool error · 2026-09-29T01:22:57.809Z
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 4 · agent · 2026-09-29T01:23:03.760Z
### Recorded reasoning summary
Exploring File Access Capabilities
I'm currently examining the boundaries of my file system access. It appears `list_directory` and `read_file` are confined to specific directories, while `run_shell_command` offers more flexibility. My next step is to test if I can leverage `run_shell_command` to access or move files from `/task` into the permitted `/app` directories.
I will copy the production model source files and evaluator from `/task/` to `/app/solution/` so that we can inspect and modify them within our workspace, and list the files in `/task/`.
### run_shell_command · 2026-09-29T01:23:03.760Z
{
"dir_path": "/app/solution",
"description": "List /task and /task/data contents, and copy current_model source and evaluate_public.py to /app/solution",
"command": "ls -la /task && ls -la /task/data && cp -r /task/current_model/* /app/solution/ && cp /task/evaluate_public.py /app/solution/ && ls -la /app/solution"
}
### Tool output · 2026-09-29T01:23:03.799Z
Output: total 40
dr-xr-xr-x 1 root root 4096 Sep 29 01:19 .
drwxr-xr-x 1 root root 4096 Sep 29 01:22 ..
-r--r--r-- 1 root root 0 Sep 29 01:17 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 data
-r--r--r-- 1 root root 5774 Sep 29 01:17 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:17 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:17 requirements.txt
total 347396
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:19 ..
-r--r--r-- 1 root root 349352 Sep 29 01:17 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:17 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:17 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:18 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:17 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:17 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:17 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:17 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:17 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:17 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:17 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:17 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:17 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:17 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:17 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:18 validation_preference_state.parquet
total 116
drwxr-xr-x 1 agent agent 4096 Sep 29 01:23 .
drwxr-xr-x 1 agent agent 4096 Sep 29 01:19 ..
-r--r--r-- 1 agent agent 5774 Sep 29 01:23 evaluate_public.py
-r--r--r-- 1 agent agent 80058 Sep 29 01:23 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 01:23 two_tower_model.py
Process Group PGID: 1612
## Step 5 · agent · 2026-09-29T01:23:06.502Z
I will copy `ranking_evaluation.py` to `/app/solution/` and check the system resources (CPU, RAM).
### run_shell_command · 2026-09-29T01:23:06.502Z
{
"description": "Copy ranking_evaluation.py and inspect CPU and RAM availability",
"command": "cp /task/ranking_evaluation.py /app/solution/ && lscpu | grep \"CPU(s):\" && free -h",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:23:06.526Z
Output: CPU(s): 8
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1619
## Step 6 · agent · 2026-09-29T01:23:08.236Z
I will check system memory using `/proc/meminfo`.
### run_shell_command · 2026-09-29T01:23:08.236Z
{
"dir_path": "/app/solution",
"description": "Check system RAM via /proc/meminfo",
"command": "cat /proc/meminfo | head -n 5"
}
### Tool output · 2026-09-29T01:23:08.250Z
Output: MemTotal: 15988964 kB
MemFree: 1416584 kB
MemAvailable: 14479128 kB
Buffers: 247236 kB
Cached: 12490644 kB
Process Group PGID: 1625
## Step 7 · agent · 2026-09-29T01:23:10.596Z
### read_file · 2026-09-29T01:23:10.596Z
{
"file_path": "/app/solution/evaluate_public.py",
"start_line": 1,
"end_line": 150
}
### Tool output · 2026-09-29T01:23:10.602Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 158 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) ---
#!/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
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["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 "
)
## Step 8 · agent · 2026-09-29T01:23:14.684Z
### Recorded reasoning summary
Refining Data Location
I'm currently re-evaluating the data directory path within `evaluate_public.py`. My thought process indicates a potential issue where, depending on the execution context, the `DATA` variable might resolve to an incorrect location, failing to access the expected data files.
### read_file · 2026-09-29T01:23:14.684Z
{
"end_line": 150,
"file_path": "/app/solution/two_tower_model.py",
"start_line": 1
}
### Tool output · 2026-09-29T01:23:14.689Z
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 9 · agent · 2026-09-29T01:23:16.722Z
### read_file · 2026-09-29T01:23:16.722Z
{
"start_line": 151,
"file_path": "/app/solution/two_tower_model.py",
"end_line": 300
}
### Tool output · 2026-09-29T01:23:16.726Z
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 10 · agent · 2026-09-29T01:23:18.315Z
### read_file · 2026-09-29T01:23:18.315Z
{
"file_path": "/app/solution/two_tower_model.py",
"end_line": 434,
"start_line": 301
}
### Tool output · 2026-09-29T01:23:18.319Z
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 11 · agent · 2026-09-29T01:23:20.958Z
### read_file · 2026-09-29T01:23:20.958Z
{
"end_line": 150,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1
}
### Tool output · 2026-09-29T01:23:20.963Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 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: 151.
--- FILE CONTENT (truncated) ---
#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.
Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss. The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split. A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
into the next block.
The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations
import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F
import two_tower_model as base
CACHE_VERSION = 2
@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
@classmethod
def read(cls, path: Path) -> "CandidateSet":
with np.load(path, allow_pickle=False) as artifact:
result = cls(
uids=artifact["uids"].astype(np.int64, copy=True),
item_ids=artifact["item_ids"].astype(np.int64, copy=True),
packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
)
expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
if result.packed.shape != expected:
raise ValueError(
f"invalid candidate artifact shape {result.packed.shape}; "
f"expected {expected}"
)
return result
@dataclass(frozen=True)
class SequenceData:
item_ids: np.ndarray
item_features: np.ndarray
user_ids: np.ndarray
offsets: np.ndarray
sequence_timestamps: np.ndarray
sequence_items: np.ndarray
event_features: np.ndarray
activity_features: np.ndarray
state_ids: np.ndarray
liked_states: np.ndarray
disliked_states: np.ndarray
state_counts: np.ndarray
inference_user_rows: np.ndarray
inference_liked_items: np.ndarray
inference_disliked_items: np.ndarray
inference_user_features: np.ndarray
@property
def lengths(self) -> np.ndarray:
return np.diff(self.offsets)
@property
def target_count(self) -> int:
return int(np.maximum(self.lengths - 1, 0).sum())
@dataclass(frozen=True)
class TargetPartition:
"""A contiguous recent target window and its chronological BCE split."""
window_starts: np.ndarray
training_counts: np.ndarray
validation_counts: np.ndarray
history_training_counts: np.ndarray
@property
def selected_count(self) -> int:
return int(self.training_counts.sum() + self.validation_counts.sum())
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def model_config(config: Config) -> base.TowerConfig:
## Step 12 · agent · 2026-09-29T01:23:22.706Z
### read_file · 2026-09-29T01:23:22.706Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 350,
"start_line": 151
}
### Tool output · 2026-09-29T01:23:22.711Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-350 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: 351.
--- FILE CONTENT (truncated) ---
"""Map the dense trainer configuration onto the unchanged tower."""
return base.TowerConfig(
sequence_length=config.bptt_steps,
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim,
user_hidden_1=config.user_hidden_1,
user_hidden_2=config.user_hidden_2,
item_hidden_1=config.item_hidden_1,
item_hidden_2=config.item_hidden_2,
dropout=config.dropout,
epochs=config.epochs,
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
gradient_clip=config.gradient_clip,
uniform_negatives=config.uniform_negatives,
inbatch_negatives=config.inbatch_negatives,
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
seed=config.seed,
)
def append_state_snapshot(
active_likes: dict[int, int],
active_dislikes: dict[int, int],
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> int:
liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))
disliked_states.append(
base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
)
state_counts.append((len(active_likes), len(active_dislikes)))
return len(liked_states) - 1
def causal_activity_features(
timestamps: np.ndarray,
items: np.ndarray,
organic: np.ndarray,
completion: np.ndarray,
) -> np.ndarray:
"""Compute the first four reviewed user features at every event cutoff."""
length = len(items)
result = np.zeros((length, 4), dtype=np.float16)
if not length:
return result
left_rows = np.searchsorted(
timestamps,
timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
rows = np.arange(length, dtype=np.int64)
counts = rows - left_rows + 1
organic_sum = np.cumsum(organic.astype(np.float64))
completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
before_organic = np.where(
left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
)
before_completion = np.where(
left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
)
unique_counts = np.empty(length, dtype=np.int32)
frequencies: dict[int, int] = {}
left = 0
for right, raw_item in enumerate(items):
item = int(raw_item)
frequencies[item] = frequencies.get(item, 0) + 1
required_left = int(left_rows[right])
while left < required_left:
old_item = int(items[left])
remaining = frequencies[old_item] - 1
if remaining:
frequencies[old_item] = remaining
else:
del frequencies[old_item]
left += 1
unique_counts[right] = len(frequencies)
result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
result[:, 2] = (
(organic_sum - before_organic) / np.maximum(counts, 1)
).astype(np.float16)
result[:, 3] = (
(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,
) -> np.ndarray:
start = int(
np.searchsorted(
timestamps,
history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
)
result = np.zeros(6, dtype=np.float32)
recent_items = items[start:]
if len(recent_items):
result[0] = np.log1p(len(recent_items)) / 8.0
result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
result[2] = float(np.mean(event_features[start:, 0]))
result[3] = float(np.mean(event_features[start:, 1]))
result[4] = np.log1p(like_count) / 6.0
result[5] = np.log1p(dislike_count) / 4.0
return result
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)
## Step 13 · agent · 2026-09-29T01:23:24.467Z
### grep_search · 2026-09-29T01:23:24.467Z
{
"pattern": "^def ",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:24.483Z
Found 59 matches for pattern "^def " in the workspace directory:
---
File: solution/train_current_model.py
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: solution/two_tower_model.py
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(
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---
File: solution/evaluate_public.py
L51: def validation_truth(
L92: def load_submission(
L124: def score(
L140: def main() -> None:
---
File: solution/ranking_evaluation.py
L88: def read_ranking(path: Path) -> pd.DataFrame:
L101: def validate_ranking(
L167: def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
L183: def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
L207: def score_rankings(
---
## Step 14 · agent · 2026-09-29T01:23:27.882Z
### Recorded reasoning summary
Exploring Model Training Logic
I'm currently examining several key functions within `train_current_model.py`, specifically focusing on how item corrections, known positive filters, and various negative sampling strategies are constructed and implemented for model training.
### read_file · 2026-09-29T01:23:27.882Z
{
"end_line": 760,
"start_line": 530,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:27.886Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 530-760 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: 761.
--- 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)
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(
positive_items,
known_history,
liked_items,
len(prepared.item_ids),
config.uniform_negatives,
config.inbatch_negatives,
item_correction,
generator,
device,
)
## Step 15 · agent · 2026-09-29T01:23:35.118Z
### read_file · 2026-09-29T01:23:35.118Z
{
"start_line": 761,
"file_path": "/app/solution/train_current_model.py",
"end_line": 980
}
### Tool output · 2026-09-29T01:23:35.122Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 761-980 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: 981.
--- FILE CONTENT (truncated) ---
def event_sequence(
model: base.SequentialTwoTower,
items: torch.Tensor,
features: torch.Tensor,
hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
inputs = torch.cat([model.context_embedding(items), features], dim=-1)
projected = model.event_projection(inputs)
return model.sequence_encoder(projected, hidden)
def pool_explicit_states(
model: base.SequentialTwoTower,
prepared: SequenceData,
state_ids: np.ndarray,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
unique_states, inverse = np.unique(state_ids, return_inverse=True)
liked_items = torch.from_numpy(
prepared.liked_states[unique_states].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.disliked_states[unique_states].astype(np.int64)
).to(device)
liked_vectors = model._mean_context_channel(liked_items)
disliked_vectors = model._mean_context_channel(disliked_items)
inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
counts = prepared.state_counts[state_ids]
return (
liked_vectors[inverse_rows],
disliked_vectors[inverse_rows],
torch.from_numpy(counts.astype(np.float32)).to(device),
)
def user_queries(
model: base.SequentialTwoTower,
sequence_vectors: torch.Tensor,
liked_vectors: torch.Tensor,
disliked_vectors: torch.Tensor,
user_features: torch.Tensor,
) -> torch.Tensor:
return model.user_tower(
torch.cat(
[sequence_vectors, liked_vectors, disliked_vectors, user_features],
dim=1,
)
)
def candidate_vectors(
model: base.SequentialTwoTower,
positives: torch.Tensor,
negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
all_items = torch.cat([positives, negatives.reshape(-1)])
unique_items, inverse = torch.unique(all_items, return_inverse=True)
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):,}"
)
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 = (
## Step 16 · agent · 2026-09-29T01:23:36.949Z
### read_file · 2026-09-29T01:23:36.949Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1250,
"start_line": 981
}
### Tool output · 2026-09-29T01:23:36.953Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 981-1250 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: 1251.
--- FILE CONTENT (truncated) ---
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]
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],
## Step 17 · agent · 2026-09-29T01:23:38.718Z
### read_file · 2026-09-29T01:23:38.718Z
{
"start_line": 1251,
"end_line": 1400,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:38.722Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1251-1400 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: 1401.
--- FILE CONTENT (truncated) ---
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),
"loss": total_loss / max(total_weight, 1.0e-12),
"positive_logit": total_positive / max(total_targets, 1),
"negative_logit": total_negative / max(total_targets * negative_count, 1),
"pair_accuracy": total_pair_correct
/ max(total_targets * negative_count, 1),
"targets": float(total_targets),
"target_weight_sum": total_weight,
"batches": float(batches),
"seconds": time.monotonic() - started,
}
@torch.no_grad()
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):
## Step 18 · agent · 2026-09-29T01:23:41.302Z
### read_file · 2026-09-29T01:23:41.302Z
{
"end_line": 1600,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1401
}
### Tool output · 2026-09-29T01:23:41.308Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1401-1600 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: 1601.
--- FILE CONTENT (truncated) ---
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 {
"validation_loss": total_loss / max(total_weight, 1.0e-12),
"validation_targets": float(total_targets),
"validation_target_weight_sum": total_weight,
"validation_seconds": time.monotonic() - started,
}
def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
return {
name: value.detach().cpu().clone()
for name, value in model.state_dict().items()
}
def save_epoch_checkpoint(
checkpoint_dir: Path,
epoch: int,
model_state: dict[str, torch.Tensor],
config: Config,
diagnostics: dict[str, float],
optimizer: torch.optim.Optimizer,
generator: torch.Generator,
) -> Path:
checkpoint_dir.mkdir(parents=True, exist_ok=True)
path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": model_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
path,
)
log(f"saved epoch checkpoint: {path}")
return path
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(
## Step 19 · agent · 2026-09-29T01:23:43.024Z
### read_file · 2026-09-29T01:23:43.024Z
{
"start_line": 1601,
"file_path": "/app/solution/train_current_model.py",
"end_line": 1800
}
### Tool output · 2026-09-29T01:23:43.030Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1601-1800 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: 1801.
--- FILE CONTENT (truncated) ---
"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,
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
@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(
## Step 20 · agent · 2026-09-29T01:23:46.683Z
### read_file · 2026-09-29T01:23:46.683Z
{
"start_line": 1801,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2050
}
### Tool output · 2026-09-29T01:23:46.690Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1801-2050 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) ---
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")
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 21 · agent · 2026-09-29T01:23:48.617Z
### read_file · 2026-09-29T01:23:48.617Z
{
"start_line": 2051,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2165
}
### Tool output · 2026-09-29T01:23:48.624Z
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 22 · agent · 2026-09-29T01:23:51.513Z
### Recorded reasoning summary
Running Baseline Model
I'm initiating a baseline model run to establish initial performance metrics, specifically focusing on execution time and the validation NDCG@10 score. Concurrently, I'm reviewing the prompt's directive for reproducing the production public backtest.
### read_file · 2026-09-29T01:23:51.513Z
{
"start_line": 470,
"file_path": "/app/solution/train_current_model.py",
"end_line": 535
}
### Tool output · 2026-09-29T01:23:51.520Z
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 23 · agent · 2026-09-29T01:23:55.433Z
I will run the baseline training on the validation stage with `--prepared-cache /app/solution/cache_val.npz` and evaluate it with `evaluate_public.py`.
### run_shell_command · 2026-09-29T01:23:55.433Z
{
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --output /app/solution/production_validation.parquet && \\\npython /task/evaluate_public.py /app/solution/production_validation.parquet",
"dir_path": "/app/solution",
"description": "Run baseline model training on validation stage and evaluate with public evaluator"
}
### Tool output · 2026-09-29T01:30:04.813Z
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=20.6s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.5s
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=35.8s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s
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/cache_val.npz
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=198.1s
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=211.4 validation_seconds=15.7
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": 361.553,
"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": 0.5398380465323656,
"positive_logit": 0.7427799632374114,
"negative_logit": -0.6222247021058293,
"pair_accuracy": 0.754773301847281,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 211.42121194599997,
"validation_loss": 0.4966362195648962,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.701991082000063
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497606277466,
"item_norm_mean": 1.7570279836654663,
"item_norm_std": 0.8279485106468201
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1631
## Step 24 · agent · 2026-09-29T01:30:11.369Z
### Recorded reasoning summary
Reproducing Baseline Performance
I've successfully reproduced the baseline metrics. The quality NDCG@10 is 0.02128 for 4263 users. The total runtime was approximately six minutes, with data preparation cached and one epoch of training taking the longest portion.
I will verify the cache loading speed by running `--prepare-only` with `--prepared-cache`.
### run_shell_command · 2026-09-29T01:30:11.369Z
{
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --prepare-only",
"dir_path": "/app/solution",
"description": "Test cache loading speed"
}
### Tool output · 2026-09-29T01:30:15.389Z
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: 1683
## Step 25 · agent · 2026-09-29T01:30:17.559Z
I will test loading the cache with `--prepare-only` and `--output`.
### run_shell_command · 2026-09-29T01:30:17.559Z
{
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --output /app/solution/dummy.parquet \\\n --prepare-only",
"dir_path": "/app/solution",
"description": "Test cache load time with prepare-only"
}
### Tool output · 2026-09-29T01:30:22.001Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
{
"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": 0.491,
"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: 1694
## Step 26 · agent · 2026-09-29T01:30:32.564Z
### Recorded reasoning summary
Analyzing Prompt Parameters
I'm currently dissecting the prompt's nuances and constraints. My recent focus has been on understanding the core requirements for the music recommendation system, particularly within the "Made for You" shelf context.
### Recorded reasoning summary
Investigating Retrieval Gaps
I'm digging into the current two-tower model, specifically its offline retrieval quality which is falling short for the upcoming launch. I'm using the provided implementation and validation data to pinpoint the issues and then will focus on improving its training objective to ship a more effective ranking artifact.
### Recorded reasoning summary
Refining Negative Sampling
I'm now zeroing in on the training objective's handling of negative examples. My current task is to pinpoint shortcomings in the existing negative-sampling strategy and devise improvements that fit within the CPU budget, aiming to enhance offline retrieval quality.
### Recorded reasoning summary
Validating Offline Improvements
I'm now focusing on validating recent negative sampling and loss function adjustments via the public backtest. My priority is to confirm these changes perform effectively within the existing CPU constraints, while respecting all data and serving boundaries.
### Recorded reasoning summary
Improving Negative Sampling Strategy
I'm currently refining the negative sampling logic. My recent focus has been on how to efficiently draw mixed negatives while excluding known positives from the training set, aiming to boost the overall retrieval effectiveness.
### Recorded reasoning summary
Enhancing Negative Sampling
I'm currently refining the negative sampling logic. My recent focus has been on how to efficiently draw mixed negatives while excluding known positives from the training set, aiming to boost the overall retrieval effectiveness.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm currently refining the negative sampling logic. My recent focus has been on how to efficiently draw mixed negatives while excluding known positives from the training set, aiming to boost the overall retrieval effectiveness.
### grep_search · 2026-09-29T01:30:32.564Z
{
"pattern": "dislike",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:30:32.583Z
Found 40 matches for pattern "dislike" in the workspace directory:
---
File: solution/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: solution/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 27 · agent · 2026-09-29T01:30:55.559Z
### Recorded reasoning summary
Refining Input Data
I'm re-evaluating the training data. Currently, dislikes are exclusively an input feature for the user tower. I need to consider if other data points, like low-completion listens, should also be incorporated as negative examples to enhance model training.
### Recorded reasoning summary
Investigating Negative Examples
I'm digging into how the training objective currently selects and utilizes negative examples. My initial hypothesis is that the strategy is limited. I need to pinpoint these limitations and devise improvements within the CPU constraints, with validation via the public backtest.
### Recorded reasoning summary
Exploring Negative Sampling Strategies
I'm examining the specific mechanisms for negative sampling, including retrieval, weighted cross-entropy, and the number of unique in-batch negatives. I'm particularly focused on how `draw_full_history_filtered_negatives` selects and corrects these in-batch negatives to ensure optimal training data.
### Recorded reasoning summary
Clarifying Positive Item Definition
I'm tracing the data flow, specifically examining the `positive_items` tensor within `run_training_epoch`. I need to understand its exact composition and how it relates to user batch size and timesteps to fully grasp the `draw_training_negatives` function's input.
### Recorded reasoning summary
Analyzing Negative Sampling Efficiency
I'm observing that `draw_full_history_filtered_negatives` is invoked for each target within a large block, leading to substantial tensor operations. This is impacting performance, so I'm investigating how unique targets are identified and processed to optimize the generation of in-batch negatives.
### Recorded reasoning summary
Revising Loss Calculation
I'm reviewing the loss calculations and noticed something interesting. The positive loss is essentially `-log(sigmoid(positive_logits))`. I'm now analyzing the `corrected_negative_loss` function to understand its components and how they differ from a standard approach.
### Recorded reasoning summary
Re-evaluating Loss Logic
I'm reassessing how positive and negative losses are currently combined within the training objective. The current formulation seems to average them, which might not be optimal. I'm investigating the implications of this averaging and considering alternatives that could yield better performance.
### Recorded reasoning summary
Analyzing Loss Averaging
I'm realizing the current loss calculation averages the positive loss with the mean of 32 negatives. This means the model optimizes for a single positive against the *average* negative score, not necessarily the hardest negatives. This might explain why some slightly negative scores aren't strongly penalized.
### Recorded reasoning summary
Investigating Selection Logic
I'm re-examining the selection and usage of negative examples within the training objective. My focus is on pinpointing limitations in the current negative-sampling strategy.
### Recorded reasoning summary
Examining Selection and Usage
I'm now meticulously tracing how negative examples are selected, looking at `uniform_negatives` and `inbatch_negatives`. My next step is to thoroughly investigate the `invalid_full_history_negatives` function to understand its role in filtering and to diagnose current limitations in my negative sampling strategy.
### Recorded reasoning summary
Refining Negative Selection
I'm delving into the specifics of `invalid_full_history_negatives`. I've discovered that it's designed to catch duplicates across positives and negatives within a batch, but also handles self-duplication among negatives. I need to ensure this filtering is robust and doesn't inadvertently exclude useful hard negatives.
I will inspect the distributions and sizes of dislikes, listens, and candidate data.
### run_shell_command · 2026-09-29T01:30:55.559Z
{
"dir_path": "/app/solution",
"description": "Inspect shape and user/item counts across event parquet files",
"command": "python3 -c \"\nimport pandas as pd\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'val_listens', 'val_likes', 'val_dislikes']:\n df = pd.read_parquet(f'/task/data/{name}.parquet')\n print(f'{name}: shape={df.shape}, uids={df.uid.nunique()}, items={df.item_id.nunique()}')\n\""
}
### Tool output · 2026-09-29T01:30:58.226Z
Output: train_listens: shape=(43484159, 6), uids=9238, items=845395
train_likes: shape=(814831, 4), uids=8144, items=172303
train_dislikes: shape=(101075, 4), uids=5732, items=50876
val_listens: shape=(1501697, 6), uids=8196, items=191747
val_likes: shape=(37795, 4), uids=4114, items=22902
val_dislikes: shape=(3563, 4), uids=955, items=3135
Process Group PGID: 1714
## Step 28 · agent · 2026-09-29T01:31:00.989Z
I will inspect `candidate_catalog.parquet` and eligibility candidates.
### run_shell_command · 2026-09-29T01:31:00.989Z
{
"description": "Inspect candidate catalog and eligibility data structure",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog:', catalog.shape)\nprint(catalog.head())\n\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('elig keys:', elig.files)\nprint('uids:', len(elig['uids']), 'item_ids:', len(elig['item_ids']), 'packed:', elig['packed_eligible'].shape)\n\"",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:31:02.636Z
Output: catalog: (30000, 4)
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
elig keys: ['uids', 'item_ids', 'packed_eligible']
uids: 8456 item_ids: 30000 packed: (8456, 3750)
Process Group PGID: 1734
## Step 29 · agent · 2026-09-29T01:31:05.470Z
I will check how many items are eligible per user.
### run_shell_command · 2026-09-29T01:31:05.470Z
{
"command": "python3 -c \"\nimport numpy as np\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(elig['packed_eligible'], axis=1, count=30000, bitorder='little')\nprint('eligible per user mean:', unpacked.sum(axis=1).mean(), 'min:', unpacked.sum(axis=1).min(), 'max:', unpacked.sum(axis=1).max())\n\"",
"description": "Check mean/min/max eligible candidate count per user",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:31:06.188Z
Output: eligible per user mean: 29067.306291390727 min: 24657 max: 30000
Process Group PGID: 1751
## Step 30 · agent · 2026-09-29T01:31:10.794Z
### Recorded reasoning summary
Refining candidate eligibility
I'm currently focusing on the eligibility criteria for candidates, observing that a substantial majority, approximately 29,067 out of 30,000, qualify for each user. My next step involves re-examining the evaluation metric to ensure a precise understanding of "relevance" within the specified organic behavior windows.
I will inspect the validation truth statistics: number of users, number of relevant items, grade distribution.
### run_shell_command · 2026-09-29T01:31:10.794Z
{
"description": "Inspect validation ground truth statistics",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nprint('Users with truth:', len(truth))\ntotal_grade1 = sum(sum(1 for g in items.values() if g == 1) for items in truth.values())\ntotal_grade3 = sum(sum(1 for g in items.values() if g == 3) for items in truth.values())\nprint(f'Total grade 1: {total_grade1}, Total grade 3: {total_grade3}')\nuser_items_count = [len(items) for items in truth.values()]\nprint(f'Items per user: mean={np.mean(user_items_count):.2f}, median={np.median(user_items_count)}, max={max(user_items_count)}')\n\"",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:31:12.460Z
Output: Traceback (most recent call last):
File "", line 9, in
File "/app/solution/evaluate_public.py", line 69, in validation_truth
listens = pd.read_parquet(DATA / "val_listens.parquet")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 669, in read_parquet
return impl.read(
^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 258, in read
path_or_handle, handles, filesystem = _get_path_or_handle(
^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 141, in _get_path_or_handle
handles = get_handle(
^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle
handle = open(handle, ioargs.mode)
^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/app/solution/data/val_listens.parquet'
Exit Code: 1
Process Group PGID: 1761
## Step 31 · agent · 2026-09-29T01:31:18.719Z
I will create a symlink `/app/solution/data -> /task/data` so relative data paths in `/app/solution` work seamlessly.
### run_shell_command · 2026-09-29T01:31:18.719Z
{
"description": "Symlink /task/data to /app/solution/data",
"command": "ln -s /task/data /app/solution/data",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:31:19.967Z
Output: (empty)
Process Group PGID: 1777
## Step 32 · agent · 2026-09-29T01:31:24.045Z
I will run the truth inspection script again.
### run_shell_command · 2026-09-29T01:31:24.045Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nprint('Users with truth:', len(truth))\ntotal_grade1 = sum(sum(1 for g in items.values() if g == 1) for items in truth.values())\ntotal_grade3 = sum(sum(1 for g in items.values() if g == 3) for items in truth.values())\nprint(f'Total grade 1: {total_grade1}, Total grade 3: {total_grade3}')\nuser_items_count = [len(items) for items in truth.values()]\nprint(f'Items per user: mean={np.mean(user_items_count):.2f}, median={np.median(user_items_count)}, max={max(user_items_count)}')\n\"",
"description": "Inspect validation ground truth statistics",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:31:26.104Z
Output: Users with truth: 4263
Total grade 1: 33352, Total grade 3: 4417
Items per user: mean=8.86, median=4.0, max=442
Process Group PGID: 1780
## Step 33 · agent · 2026-09-29T01:31:29.255Z
I will inspect the predictions of the baseline ranking compared to the validation truth.
### run_shell_command · 2026-09-29T01:31:29.255Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npred = pd.read_parquet('/app/solution/production_validation.parquet')\nprint('pred shape:', pred.shape)\nprint('pred head:')\nprint(pred.head(10))\n\n# Popularity distribution in top-10\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\npred['pop_rank'] = pred.item_id.map(item_pop)\ntop10 = pred[pred.rank <= 10]\nprint('Top 10 predicted popularity ranks:')\nprint(top10['pop_rank'].describe())\n\ntop100 = pred\nprint('Top 100 predicted popularity ranks:')\nprint(top100['pop_rank'].describe())\n\"",
"dir_path": "/app/solution",
"description": "Analyze predicted items popularity distribution"
}
### Tool output · 2026-09-29T01:31:30.853Z
Output: Traceback (most recent call last):
File "", line 15, in
TypeError: '<=' not supported between instances of 'method' and 'int'
pred shape: (845600, 3)
pred head:
uid item_id rank
0 100 6511826 1
1 100 3972057 2
2 100 2736766 3
3 100 4948220 4
4 100 5488609 5
5 100 7958357 6
6 100 8460142 7
7 100 8250570 8
8 100 5570016 9
9 100 7510702 10
Exit Code: 1
Process Group PGID: 1800
## Step 34 · agent · 2026-09-29T01:31:33.559Z
I will inspect the popularity distribution of predictions using bracket indexing for `rank`.
### run_shell_command · 2026-09-29T01:31:33.559Z
{
"description": "Check popularity of top predictions",
"dir_path": "/app/solution",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npred = pd.read_parquet('/app/solution/production_validation.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\npred['pop_rank'] = pred['item_id'].map(item_pop)\ntop10 = pred[pred['rank'] <= 10]\nprint('Top 10 predicted popularity ranks:')\nprint(top10['pop_rank'].describe())\n\nprint('Top 100 predicted popularity ranks:')\nprint(pred['pop_rank'].describe())\n\""
}
### Tool output · 2026-09-29T01:31:35.185Z
Output: Top 10 predicted popularity ranks:
count 84560.000000
mean 2597.053903
std 5715.185504
min 1.000000
25% 14.000000
50% 111.000000
75% 2124.000000
max 29868.000000
Name: pop_rank, dtype: float64
Top 100 predicted popularity ranks:
count 845600.000000
mean 2991.800246
std 5853.839823
min 1.000000
25% 102.000000
50% 413.000000
75% 2627.000000
max 29992.000000
Name: pop_rank, dtype: float64
Process Group PGID: 1819
## Step 35 · agent · 2026-09-29T01:31:41.927Z
I will inspect the popularity distribution of the actual validation relevant items.
### run_shell_command · 2026-09-29T01:31:41.927Z
{
"description": "Check popularity distribution of true validation items",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\nall_items = []\nfor items in truth.values():\n all_items.extend(items.keys())\n\ns = pd.Series([item_pop[i] for i in all_items])\nprint('True validation items popularity ranks:')\nprint(s.describe())\n\"",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:31:44.024Z
Output: True validation items popularity ranks:
count 37769.000000
mean 8463.087744
std 8117.953262
min 1.000000
25% 1733.000000
50% 5622.000000
75% 13374.000000
max 30000.000000
dtype: float64
Process Group PGID: 1837
## Step 36 · agent · 2026-09-29T01:32:33.635Z
### Recorded reasoning summary
Analyzing Model Performance
I'm reviewing the latest metrics, specifically comparing true validation item ranks against those predicted by the current model. The median rank for predicted top 10 items is significantly lower than the true validation median, which is a promising sign of improved predictive accuracy in identifying popular items.
### Recorded reasoning summary
Investigating Training Objective
My latest analysis reveals a concerning trend: the model excels at predicting *super popular* items, but these aren't aligning with organic user discovery. This suggests my negative sampling strategy might be contributing to a popularity bias, which is counter to our Music Discovery objective of helping users find new, organically engaging tracks.
### Recorded reasoning summary
Reviewing Sampling Strategy
I'm digging into the negative sampling code. It seems `uniform_negatives` are set to 24 and `inbatch_negatives` to 8, with correction factors applied. My focus is now on the `build_item_correction` function to understand how these in-batch negatives are actually weighted.
### Recorded reasoning summary
Exploring Correction Logic
I'm examining how the calculated in-batch correction factors are applied. Specifically, I'm tracing their integration within `draw_full_history_filtered_negatives` to understand their direct impact on the sampling process and its potential influence on the observed popularity bias.
### Recorded reasoning summary
Adjusting Loss Calculation
I'm now focusing on how the `corrected_negative_loss` function utilizes the `correction` tensor. It appears the raw softplus values are being averaged across uniform negatives, and I'm investigating how the in-batch negatives are then incorporated using this correction.
### Recorded reasoning summary
Refining Loss Weighting
I've identified a crucial detail: the `weights` in `corrected_negative_loss` are inversely proportional to item probability. This means popular items get a small weight, reducing their loss contribution when they're sampled as negatives. I need to re-evaluate this logic.
### Recorded reasoning summary
Correcting Popularity Bias
I'm re-evaluating the loss calculation, specifically how item frequencies influence negative sampling. It appears popular items, sampled more often as negatives, should have their logits adjusted by subtracting their log probability to mitigate bias, a detail I'd overlooked.
### Recorded reasoning summary
Questioning Baseline Approach
I'm questioning the baseline's methodology, particularly their use of Binary Cross-Entropy with a specific correction factor. It seems their approach of weighting in-batch negative loss by the inverse square root of item probability effectively attempts to equalize negative sampling distributions, inadvertently downplaying penalties for popular items.
### Recorded reasoning summary
Revising Negative Sampling
I'm re-examining how negative examples are chosen and utilized within the training objective. My recent findings indicate the top recommendations are heavily skewed towards the most popular items, suggesting a critical need to diagnose and improve the current negative-sampling strategy to address this popularity bias.
### Recorded reasoning summary
Revising Selection Logic
I'm scrutinizing how negative examples are identified and incorporated. The current process, which samples a limited number of in-batch and uniform negatives per target token, seems computationally inefficient and potentially flawed. I'm investigating the exact mechanics within `draw_full_history_filtered_negatives` to pinpoint where improvements can be made.
### Recorded reasoning summary
Revising Batching Strategy
I'm realizing that standard two-tower architectures leverage the entire query-item dot product matrix as in-batch negatives. This implicitly provides $B-1$ negatives per query from a batch of size $B$, a much richer set than my current explicit sampling. I need to integrate this insight.
### Recorded reasoning summary
Investigating Loss Function
I'm exploring the loss function, considering options like InfoNCE, sampled softmax, BPR, or margin ranking. The current model utilizes sample-weighted binary cross-entropy with a specific configuration of uniform and in-batch negatives. My next step is to determine how this combination impacts the observed popularity bias.
### Recorded reasoning summary
Revising Loss Contribution
I'm recalibrating how popular items contribute to the loss. It appears the inverse probability weighting for negatives needs adjustment; currently, highly frequent items receive minimal penalty, exacerbating the popularity bias. I need to ensure they are penalized appropriately when sampled.
### Recorded reasoning summary
Analyzing Negative Sampling
I'm continuing to investigate the negative sampling process, focusing on the interplay between uniform and in-batch negatives. My current hypothesis is that the ratio and weighting of these are directly contributing to the observed popularity bias, and I'm exploring how to adjust them for better representation.
### Recorded reasoning summary
Exploring Negative Sampling Options
I'm considering increasing the number of in-batch negatives or experimenting with popularity-sampled or hard negatives. I also discovered that disliked items are not currently used as negatives, which presents a new avenue for improving the sampling strategy.
I will check the distribution of played_ratio_pct in `train_listens.parquet`.
### run_shell_command · 2026-09-29T01:32:33.635Z
{
"dir_path": "/app/solution",
"description": "Inspect listen completion ratio distribution",
"command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['played_ratio_pct', 'is_organic'])\nprint('Total listens:', len(df))\nprint('played_ratio_pct < 50%:', (df.played_ratio_pct < 50).sum(), f'({(df.played_ratio_pct < 50).mean():.1%})')\nprint('played_ratio_pct < 20% (skipped):', (df.played_ratio_pct < 20).sum(), f'({(df.played_ratio_pct < 20).mean():.1%})')\nprint('played_ratio_pct >= 50%:', (df.played_ratio_pct >= 50).sum(), f'({(df.played_ratio_pct >= 50).mean():.1%})')\nprint('played_ratio_pct >= 80% (strong):', (df.played_ratio_pct >= 80).sum(), f'({(df.played_ratio_pct >= 80).mean():.1%})')\n\""
}
### Tool output · 2026-09-29T01:32:35.539Z
Output: Total listens: 43484159
played_ratio_pct < 50%: 15983404 (36.8%)
played_ratio_pct < 20% (skipped): 13293566 (30.6%)
played_ratio_pct >= 50%: 27500755 (63.2%)
played_ratio_pct >= 80% (strong): 25755197 (59.2%)
Process Group PGID: 1857
## Step 37 · agent · 2026-09-29T01:33:18.907Z
### Recorded reasoning summary
Analyzing Listen Completion
My thoughts are currently focused on understanding the significant number of listens that are not being completed, with a substantial portion falling below 50% and even 20% completion. I'm now reviewing the current pipeline, specifically within `two_tower_model.py`, to see how these listen events are being processed.
### Recorded reasoning summary
Investigating Negative Selection
I've discovered that listens with less than 50% completion are completely excluded by the data loader. I need to verify if this filtering impacts the selection of negative examples, as the prompt specifically directs attention to how negative examples are chosen and utilized within the training objective.
### Recorded reasoning summary
Refining Negative Sampling
I'm now examining the `Config` parameters related to negative sampling and target weighting. My focus is on understanding how listens below 50% completion are handled and how this might influence the selection and weighting of negative examples. I'm comparing the `Config` defaults with available argument parser options.
### Recorded reasoning summary
Exploring Target Weighting
I'm currently examining the `target-weighting` configuration. It appears the default setting is `organic-strong`, not `discovery-graded` as I initially suspected. This distinction is crucial as the prompt emphasizes negative example selection and utilization.
### Recorded reasoning summary
Examining Negative Draw Logic
I'm now delving into `draw_full_history_filtered_negatives` and `corrected_negative_loss`. My focus is on precisely how in-batch negatives are sourced and integrated into the training objective. I'm closely reviewing specific code sections within `train_current_model.py` for these functions.
### Recorded reasoning summary
Analyzing Negative Sampling Logic
I'm now scrutinizing the `uniform` and `inbatch` negative sampling code. My current focus is on how unique targets are determined and how `corrected_count` is calculated. I'm also examining the logic for filling remaining in-batch negatives when `corrected_count` is less than the requested amount.
### Recorded reasoning summary
Reviewing Negative Sample Generation
I'm now focused on how `corrected_count` influences the filling of in-batch negatives and the subsequent generation of replacements when invalid negatives are detected. My analysis traces the logic through `invalid_full_history_negatives` to ensure accurate sample creation.
### Recorded reasoning summary
Confirming Uniform Negative Sampling
I've confirmed that `unique_targets` are sampled uniformly at random for in-batch negatives. This means every unique target within the batch has an equal probability of being selected as a negative example, regardless of its specific characteristics.
### Recorded reasoning summary
Adjusting Negative Sample Selection
I'm now investigating how `priorities = torch.rand((batch_size, len(unique_targets)))` samples from `unique_targets`. It seems popular and unpopular items are chosen with equal probability. This is a crucial observation regarding the current uniform negative sampling strategy.
### Recorded reasoning summary
Adjusting Negative Sample Selection
I've determined that `unique_targets` are sampled uniformly for in-batch negatives, meaning popular and unpopular items have an equal chance of selection. This uniform sampling, combined with the inverse propensity weighting `item_correction` which downweights popular items by a factor of $(1 / p_i)^{0.5}$, means popular items are sampled less often in the loss and receive a significantly lower weight.
### Recorded reasoning summary
Refining Negative Sampling Strategy
I've found that the current strategy uniformly samples unique targets for in-batch negatives. This, combined with inverse propensity weighting, results in popular items being sampled less often and receiving lower loss weights, effectively making them less penalized. This needs adjustment to properly penalize popular items.
### Recorded reasoning summary
Adjusting Negative Sampling Strategy
I've found that the current strategy uniformly samples unique targets for in-batch negatives. This, combined with inverse propensity weighting, results in popular items being sampled less often and receiving lower loss weights, effectively making them less penalized. This needs adjustment to properly penalize popular items.
### Recorded reasoning summary
Revising Negative Sampling
I'm now considering sampling in-batch negatives directly from `targets` with replacement. This approach would naturally reflect item popularity within the batch, aligning with the core principle of in-batch negative sampling, which is to draw negatives based on their empirical popularity. This contrasts with my previous focus on uniform sampling of unique targets.
### Recorded reasoning summary
Sampling Negatives Differently
I'm now exploring sampling in-batch negatives directly from the `targets` tensor with replacement. This method would inherently capture item popularity within the batch, directly aligning with the core intent of in-batch negative sampling. This is a significant shift from my earlier focus on uniform sampling of unique targets.
### Recorded reasoning summary
Adjusting Negative Sampling Implementation
I'm now refining the implementation for calculating the loss contribution from uniform and in-batch negatives. My focus is on correctly applying the weights and ensuring the averaging across the sampled negatives is accurate, particularly for the in-batch component.
### Recorded reasoning summary
Revising Loss Calculation
I'm now re-evaluating how the loss is structured. My current thoughts are on whether BCE is truly the optimal choice for retrieval tasks, considering alternatives like InfoNCE, Softmax, BPR, Margin, or Listwise ranking losses, and specifically checking if the prompt mandates BCE or allows for other loss functions.
### Recorded reasoning summary
Revising Loss Function Choice
I'm now questioning whether Binary Cross-Entropy (BCE) is the best fit for retrieval tasks, considering alternatives like InfoNCE, Softmax, BPR, Margin, or Listwise ranking losses. My primary focus is on ensuring the chosen loss aligns with the prompt's implicit requirements and effectively handles negative example utilization.
### Recorded reasoning summary
Refining Negative Sampling Strategy
I've found that the current strategy uniformly samples unique targets for in-batch negatives. This, combined with inverse propensity weighting, results in popular items being sampled less often and receiving lower loss weights, effectively making them less penalized. This needs adjustment to properly penalize popular items.
### Recorded reasoning summary
Testing Loss Hypotheses
I'm now experimentally verifying the impact of the inverse propensity weighting on popular and rare items within the BCE loss function. My focus is on empirically demonstrating how this weighting influences the penalty applied to different item popularity levels and assessing training duration.
### Recorded reasoning summary
Testing Loss Impact
I am now empirically evaluating how the inverse propensity weighting within the BCE loss affects popular versus rare items. My goal is to demonstrate the weighting's influence on penalties for different item popularity levels and observe any impact on training duration.
### Recorded reasoning summary
Analyzing Unique Target Sampling
I'm now scrutinizing the sampling of `unique_targets` within `draw_full_history_filtered_negatives`. My focus is on understanding why unique targets are used instead of sampling directly from the `targets` tensor, particularly how this impacts the representation of item popularity in negative sampling.
I will inspect the frequency distribution of targets in training batches.
### run_shell_command · 2026-09-29T01:33:18.907Z
{
"command": "python3 -c \"\nimport numpy as np\n\ncache = np.load('/app/solution/cache_val.npz')\nitems = cache['sequence_items']\noffsets = cache['offsets']\n# check items distribution in the recent window\nrecent_items = []\nfor i in range(len(offsets)-1):\n start = offsets[i]\n end = offsets[i+1]\n if end - start > 1:\n # last 128 targets\n t_start = max(start, end - 128)\n recent_items.append(items[t_start:end])\n\nall_recent = np.concatenate(recent_items)\nprint('Total recent targets:', len(all_recent))\nunique, counts = np.unique(all_recent, return_counts=True)\nprint('Unique items in recent targets:', len(unique), 'out of 30,000')\nprint('Counts summary: min=', counts.min(), 'median=', np.median(counts), 'mean=', counts.mean(), 'max=', counts.max())\nprint('Top 10 item frequencies:', np.sort(counts)[-10:])\n\"",
"dir_path": "/app/solution",
"description": "Check target item frequencies in recent training targets"
}
### Tool output · 2026-09-29T01:33:19.385Z
Output: Total recent targets: 1060695
Unique items in recent targets: 29757 out of 30,000
Counts summary: min= 1 median= 18.0 mean= 35.645226333299725 max= 2967
Top 10 item frequencies: [1273 1292 1327 1515 1585 1630 1631 1750 1974 2967]
Process Group PGID: 1878
## Step 38 · agent · 2026-09-29T01:33:25.414Z
### Recorded reasoning summary
Analyzing Target Frequencies
I'm currently delving into the striking disparity in target frequencies, observing that top items appear an astonishing ~3,000 times versus a mere 18 for the median. My focus is now shifting to investigate the impact of `invalid_full_history_negatives` and `invalid_negative_mask`, and specifically how `negative_filter_scope` influences training with `train_current_model`.
### Recorded reasoning summary
Investigating Invalid Negatives
I'm now dissecting how `invalid_full_history_negatives` and `invalid_negative_mask` affect training. My current focus is on understanding the implications of a `full-training-history` scope and the logic within the `train_current_model.py` script that handles invalid negative replacements.
I will inspect sample rows of `train_dislikes.parquet`.
### run_shell_command · 2026-09-29T01:33:25.414Z
{
"dir_path": "/app/solution",
"command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint(df.head(10))\nprint(df.info())\n\"",
"description": "Inspect train_dislikes sample data"
}
### Tool output · 2026-09-29T01:33:26.972Z
Output: uid timestamp item_id is_organic
0 100 3087560 9170134 1
1 100 3936560 8661238 1
2 100 20432825 4927727 1
3 400 2980800 399811 1
4 400 3157520 1575108 1
5 400 3417600 9229946 1
6 400 4968885 6593949 1
7 400 4968905 9198404 1
8 400 5519800 999732 1
9 400 5754475 1254798 1
RangeIndex: 101075 entries, 0 to 101074
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 uid 101075 non-null uint32
1 timestamp 101075 non-null uint32
2 item_id 101075 non-null uint32
3 is_organic 101075 non-null uint8
dtypes: uint32(3), uint8(1)
memory usage: 1.3 MB
None
Process Group PGID: 1888
## Step 39 · agent · 2026-09-29T01:33:54.680Z
### Recorded reasoning summary
Considering Discovery Objective
I'm currently re-evaluating the discovery objective, focusing intently on the definition of "relevance" as it pertains to organic behavior within the specified cutoff. This detailed review is crucial for accurately ranking candidates.
### Recorded reasoning summary
Refining Ranking Metrics
I'm now zeroing in on the specifics of the organic objective, particularly how "relevance" is defined for track engagement within the defined window. This means I'm scrutinizing the grading system, from grade 3 for user liking down to grade 0 for mere listening, to ensure our ranking metrics accurately reflect genuine user interest and lead to strong NDCG@10 scores.
### Recorded reasoning summary
Revising Exclusion Logic
I've realized a significant implication: candidates for validation cannot include *any* track a user encountered during training. This drastically redefines the scope of organic discovery and necessitates a careful re-evaluation of how I'm selecting and filtering validation candidates to ensure accurate performance measurement.
### Recorded reasoning summary
Analyzing Loss Calculation
I am now scrutinizing the loss calculation, specifically how positives and negatives are scored. I'm examining `score_target_microbatches` and `corrected_negative_loss` in `train_current_model.py` to understand their interplay with `positive_logits` and `negative_logits`.
### Recorded reasoning summary
Investigating Loss Functions
I'm currently diving into the loss calculation, specifically the interplay between positive and negative logits. I'm questioning whether the current implementation aligns with standard Binary Cross-Entropy (BCE) by examining how `softplus` is applied to these logits and the subsequent aggregation of losses.
### Recorded reasoning summary
Adjusting Loss Weights
I'm re-examining the loss function's gradient calculations. Specifically, I'm investigating how averaging negative samples, rather than summing them, disproportionately reduces the influence of negative items, leading to a 1:32 gradient ratio. This imbalance requires adjustment to ensure effective learning.
### Recorded reasoning summary
Evaluating Loss Functions
I am now deeply analyzing the proposed InfoNCE loss function and its implications. The core idea is to optimize ranking directly by leveraging the ratio of the positive item's score against the sum of scores from sampled negatives. This contrasts sharply with the current binary approach.
### Recorded reasoning summary
Analyzing Loss Implementations
I'm currently comparing different loss functions, specifically InfoNCE, BPR, and Binary Cross-Entropy. My focus is on how each handles the relationship between positive and negative item scores and their implications for gradient updates, particularly regarding the proposed "propensity-corrected unique in-batch negatives."
### Recorded reasoning summary
Reviewing Negative Sampling
I am now re-examining the training objective's selection and utilization of negative examples. My focus is on identifying limitations within the current negative-sampling strategy, considering the implications of "sample-weighted binary cross entropy" alongside the specifics of propensity-corrected unique in-batch negatives.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now deeply scrutinizing the training objective's handling of negative examples. My primary concern is how the current strategy, particularly "sample-weighted binary cross entropy" and propensity-corrected unique in-batch negatives, might be misinterpreting or underutilizing popular items as negatives.
### Recorded reasoning summary
Adjusting Negative Sampling
I'm now re-evaluating how in-batch negatives are sampled. The current method, using `unique_targets`, gives equal probability to all unique items regardless of their frequency, which undermines the natural advantage of in-batch sampling in exposing popular items as negatives. This needs correction.
### Recorded reasoning summary
Incorporating Dislikes
I'm now considering the explicit dislikes present in the `train_dislikes.parquet` data. My current thought is to leverage these explicitly disliked items as hard negative examples during user training, which is a significant departure from their current use solely as input features. This could offer a powerful signal.
### Recorded reasoning summary
Leveraging Dislikes Explicitly
I'm now thinking about how to best utilize the `train_dislikes.parquet` data. My current idea is to actively use these explicitly disliked items as hard negatives during user training, moving beyond their current role as just input features. This is a critical shift that could significantly improve user modeling.
**Revisiting Loss Function Rationale**
I'm now questioning the rationale behind the current loss formulation. Specifically, I'm analyzing why the existing binary cross-entropy approach averages negative losses and divides the total by two. Standard BCE typically sums negative losses, and this deviation warrants closer examination of its impact on training.
### grep_search · 2026-09-29T01:33:54.680Z
{
"pattern": "add_argument.*inbatch",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:33:54.696Z
No matches found for pattern "add_argument.*inbatch" in the workspace directory.
## Step 40 · agent · 2026-09-29T01:34:08.741Z
### Recorded reasoning summary
Analyzing Argument Scope
I've been delving into the argument definitions, specifically noting that the `inbatch` arguments are not present within `add_argument`. My current focus is on identifying which arguments *are* included in `add_argument`, examining lines 1920-2050 to pinpoint parameters like `stage`, `output`, `public-dir`, `prepared-cache`, `device`, `threads`, `bptt-steps`, and `user-batch`.
### Recorded reasoning summary
Identifying Config Parameters
I'm now focusing on arguments listed within the `Config` initialization. I've identified additional parameters like `size`, `epochs`, `learning-rate`, and various `target-weight` options, along with settings for `negative-filter-scope`, `validation-fraction`, and `early-stopping-patience`. I am also investigating the initialization of `Config` within `main`.
### Recorded reasoning summary
Discovering Defaults
I've found that several `inbatch` related parameters are hardcoded defaults within `Config`, not CLI flags. This includes `uniform_negatives`, `inbatch_negatives`, and their correction parameters. This insight is crucial for understanding their behavior and potential for modification.
### Recorded reasoning summary
Investigating Correction Weights
I'm now examining the inverse-propensity correction logic within `build_item_correction` and `corrected_negative_loss`. My focus is on how `probabilities` influence `weights`, specifically how high probabilities lead to minimal weights, potentially scaling down negative penalties for popular items.
### Recorded reasoning summary
Analyzing Sampling Impact
I am now analyzing the impact of sampling strategies on popular items. It appears uniform sampling of `unique_targets` for in-batch negatives does not penalize popular items sufficiently, unlike standard methods which inherently balance positive and negative feedback based on interaction frequency.
### Recorded reasoning summary
Exploring Negative Sampling Ratios
I'm now considering how the balance between uniform and in-batch negatives might impact gradient signals. My hypothesis is that an increased proportion of in-batch negatives, or sampling from the empirical distribution, could significantly enhance discriminative power. I am also investigating how averaging 32 negatives in the loss function attenuates individual negative gradients.
### Recorded reasoning summary
Refining Loss Function Interpretation
I'm now contrasting BCE loss with InfoNCE and BPR. InfoNCE and BPR directly encourage positive logits to surpass negative ones, with harder negatives yielding exponentially larger gradients, unlike BCE's independent treatment. This difference is key to understanding discriminative power and potential improvements.
### update_topic · 2026-09-29T01:34:08.741Z
{
"title": "Experimenting with Negative Sampling Strategy",
"strategic_intent": "Test negative sampling improvements systematically on the validation backtest.",
"summary": "We established the baseline NDCG@10 of 0.02128 and diagnosed several critical flaws in the current negative sampling strategy: (1) inverse propensity downweighting of popular in-batch negatives by up to 16x, (2) deduplication of in-batch targets which eliminates frequency-proportional sampling, (3) heavy bias toward uniform negatives over informative in-batch negatives, and (4) average BCE loss which attenuates negative gradients. We are now entering the execution phase to implement and test improvements."
}
### Tool output · 2026-09-29T01:34:08.748Z
Current topic: "Experimenting with Negative Sampling Strategy"
Topic summary: We established the baseline NDCG@10 of 0.02128 and diagnosed several critical flaws in the current negative sampling strategy: (1) inverse propensity downweighting of popular in-batch negatives by up to 16x, (2) deduplication of in-batch targets which eliminates frequency-proportional sampling, (3) heavy bias toward uniform negatives over informative in-batch negatives, and (4) average BCE loss which attenuates negative gradients. We are now entering the execution phase to implement and test improvements.
Strategic Intent: Test negative sampling improvements systematically on the validation backtest.
## Step 41 · agent · 2026-09-29T01:34:11.582Z
### Recorded reasoning summary
Implementing Model Training Parameters
I'm currently integrating new CLI arguments into the model training script to control negative sampling strategies and loss functions. This includes options for uniform and in-batch negatives, as well as parameters like correction power and sampling methods, and the ability to select different loss types.
### read_file · 2026-09-29T01:34:11.582Z
{
"end_line": 75,
"start_line": 40,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:34:11.590Z
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 42 · agent · 2026-09-29T01:34:15.011Z
### Recorded reasoning summary
Reviewing Configurations
I'm examining the `Config` parameters, specifically `uniform_negatives`, `inbatch_negatives`, and the associated `inbatch_correction` settings. I'm also cross-referencing these with line 193 for context.
### read_file · 2026-09-29T01:34:15.011Z
{
"start_line": 1940,
"end_line": 2040,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:34:15.018Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1940-2040 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: 2041.
--- 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
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,
## Step 43 · agent · 2026-09-29T01:34:52.262Z
### Recorded reasoning summary
Configuring Negative Sampling
I'm exploring command-line options to fine-tune negative sampling parameters, specifically for uniform and in-batch methods, including their counts and correction powers, alongside an option for sampling strategy ('unique' or 'frequency').
### Recorded reasoning summary
Testing Correction Power
I've been investigating the impact of setting `inbatch_correction_power` to 0.0, which simplifies negative sample weighting to uniform values. I also noted that `target_weighting="discovery-graded"` is already supported and is quick to load training data for validation.
### Recorded reasoning summary
Implementing Sampling Options
I'm adding command-line arguments for uniform and in-batch negatives, including parameters for correction power and sampling strategy ('unique' or 'frequency'). I'm also considering implementing different loss types and temperature settings.
### Recorded reasoning summary
Refining In-Batch Sampling
I've realized that drawing indices directly from the full `targets` tensor, rather than `unique_targets`, automatically achieves frequency-proportional sampling for in-batch negatives. This simplifies the process.
### Recorded reasoning summary
Validating Negative Sample Logic
I'm confirming that my existing handling of `invalid_full_history_negatives` correctly filters out cases where sampled negatives match actual targets. I'm also scrutinizing the resampling loop to ensure replacements for invalid samples are appropriate, particularly when drawing from batch targets.
### Recorded reasoning summary
Optimizing Index Generation
I've confirmed that using `torch.randint` for index generation for in-batch negatives is significantly faster than prior methods involving large float tensors and `torch.topk`. This optimization reduces the number of generated values by over 250x, making the process much more efficient.
**Investigating Item Popularity Bias**
I'm delving into why the model over-predicts popular items. My previous observation that a baseline model with `item_correction` (which downweighted popular items) resulted in a median popularity rank of 111 in the top 10 suggests an issue related to how popularity is handled or perceived by the model.
### Recorded reasoning summary
Analyzing Negative Loss
I'm questioning how sampled popular items as negatives impact their scores, potentially pushing them down and requiring stronger user affinity for recommendation. I'm now examining InfoNCE loss versus BCE, specifically how sampled softmax handles positive and negative logits with temperature.
### Recorded reasoning summary
Analyzing InfoNCE Loss
I'm struck by the elegant simplicity and effectiveness of InfoNCE. It appears to directly optimize mutual information and handles the interplay of positive and negative logits with temperature intelligently, avoiding wasted capacity when negatives are already sufficiently dissimilar.
### Recorded reasoning summary
Testing InfoNCE Evaluation
I'm confirming that my validation evaluation function correctly uses the model's loss, and I'm planning to test InfoNCE's performance on validation metrics by adjusting the number of training epochs. This should allow for quick, iterative evaluation of this promising loss function.
### Recorded reasoning summary
Exploring Model Configuration
I'm examining how the dense trainer configuration maps to the tower configuration, specifically focusing on how parameters like `sequence_length`, embedding and hidden dimensions, dropout, learning rate, and various negative sampling settings are passed through the `model_config` function.
### Recorded reasoning summary
Analyzing Model Configuration
I'm tracing how the dense trainer configuration translates to the tower configuration. Specifically, I'm reviewing how parameters like `sequence_length`, embedding and hidden dimensions, dropout, learning rate, and various negative sampling settings are propagated via `model_config`.
### Recorded reasoning summary
Analyzing Loss Options
I'm considering how different loss types like BCE, InfoNCE, and BPR, along with temperature settings and in-batch sampling strategies, will interact with the current scoring mechanism. The focus is on how these choices influence the model's learned representations and ranking performance.
### Recorded reasoning summary
Computing BCE Loss Slices
I'm now focusing on computing the exact Binary Cross-Entropy (BCE) loss within bounded-memory slices. This involves iterating through positive items, summing weights, and processing sequences and states in micro-batches to calculate loss, positive sums, negative sums, and pair correctness.
### Recorded reasoning summary
Computing BCE Loss Slices
I'm now focusing on computing the exact Binary Cross-Entropy (BCE) loss within bounded-memory slices. This involves iterating through positive items, summing weights, and processing sequences and states in micro-batches to calculate loss, positive sums, negative sums, and pair correctness.
### Recorded reasoning summary
Calculating InfoNCE Loss
I'm examining the InfoNCE loss calculation, specifically how positive and negative logits are concatenated and scaled by temperature. I'm considering the implications of omitting logQ correction for a pure InfoNCE loss and how this interacts with the BPR loss formulation.
### Recorded reasoning summary
Processing Loss Options
I'm now focusing on how to correctly implement and integrate the InfoNCE and BCE loss calculations. This involves carefully handling the concatenation and scaling of logits for InfoNCE and ensuring proper slice-based computation for BCE, while also considering the implications of in-batch sampling.
### Recorded reasoning summary
Implementing In-Batch Sampling
I'm refining the in-batch negative sampling logic. My current focus is on correctly implementing frequency-proportional sampling by drawing indices directly from the batch targets tensor, which simplifies the process and avoids needing to handle unique targets separately.
### Recorded reasoning summary
Refining Sampling Logic
I'm re-examining the indices used for frequency-based in-batch sampling. My current focus is on understanding precisely what these indices represent and how they are generated before they are used to select negative samples from the `unique_targets`.
### Recorded reasoning summary
Validating Negative Sample Logic
I've confirmed that my existing filtering correctly prevents sampled negatives from matching actual targets, with subsequent resampling in place. I'm now preparing to test the impact of BCE loss with frequency-based in-batch sampling and zero correction power, comparing it to the current baseline score.
### replace · 2026-09-29T01:34:52.262Z
{
"allow_multiple": false,
"instruction": "Add loss_type, temperature, and inbatch_sampling fields to Config dataclass",
"file_path": "/app/solution/train_current_model.py",
"old_string": " 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\"",
"new_string": " 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 inbatch_sampling: str = \"unique\"\n loss_type: str = \"bce\"\n temperature: float = 0.07\n negative_filter_scope: str = \"full-training-history\""
}
### Tool error · 2026-09-29T01:34:52.271Z
EACCES: permission denied, open '/app/solution/train_current_model.py'
## Step 44 · agent · 2026-09-29T01:34:55.387Z
I will grant write permissions to the files in `/app/solution`.
### run_shell_command · 2026-09-29T01:34:55.387Z
{
"description": "Add write permissions to copied files in /app/solution",
"command": "chmod -R u+w /app/solution",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:34:55.406Z
Output: (empty)
Process Group PGID: 1907
## Step 45 · agent · 2026-09-29T01:35:03.131Z
### replace · 2026-09-29T01:35:03.131Z
{
"file_path": "/app/solution/train_current_model.py",
"old_string": " 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\"",
"new_string": " 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 inbatch_sampling: str = \"unique\"\n loss_type: str = \"bce\"\n temperature: float = 0.07\n negative_filter_scope: str = \"full-training-history\"",
"allow_multiple": false,
"instruction": "Add loss_type, temperature, and inbatch_sampling fields to Config dataclass"
}
### Tool output · 2026-09-29T01:35:03.153Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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
inbatch_sampling: str = "unique"
loss_type: str = "bce"
temperature: float = 0.07
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
...
## Step 46 · agent · 2026-09-29T01:35:06.221Z
I updated `Config` in `/app/solution/train_current_model.py` to add `inbatch_sampling`, `loss_type`, and `temperature` fields. Next, I will update `draw_full_history_filtered_negatives`, `score_target_microbatches`, and CLI argument parsing to support frequency-based in-batch negative sampling and InfoNCE loss.
### read_file · 2026-09-29T01:35:06.221Z
{
"end_line": 685,
"file_path": "/app/solution/train_current_model.py",
"start_line": 635
}
### Tool output · 2026-09-29T01:35:07.445Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 635-685 of 2168 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) ---
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)
## Step 47 · agent · 2026-09-29T01:35:16.509Z
### Recorded reasoning summary
Refining Drawing Logic
I'm currently focusing on refining the `draw_full_history_filtered_negatives` function within `train_current_model.py`. My current work is addressing the specific case where `config.inbatch_sampling` is set to `"frequency"` and `config.inbatch_negatives` is also enabled, ensuring the filtering logic correctly handles these conditions.
### Recorded reasoning summary
Testing Sampling Approaches
My recent efforts have been dedicated to validating the implementation of sampling strategies, specifically examining how the `frequency` based in-batch negative sampling interacts with existing filtering mechanisms. I'm ensuring edge cases like zero unique targets are handled gracefully.
### replace · 2026-09-29T01:35:16.509Z
{
"file_path": "/app/solution/train_current_model.py",
"old_string": " 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)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()",
"allow_multiple": false,
"new_string": " if config.inbatch_negatives:\n if config.inbatch_sampling == \"frequency\":\n indices = torch.randint(\n batch_size,\n (batch_size, config.inbatch_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.from_numpy(targets_cpu)[indices]\n corrected_count = config.inbatch_negatives\n else:\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)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count and config.inbatch_correction_power != 0.0:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()",
"instruction": "Support frequency-based in-batch sampling and honor inbatch_correction_power in draw_full_history_filtered_negatives"
}
### Tool output · 2026-09-29T01:35:16.530Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
if config.inbatch_sampling == "frequency":
indices = torch.randint(
batch_size,
(batch_size, config.inbatch_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.from_numpy(targets_cpu)[indices]
corrected_count = config.inbatch_negatives
else:
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 and config.inbatch_correction_power != 0.0:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
...
## Step 48 · agent · 2026-09-29T01:35:28.049Z
I updated `draw_full_history_filtered_negatives` to support frequency-based in-batch negative sampling and skipping inverse propensity downweighting when `inbatch_correction_power == 0.0`. Next, I will inspect `score_target_microbatches`.
### read_file · 2026-09-29T01:35:28.049Z
{
"end_line": 1210,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1160
}
### Tool output · 2026-09-29T01:35:28.057Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1160-1210 of 2178 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: 1211.
--- FILE CONTENT (truncated) ---
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
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
## Step 49 · agent · 2026-09-29T01:35:33.049Z
### Recorded reasoning summary
Analyzing Loss Calculations
I'm currently examining the loss calculation within lines 1180-1200, specifically focusing on how logits are concatenated and scaled by temperature when the "infonce" loss type is selected. This part seems critical for understanding the contrastive learning mechanism.
### Recorded reasoning summary
Refining Loss Statistics
My focus has shifted to ensuring that the `positive_logits`, `negative_logits`, `pair_correct`, and `weight_sum` are accurately tracked across different loss types. I am now verifying that the `loss_sum` variable correctly captures the computed loss for each scenario.
### read_file · 2026-09-29T01:35:33.049Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1940,
"end_line": 2040
}
### Tool output · 2026-09-29T01:35:33.056Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1940-2040 of 2178 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.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}")
## Step 50 · agent · 2026-09-29T01:35:35.590Z
### read_file · 2026-09-29T01:35:35.590Z
{
"start_line": 2040,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2075
}
### Tool output · 2026-09-29T01:35:35.597Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2040-2075 of 2178 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: 2076.
--- FILE CONTENT (truncated) ---
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)
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,
## Step 51 · agent · 2026-09-29T01:35:40.994Z
### Recorded reasoning summary
Updating Scoring Logic
I'm refining the scoring mechanism to incorporate a broader range of loss types, including bce, infonce, and bpr. Additionally, I'm integrating new command-line arguments to enable more flexible negative sampling strategies, specifically uniform and in-batch negatives, with a tunable correction power for the latter.
### replace · 2026-09-29T01:35:40.994Z
{
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py",
"new_string": " positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n if config.loss_type == \"infonce\":\n all_logits = torch.cat(\n [positive_logits.unsqueeze(1), negative_logits], dim=1\n ) / config.temperature\n target_indices = torch.zeros(\n len(all_logits), dtype=torch.long, device=device\n )\n per_sample_loss = F.cross_entropy(\n all_logits, target_indices, reduction=\"none\"\n )\n elif config.loss_type == \"bpr\":\n diffs = positive_logits.unsqueeze(1) - negative_logits\n per_sample_loss = F.softplus(-diffs).mean(dim=1)\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 per_sample_loss = (positive_loss + negative_loss) / 2.0\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 * per_sample_loss\n ).sum()",
"instruction": "Update loss calculation in score_target_microbatches to support infonce and bpr as well as bce",
"old_string": " positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n 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()"
}
### Tool output · 2026-09-29T01:35:41.011Z
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
)
if config.loss_type == "infonce":
all_logits = torch.cat(
[positive_logits.unsqueeze(1), negative_logits], dim=1
) / config.temperature
target_indices = torch.zeros(
len(all_logits), dtype=torch.long, device=device
)
per_sample_loss = F.cross_entropy(
all_logits, target_indices, reduction="none"
)
elif config.loss_type == "bpr":
diffs = positive_logits.unsqueeze(1) - negative_logits
per_sample_loss = F.softplus(-diffs).mean(dim=1)
else:
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
per_sample_loss = (positive_loss + negative_loss) / 2.0
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * per_sample_loss
).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()
...
## Step 52 · agent · 2026-09-29T01:35:43.668Z
I updated `score_target_microbatches` to compute the loss according to `config.loss_type` (supporting InfoNCE, BPR, and BCE). Next, I will add the corresponding CLI arguments in `main()`.
### read_file · 2026-09-29T01:35:43.668Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1980,
"end_line": 2070
}
### Tool output · 2026-09-29T01:35:43.675Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1980-2070 of 2193 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) ---
)
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,
## Step 53 · agent · 2026-09-29T01:35:53.115Z
### Recorded reasoning summary
Implementing Parser Arguments
I'm currently integrating new parser arguments, like `--uniform-negatives` and `--inbatch-sampling`, and feeding them into the `Config` object. I'll then leverage `replace` within the training loop for these updates.
### replace · 2026-09-29T01:35:53.115Z
{
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py",
"new_string": " parser.add_argument(\"--uniform-negatives\", type=int, default=Config.uniform_negatives)\n parser.add_argument(\"--inbatch-negatives\", type=int, default=Config.inbatch_negatives)\n parser.add_argument(\"--inbatch-correction-power\", type=float, default=Config.inbatch_correction_power)\n parser.add_argument(\"--inbatch-sampling\", choices=(\"unique\", \"frequency\"), default=Config.inbatch_sampling)\n parser.add_argument(\"--loss-type\", choices=(\"bce\", \"infonce\", \"bpr\"), default=Config.loss_type)\n parser.add_argument(\"--temperature\", type=float, default=Config.temperature)\n 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 or args.uniform_negatives < 0\n or args.inbatch_negatives < 0\n or args.temperature <= 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 uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n inbatch_correction_power=args.inbatch_correction_power,\n inbatch_sampling=args.inbatch_sampling,\n loss_type=args.loss_type,\n temperature=args.temperature,\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,",
"instruction": "Add CLI flags and config fields for negative sampling and loss type",
"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,"
}
### Tool output · 2026-09-29T01:35:53.132Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
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("--inbatch-correction-power", type=float, default=Config.inbatch_correction_power)
parser.add_argument("--inbatch-sampling", choices=("unique", "frequency"), default=Config.inbatch_sampling)
parser.add_argument("--loss-type", choices=("bce", "infonce", "bpr"), default=Config.loss_type)
parser.add_argument("--temperature", type=float, default=Config.temperature)
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")
...
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.uniform_negatives < 0
or args.inbatch_negatives < 0
or args.temperature <= 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()
...
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,
inbatch_sampling=args.inbatch_sampling,
loss_type=args.loss_type,
temperature=args.temperature,
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,
...
## Step 54 · agent · 2026-09-29T01:35:56.154Z
I added the CLI arguments and configured `Config` to parse negative sampling parameters and loss types. Next, I will check the resume checkpoint logic to verify config compatibility.
### read_file · 2026-09-29T01:35:56.154Z
{
"start_line": 1590,
"end_line": 1640,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:35:56.161Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1590-1640 of 2208 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: 1641.
--- FILE CONTENT (truncated) ---
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 = (
## Step 55 · agent · 2026-09-29T01:35:59.583Z
I will check syntax of `train_current_model.py` with `python -m py_compile`.
### run_shell_command · 2026-09-29T01:35:59.583Z
{
"description": "Check syntax of train_current_model.py",
"dir_path": "/app/solution",
"command": "python -m py_compile /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:35:59.735Z
Output: (empty)
Process Group PGID: 1910
## Step 56 · agent · 2026-09-29T01:36:03.783Z
### Recorded reasoning summary
Testing Diagnosis Hypotheses
I'm now initiating Experiment 1 to directly test Diagnoses 1 and 2. This experiment will specifically involve disabling inverse-propensity downweighting for popular negatives by setting `--inbatch-correction-power 0.0`, and then proceeding to sample in-batch negatives proportionally.
I will run Experiment 1: `--inbatch-correction-power 0.0 --inbatch-sampling frequency` (BCE loss, 24 uniform, 8 inbatch) and evaluate with `evaluate_public.py`.
### run_shell_command · 2026-09-29T01:36:03.783Z
{
"description": "Run Experiment 1 with frequency inbatch sampling and power=0.0",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --inbatch-correction-power 0.0 \\\n --inbatch-sampling frequency \\\n --output /app/solution/exp1_validation.parquet && \\\npython /task/evaluate_public.py /app/solution/exp1_validation.parquet",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:41:00.476Z
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
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=0.558826 running_bce=0.571199 elapsed=178.0s
epoch=1/1 train_loss=0.56884 validation_loss=0.51997 pair_accuracy=0.751 train_targets=1,052,953 validation_targets=130,103 train_seconds=189.7 validation_seconds=15.2
restored best validation-BCE checkpoint: epoch=1 loss=0.519969
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_validation.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 288.919,
"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": 0.0,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"inbatch_sampling": "frequency",
"loss_type": "bce",
"temperature": 0.07,
"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": 0.5688422779714505,
"positive_logit": 0.5348457128254654,
"negative_logit": -0.6062991975737275,
"pair_accuracy": 0.7508769978337114,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 189.7411161729999,
"validation_loss": 0.5199690936254368,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.169301435999841
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.5344889163970947,
"query_norm_std": 0.8001477718353271,
"item_norm_mean": 1.8621972799301147,
"item_norm_std": 0.7460529208183289
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp1_validation.parquet",
"scores": {
"quality_ndcg@10": 0.011468,
"quality_users": 4263
}
}
Process Group PGID: 1913
## Step 57 · agent · 2026-09-29T01:41:05.908Z
I will inspect the popularity distribution of predictions in `exp1_validation.parquet`.
### run_shell_command · 2026-09-29T01:41:05.908Z
{
"command": "python3 -c \"\nimport pandas as pd\npred = pd.read_parquet('/app/solution/exp1_validation.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\npred['pop_rank'] = pred['item_id'].map(item_pop)\ntop10 = pred[pred['rank'] <= 10]\nprint('Top 10 predicted popularity ranks in exp1:')\nprint(top10['pop_rank'].describe())\n\"",
"description": "Inspect popularity of exp1 predictions",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:41:07.516Z
Output: Top 10 predicted popularity ranks in exp1:
count 84560.000000
mean 2279.055014
std 4761.738155
min 1.000000
25% 84.000000
50% 413.000000
75% 1666.000000
max 29978.000000
Name: pop_rank, dtype: float64
Process Group PGID: 1956
## Step 58 · agent · 2026-09-29T01:41:18.259Z
### Recorded reasoning summary
Analyzing Popularity Distribution
I'm focusing on understanding why popularity remains so concentrated in the top 400. My current investigation centers on the behavior of BCE with average negative loss. I'm examining how the average loss, calculated as `(positive_loss + negative_loss) / 2`, influences the positive dot product.
### Recorded reasoning summary
Investigating Training Objective
I'm shifting my focus to the training objective, specifically how the baseline selects negative examples. I'm planning to test InfoNCE loss to see its impact, considering the typical norms of query and item embeddings which suggest a dot product around 2.7.
### Recorded reasoning summary
Testing Temperature Variations
I'm exploring the impact of temperature on my model. My current thoughts are centered on how varying the temperature hyperparameter, especially for InfoNCE loss, might resolve the significant logit differences I'm observing. I'm considering testing temperatures like 0.2 or 0.5.
### Recorded reasoning summary
Testing InfoNCE with Modifications
I'm now considering how to adapt InfoNCE for my specific scenario. My current thinking involves integrating `unique` and `inbatch_correction_power=0.5` into InfoNCE, while also exploring different temperature values. This approach aims to address the observed logit differences effectively.
I will check git status and commit history in `/task` or `/app`.
### run_shell_command · 2026-09-29T01:41:18.259Z
{
"description": "Check git log in /task",
"dir_path": "/app/solution",
"command": "cd /task && git log -n 5 2>&1 || git status 2>&1"
}
### Tool output · 2026-09-29T01:41:18.278Z
Output: /usr/bin/bash: line 4: git: command not found
/usr/bin/bash: line 4: git: command not found
Exit Code: 127
Process Group PGID: 1975
## Step 59 · agent · 2026-09-29T01:41:20.899Z
I will search for comments in `train_current_model.py` to see architectural notes.
### run_shell_command · 2026-09-29T01:41:20.899Z
{
"dir_path": "/app/solution",
"command": "grep -n -E \"^\\s*#\" /task/current_model/train_current_model.py",
"description": "Search for comments in train_current_model.py"
}
### Tool output · 2026-09-29T01:41:20.916Z
Output: 1:#!/usr/bin/env python3
381: # Snapshot zero is the shared empty explicit state.
589: # T adjacent targets consume sequence positions 0 through T. The
590: # chronological BCE suffix starts at T + 1 and is intentionally absent.
707: # Rejected in-batch items become ordinary uniform draws.
1004: # Repeats remain training examples, but they cannot be
1005: # eligible discovery outcomes at the corresponding
1006: # cutoff and therefore keep only the ordinary weight.
Process Group PGID: 1979
## Step 60 · agent · 2026-09-29T01:41:23.868Z
I will search for comments in `two_tower_model.py`.
### run_shell_command · 2026-09-29T01:41:23.868Z
{
"description": "Search for comments in two_tower_model.py",
"dir_path": "/app/solution",
"command": "grep -n -E \"^\\s*#\" /task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T01:41:23.885Z
Output: (empty)
Exit Code: 1
Process Group PGID: 1982
## Step 61 · agent · 2026-09-29T01:41:25.932Z
### read_file · 2026-09-29T01:41:25.932Z
{
"start_line": 1,
"end_line": 150,
"file_path": "/app/solution/ranking_evaluation.py"
}
### Tool output · 2026-09-29T01:41:28.030Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 232 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 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))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
## Step 62 · agent · 2026-09-29T01:41:30.528Z
### read_file · 2026-09-29T01:41:30.528Z
{
"file_path": "/app/solution/ranking_evaluation.py",
"end_line": 232,
"start_line": 151
}
### Tool output · 2026-09-29T01:41:30.536Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-232 of 232 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: 233.
--- FILE CONTENT (truncated) ---
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
## Step 63 · agent · 2026-09-29T01:41:35.097Z
### Recorded reasoning summary
Investigating Anomalous Results
I'm currently analyzing a discrepancy where a specific `inbatch_sampling` configuration with `inbatch_correction_power 0.0` yielded a significantly lower NDCG@10 score than the baseline. My immediate focus is to dissect the baseline's setup to understand the root cause of this unexpected performance drop.
I will run an experiment with `--target-weighting discovery-graded` using the baseline negative sampling settings.
### run_shell_command · 2026-09-29T01:41:35.097Z
{
"dir_path": "/app/solution",
"description": "Run experiment with discovery-graded target weighting",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp_disc_val.parquet && \\\npython /task/evaluate_public.py /app/solution/exp_disc_val.parquet"
}
### Tool output · 2026-09-29T01:46:59.504Z
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=196.2s
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=209.3 validation_seconds=15.8
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/exp_disc_val.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 316.643,
"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,
"inbatch_sampling": "unique",
"loss_type": "bce",
"temperature": 0.07,
"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": 0.5421154198798847,
"positive_logit": 0.7774311401831406,
"negative_logit": -0.6407561996238257,
"pair_accuracy": 0.7602795720701684,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.33062915000005,
"validation_loss": 0.5048305736570056,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.797680388000117
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7521852254867554,
"query_norm_std": 0.8638167381286621,
"item_norm_mean": 1.8543914556503296,
"item_norm_std": 0.8459439277648926
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp_disc_val.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 1985
## Step 64 · agent · 2026-09-29T01:47:12.601Z
### Recorded reasoning summary
Observing Model Improvements
My analysis shows a significant boost in NDCG@10, increasing by over 10% with the `discovery-graded` weighting. I'm now turning my attention to negative sampling and prompt instructions to further refine performance.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now deeply exploring how negative examples are selected and utilized by the current trainer. My immediate focus is on diagnosing limitations within the existing negative-sampling strategy and devising improvements, all while respecting the fixed CPU budget. I'm also investigating the impact of varying the balance between uniform and in-batch negatives and fine-tuning the in-batch correction power.
### Recorded reasoning summary
Refining Negatives
I'm scrutinizing `inbatch_correction_power`, particularly how different exponents affect item weighting and the implications of negative values. I'm also re-evaluating the performance drop from `inbatch_sampling frequency`, suspecting it's due to penalizing highly popular items that are also present in ground truth.
I will test InfoNCE loss with temperature 0.5 and discovery-graded target weighting.
### run_shell_command · 2026-09-29T01:47:12.601Z
{
"description": "Run InfoNCE loss with temperature 0.5",
"dir_path": "/app/solution",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --target-weighting discovery-graded \\\n --loss-type infonce \\\n --temperature 0.5 \\\n --output /app/solution/exp_infonce_05.parquet && \\\npython /task/evaluate_public.py /app/solution/exp_infonce_05.parquet"
}
### Tool output · 2026-09-29T01:52:36.485Z
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=2.896681 running_bce=2.895069 elapsed=196.0s
epoch=1/1 train_loss=2.88386 validation_loss=2.69833 pair_accuracy=0.767 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.1 validation_seconds=15.8
restored best validation-BCE checkpoint: epoch=1 loss=2.698328
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/exp_infonce_05.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 316.203,
"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,
"inbatch_sampling": "unique",
"loss_type": "infonce",
"temperature": 0.5,
"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": 2.8838567967821995,
"positive_logit": 0.4339510783399619,
"negative_logit": -0.25847992199584063,
"pair_accuracy": 0.7666582696473632,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.05512949100012,
"validation_loss": 2.6983276707117687,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.762151709000136
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.2678135633468628,
"query_norm_std": 0.4623965620994568,
"item_norm_mean": 1.1715781688690186,
"item_norm_std": 0.4589647948741913
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp_infonce_05.parquet",
"scores": {
"quality_ndcg@10": 0.017358,
"quality_users": 4263
}
}
Process Group PGID: 2036
## Step 65 · agent · 2026-09-29T01:52:44.675Z
### Recorded reasoning summary
Analyzing Loss Functions
My recent focus has been on contrasting InfoNCE and BCE loss. While InfoNCE yielded a result of 0.017358 at $\tau=0.5$, BCE with discovery-graded weights showed a slight increase to 0.023522 compared to the baseline's 0.02128. I'm now delving into the impact of negative sampling parameters on BCE.
### grep_search · 2026-09-29T01:52:44.675Z
{
"file_path": "/app/solution/train_current_model.py",
"pattern": "negative_filter_scope"
}
### Tool output · 2026-09-29T01:52:44.697Z
Found 9 matches for pattern "negative_filter_scope" in the workspace directory:
---
File: solution/train_current_model.py
L64: negative_filter_scope: str = "full-training-history"
L749: if config.negative_filter_scope == "full-training-history":
L1595: if config.negative_filter_scope == "full-training-history"
L1629: "negative_filter_scope", "last-100"
L1657: stored_filter_scope != config.negative_filter_scope
L1688: f"{stored_filter_scope}->{config.negative_filter_scope}, "
L1974: default=Config.negative_filter_scope,
L2077: negative_filter_scope=args.negative_filter_scope,
L2182: "false_negative_filter": config.negative_filter_scope,
---
## Step 66 · agent · 2026-09-29T01:52:51.984Z
### Recorded reasoning summary
Exploring Negative Filter Scopes
I'm currently investigating the impact of varying `config.negative_filter_scope`, specifically when it's set to something other than "full-training-history," like "last-100." My focus is on observing the behavior around line 749 in `train_current_model.py` and its function, `draw_training_negatives`.
### Recorded reasoning summary
Analyzing Negative Filter Logic
I'm currently examining how the `config.negative_filter_scope` setting, particularly when not "full-training-history" but rather a limited window like "last-100," influences the behavior within the `draw_training_negatives` function. My analysis is centered on line 749 of `train_current_model.py`.
I will run an experiment with 16 uniform negatives and 16 in-batch negatives (`--uniform-negatives 16 --inbatch-negatives 16 --target-weighting discovery-graded`).
### run_shell_command · 2026-09-29T01:52:51.984Z
{
"description": "Test 16 uniform and 16 inbatch negatives",
"dir_path": "/app/solution",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 16 \\\n --inbatch-negatives 16 \\\n --output /app/solution/exp_u16_i16.parquet && \\\npython /task/evaluate_public.py /app/solution/exp_u16_i16.parquet"
}
### Tool output · 2026-09-29T01:58:07.286Z
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=0.556066 running_bce=0.556343 elapsed=189.4s
epoch=1/1 train_loss=0.55431 validation_loss=0.52006 pair_accuracy=0.726 train_targets=1,052,953 validation_targets=130,103 train_seconds=202.1 validation_seconds=13.7
restored best validation-BCE checkpoint: epoch=1 loss=0.520058
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/exp_u16_i16.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 307.519,
"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": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"inbatch_sampling": "unique",
"loss_type": "bce",
"temperature": 0.07,
"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": 0.5543083913050508,
"positive_logit": 0.6960524614172641,
"negative_logit": -0.5012892200416754,
"pair_accuracy": 0.7255772871628648,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 202.12939450899967,
"validation_loss": 0.5200578126173041,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 13.726802019999923
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7176125049591064,
"query_norm_std": 0.8431687951087952,
"item_norm_mean": 1.8370518684387207,
"item_norm_std": 0.7449613809585571
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp_u16_i16.parquet",
"scores": {
"quality_ndcg@10": 0.020984,
"quality_users": 4263
}
}
Process Group PGID: 2087
## Step 67 · agent · 2026-09-29T01:58:17.284Z
### Recorded reasoning summary
Investigating Negative Sampling
My recent exploration into negative sampling has yielded some intriguing results. I've observed an unexpected performance increase when using a larger proportion of uniform negatives (24 vs 16) in the sampling strategy, which prompts further investigation into the effects of even more uniform negatives, such as a 32-to-8 ratio.
I will inspect the values of `item_correction` across different power values.
### run_shell_command · 2026-09-29T01:58:17.284Z
{
"dir_path": "/app/solution",
"description": "Inspect item_correction distributions for various powers",
"command": "python3 -c \"\nimport numpy as np\n\ncache = np.load('/app/solution/cache_val.npz')\nitems = cache['sequence_items']\noffsets = cache['offsets']\nlengths = np.diff(offsets)\ntotals = np.maximum(lengths - 1, 0)\nval_counts = np.floor(totals * 0.1).astype(int)\nval_counts[(totals >= 2) & (val_counts == 0)] = 1\nval_counts = np.minimum(val_counts, 16)\ntrain_counts = np.minimum(totals - val_counts, 128)\nwindow_starts = totals - val_counts - train_counts\n\ncounts = np.zeros(len(cache['item_ids']), dtype=np.float64)\nfor u in range(len(train_counts)):\n c = train_counts[u]\n if not c: continue\n off = offsets[u]\n ws = window_starts[u]\n counts += np.bincount(items[off + ws + 1 : off + ws + c + 1], minlength=len(cache['item_ids']))\n\ntotal = counts.sum()\nprobs = (counts + 1.0) / (total + len(counts))\n\nfor power in [0.0, 0.25, 0.5, 0.75, 1.0]:\n raw = np.power((1.0 / len(counts)) / probs, power)\n norm = np.sum(counts * raw) / total\n corr = np.clip(raw / norm, 0.25, 4.0)\n print(f'power={power}: min={corr.min():.3f}, mean_on_targets={(counts*corr).sum()/total:.3f}, max={corr.max():.3f}, q25={np.percentile(corr, 25):.3f}, q50={np.percentile(corr, 50):.3f}, q75={np.percentile(corr, 75):.3f}')\n\""
}
### Tool output · 2026-09-29T01:58:21.863Z
Output: power=0.0: min=1.000, mean_on_targets=1.000, max=1.000, q25=1.000, q50=1.000, q75=1.000
power=0.25: min=0.381, mean_on_targets=1.000, max=2.806, q25=1.138, q50=1.363, q75=1.578
power=0.5: min=0.250, mean_on_targets=1.001, max=4.000, q25=1.187, q50=1.702, q75=2.284
power=0.75: min=0.250, mean_on_targets=0.998, max=4.000, q25=1.147, q50=1.969, q75=3.060
power=1.0: min=0.250, mean_on_targets=0.971, max=4.000, q25=1.035, q50=2.128, q75=3.831
Process Group PGID: 2139
## Step 68 · agent · 2026-09-29T01:58:30.604Z
### Recorded reasoning summary
Exploring Correction Mechanisms
I'm currently investigating the rationale behind introducing `item_correction`. My focus is on understanding how it addresses potential biases, particularly concerning popular items sampled as negatives. This analysis aims to clarify the impact of such corrections on the recommendation system's performance.
### Recorded reasoning summary
Analyzing Loss Components
I've been examining the `corrected_negative_loss` function and observed how `weights.sum(dim=1)` normalizes the weights. This normalization suggests that `weights` primarily re-weights the relative importance of in-batch negatives rather than affecting their absolute contribution.
I will inspect the predictions of `exp_disc_val.parquet` to see their popularity and overlap with true validation items.
### run_shell_command · 2026-09-29T01:58:30.604Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npred = pd.read_parquet('/app/solution/exp_disc_val.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\npred['pop_rank'] = pred['item_id'].map(item_pop)\n\ntop10 = pred[pred['rank'] <= 10]\nprint('Top 10 predicted popularity ranks in exp_disc:')\nprint(top10['pop_rank'].describe())\n\nprint('Unique items in top 10:', top10['item_id'].nunique())\nprint('Top 5 most frequently predicted items in top 10:')\nprint(top10['item_id'].value_counts().head(5))\n\"",
"dir_path": "/app/solution",
"description": "Analyze top 10 predictions in exp_disc"
}
### Tool output · 2026-09-29T01:58:32.223Z
Output: Top 10 predicted popularity ranks in exp_disc:
count 84560.000000
mean 2940.454991
std 5733.564101
min 1.000000
25% 19.000000
50% 193.000000
75% 2947.000000
max 29925.000000
Name: pop_rank, dtype: float64
Unique items in top 10: 632
Top 5 most frequently predicted items in top 10:
item_id
8722394 2676
5436843 2651
6006390 2590
8743390 2280
8213481 2245
Name: count, dtype: int64
Process Group PGID: 2149
## Step 69 · agent · 2026-09-29T01:58:37.924Z
### Recorded reasoning summary
Analyzing Recommendation Distribution
I'm digging into the distribution of recommendations. It's striking that so few unique items are appearing in the top 10 across many users. The concentration of recommendations for a single item suggests a potential for over-saturation, while others are barely getting a look in.
I will inspect the norm and characteristics of the most frequently recommended items.
### run_shell_command · 2026-09-29T01:58:37.924Z
{
"description": "Inspect top recommended items metadata",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\nfor item in [8722394, 5436843, 6006390, 8743390, 8213481]:\n row = catalog[catalog.item_id == item]\n print(item, 'pop_rank:', item_pop.get(item), 'row:', row.to_dict('records'))\n\"",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:58:39.483Z
Output: 8722394 pop_rank: 634 row: [{'item_id': 8722394, 'positive_listens': 3464, 'organic_positive_listens': 1695, 'popularity_rank': 634}]
5436843 pop_rank: 14 row: [{'item_id': 5436843, 'positive_listens': 14897, 'organic_positive_listens': 12127, 'popularity_rank': 14}]
6006390 pop_rank: 75 row: [{'item_id': 6006390, 'positive_listens': 8970, 'organic_positive_listens': 6813, 'popularity_rank': 75}]
8743390 pop_rank: 22 row: [{'item_id': 8743390, 'positive_listens': 13518, 'organic_positive_listens': 8480, 'popularity_rank': 22}]
8213481 pop_rank: 7 row: [{'item_id': 8213481, 'positive_listens': 22505, 'organic_positive_listens': 17071, 'popularity_rank': 7}]
Process Group PGID: 2167
## Step 70 · agent · 2026-09-29T01:58:50.464Z
### Recorded reasoning summary
Investigating Item Features
I'm currently dissecting the `build_item_features` function to understand why certain highly popular tracks are being recommended. My focus is on identifying the specific features that contribute to their broad appeal.
### Recorded reasoning summary
Analyzing Item Encoding
I've been examining how items are encoded by combining their embeddings with derived features. Specifically, I'm tracing the inputs to the `item_tower`, noting the concatenation of candidate embeddings and the four calculated item features.
### Recorded reasoning summary
Exploring Dot Product Behavior
I'm re-evaluating how item vectors interact with user queries. Specifically, I'm investigating if a large item vector norm could artificially inflate its dot product scores across many queries. I'm setting up a test script to experiment with cosine similarity versus raw dot product and analyze vector properties.
### Recorded reasoning summary
Evaluating Training Dynamics
I'm focusing on the training objective, particularly how negative examples are selected and utilized. My current thought is to investigate limitations within the existing negative-sampling strategy. I will run `train_current_model.py` with the specified checkpoint and target weighting, and I'm paying close attention to the production context regarding latency and offline precomputation of item vectors.
### Recorded reasoning summary
Refining Negative Sampling
I'm now deeply immersed in the training objective, specifically scrutinizing the mechanics of negative example selection and their impact. My primary effort is directed towards diagnosing and enhancing the current negative-sampling strategy, keeping the fixed CPU budget in sharp focus.
### read_file · 2026-09-29T01:58:50.464Z
{
"end_line": 725,
"file_path": "/app/solution/train_current_model.py",
"start_line": 620
}
### Tool output · 2026-09-29T01:58:50.472Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 620-725 of 2208 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: 726.
--- FILE CONTENT (truncated) ---
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:
if config.inbatch_sampling == "frequency":
indices = torch.randint(
batch_size,
(batch_size, config.inbatch_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.from_numpy(targets_cpu)[indices]
corrected_count = config.inbatch_negatives
else:
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 and config.inbatch_correction_power != 0.0:
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,
## Step 71 · agent · 2026-09-29T01:59:00.350Z
### Recorded reasoning summary
Analyzing Invalid Patterns
I'm currently examining a loop that iterates 32 times, focusing on identifying invalid patterns within a full history of negatives. This involves comparing user data and known positives to pinpoint anomalies.
### Recorded reasoning summary
Refining Replacement Logic
I've identified that invalid in-batch negatives are being uniformly replaced, with the correction factor set to 1.0. I'm now analyzing the implications of this, particularly considering the prevalence of popular music among active listeners and how that might skew these random replacements.
I will check how many in-batch negatives are flagged invalid and replaced during training.
### run_shell_command · 2026-09-29T01:59:00.350Z
{
"dir_path": "/app/solution",
"description": "Measure fraction of inbatch negatives that hit known history",
"command": "python3 -c \"\nimport numpy as np\nimport torch\n\ncache = np.load('/app/solution/cache_val.npz')\nitems = cache['sequence_items']\noffsets = cache['offsets']\nlengths = np.diff(offsets)\ntotals = np.maximum(lengths - 1, 0)\nval_counts = np.floor(totals * 0.1).astype(int)\nval_counts[(totals >= 2) & (val_counts == 0)] = 1\nval_counts = np.minimum(val_counts, 16)\ntrain_counts = np.minimum(totals - val_counts, 128)\nwindow_starts = totals - val_counts - train_counts\n\n# build known_positive_packed\nwidth = (30000 + 7) // 8\npacked = np.zeros((len(offsets)-1, width), dtype=np.uint8)\nfor u in range(len(train_counts)):\n c = train_counts[u]\n if not c: continue\n off = offsets[u]\n seq_items = np.unique(items[off : off + c + 1])\n np.bitwise_or.at(packed[u], seq_items >> 3, (1 << (seq_items & 7)).astype(np.uint8))\n\n# Simulate 1 block of 64 users, 100 steps -> 6400 targets\ntargets = []\nuser_rows = []\nfor u in range(64):\n c = min(100, train_counts[u])\n off = offsets[u]\n ws = window_starts[u]\n t = items[off + ws + 1 : off + ws + c + 1]\n targets.extend(t)\n user_rows.extend([u]*len(t))\n\ntargets = np.array(targets)\nuser_rows = np.array(user_rows)\nprint('Simulated batch size:', len(targets))\n\n# Draw 8 inbatch negatives using unique_targets\nunique_targets = np.unique(targets)\ninbatch_negatives = 8\npriorities = np.random.rand(len(targets), len(unique_targets))\nfor i in range(len(targets)):\n priorities[i, unique_targets == targets[i]] = -1.0\n\ncols = np.argsort(-priorities, axis=1)[:, :inbatch_negatives]\ninbatch = unique_targets[cols]\n\n# Check how many are in known_positives\nbyte_cols = inbatch >> 3\nbit_cols = inbatch & 7\nknown = packed[user_rows[:, None], byte_cols]\nis_known = ((known >> bit_cols) & 1).astype(bool)\nprint('Inbatch negatives in known history:', is_known.mean())\n\""
}
### Tool output · 2026-09-29T01:59:03.953Z
Output: Simulated batch size: 5644
Inbatch negatives in known history: 0.010077072997873849
Process Group PGID: 2184
## Step 72 · agent · 2026-09-29T01:59:24.729Z
### Recorded reasoning summary
Analyzing Negative Loss Correction
I'm focusing on the `corrected_negative_loss` function. Given that only a tiny fraction of inbatch negatives are likely historical misses, the risk of false negative collision appears minimal. My current task is to delve deeper into this specific function's mechanics and implications.
### Recorded reasoning summary
Investigating Weight Application
I'm looking closely at how the `weights` variable, derived from `correction` and tied to specific in-batch negatives, is applied. I need to understand its precise role and origin within the `draw_full_history_filtered_negatives` process to ensure accurate loss correction.
### Recorded reasoning summary
Clarifying Correction Weights
I'm trying to understand the `item_correction` calculation and how the derived `weights` are applied. My current focus is on a specific scenario where unpopular items receive significantly more weight (16x) than popular items in the in-batch loss calculation. This seems counterintuitive and prompts me to re-examine the underlying literature.
### Recorded reasoning summary
Understanding Logit Adjustment
I'm realizing that subtracting $\log Q(j)$ from the logit during training effectively penalizes popular items, decoupling their learned representations from mere popularity. This contrasts with the baseline, which downweighted popular items.
### Recorded reasoning summary
Revising Negative Sampling
I'm realizing my previous understanding was inverted. The correction actually amplifies the loss for popular items when they are sampled as negatives, rather than penalizing them. This significantly changes how I should approach the negative sampling strategy.
### Recorded reasoning summary
Re-evaluating Sampling Logic
I'm realizing the standard in-batch negative sampling doesn't inherently sample proportional to popularity. My initial assumption that popular items were over-represented as negatives was flawed due to uniform sampling. This changes how I'm viewing the impact of `item_correction`.
### Recorded reasoning summary
Reviewing Past Experiments
I'm revisiting Experiment 1's setup: `inbatch_sampling` set to "frequency" with `power` at 0.0, which resulted in a significant score drop. My current focus is understanding *why* this happened, particularly how sampling tokens from the entire block, including a user's own tokens, impacts negative selection and correction.
### Recorded reasoning summary
Analyzing Sampling Logic
I'm realizing that my assumption about how in-batch negative sampling works was inverted. It appears that when a negative sample happens to be one of the user's own positive items, it's replaced by a uniformly random item, which has significant implications for my analysis.
I will run an experiment with 32 uniform negatives and 8 in-batch negatives with discovery-graded weighting.
### run_shell_command · 2026-09-29T01:59:24.729Z
{
"description": "Test 32 uniform and 8 inbatch negatives",
"dir_path": "/app/solution",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 32 \\\n --inbatch-negatives 8 \\\n --output /app/solution/exp_u32_i8.parquet && \\\npython /task/evaluate_public.py /app/solution/exp_u32_i8.parquet"
}
### Tool output · 2026-09-29T02:05:04.288Z
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=0.553734 running_bce=0.541651 elapsed=210.9s
epoch=1/1 train_loss=0.53968 validation_loss=0.50096 pair_accuracy=0.767 train_targets=1,052,953 validation_targets=130,103 train_seconds=224.9 validation_seconds=17.1
restored best validation-BCE checkpoint: epoch=1 loss=0.500963
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/exp_u32_i8.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 331.849,
"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": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"inbatch_sampling": "unique",
"loss_type": "bce",
"temperature": 0.07,
"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": [
{
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"loss": 0.5396840075750924,
"positive_logit": 0.7991228782080911,
"negative_logit": -0.666870657387235,
"pair_accuracy": 0.7670769730462803,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 224.8658826320002,
"validation_loss": 0.5009625129150823,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
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],
"embedding_diagnostics": {
"query_norm_mean": 1.5185474157333374,
"query_norm_std": 0.6887362599372864,
"item_norm_mean": 1.9282952547073364,
"item_norm_std": 0.830059289932251
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"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
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"scores": {
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"quality_users": 4263
}
}
Process Group PGID: 2194
## Step 73 · agent · 2026-09-29T02:05:27.366Z
### Recorded reasoning summary
Testing Hyperparameters Now
I'm currently diving into the impact of varying `uniform_negatives` and `inbatch_negatives`, specifically testing combinations like 24 and 4 respectively. I'm also keen to explore the effect of `inbatch_correction_power`, with initial tests at 0.75 and 1.0.
I will test `--inbatch-correction-power 0.75` with `target-weighting discovery-graded`.
### run_shell_command · 2026-09-29T02:05:27.366Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz \\\n --target-weighting discovery-graded \\\n --inbatch-correction-power 0.75 \\\n --output /app/solution/exp_power_075.parquet && \\\npython /task/evaluate_public.py /app/solution/exp_power_075.parquet",
"dir_path": "/app/solution",
"description": "Test inbatch-correction-power 0.75"
}
### Tool output · 2026-09-29T02:10:49.200Z
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=0.998 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=0.544805 running_bce=0.537089 elapsed=196.3s
epoch=1/1 train_loss=0.53509 validation_loss=0.49793 pair_accuracy=0.759 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.4 validation_seconds=15.5
restored best validation-BCE checkpoint: epoch=1 loss=0.497935
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/exp_power_075.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 314.107,
"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": 0.75,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"inbatch_sampling": "unique",
"loss_type": "bce",
"temperature": 0.07,
"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": [
{
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"loss": 0.5350934987891478,
"positive_logit": 0.8239995633075552,
"negative_logit": -0.627545965057021,
"pair_accuracy": 0.7592069636536484,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.39203050000015,
"validation_loss": 0.4979345806938763,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.500389529000131
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],
"embedding_diagnostics": {
"query_norm_mean": 1.6196733713150024,
"query_norm_std": 0.7711300849914551,
"item_norm_mean": 1.921000599861145,
"item_norm_std": 0.8289750814437866
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"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp_power_075.parquet",
"scores": {
"quality_ndcg@10": 0.022358,
"quality_users": 4263
}
}
Process Group PGID: 2243
## Step 74 · agent · 2026-09-29T02:11:53.244Z
### Recorded reasoning summary
Testing Hyperparameters Further
I'm continuing to explore the parameter space, moving from the initial power adjustments to now investigating the impact of varying `uniform_negatives` and `inbatch_negatives`. I'm systematically testing different combinations to refine performance.
### Recorded reasoning summary
Incorporating Dislikes
I'm now considering the impact of explicit user dislikes. Over 60% of users have provided dislikes, and I believe these can serve as powerful hard negative examples. I'm exploring how to leverage these confirmed negative interactions during training to refine the model's understanding of user preferences.
### Recorded reasoning summary
Revisiting Negatives Now
I've realized that the user tower already incorporates explicit dislikes as an input feature, so my previous focus was slightly off. Now, I'm stepping back to more broadly investigate all available negative sampling strategies within our CPU constraints.
### Recorded reasoning summary
Evaluating Negative Sampling
I'm now systematically evaluating several negative sampling strategies: uniform, in-batch, popularity-biased, and dynamic hard negatives. My focus is on how these interact with our loss functions, particularly `corrected_negative_loss`, and ensuring false positives aren't inadvertently treated as negatives.
### Recorded reasoning summary
Refining Negative Scoring
I'm re-examining `score_target_microbatches`, specifically lines 1175-1190. My current focus is on how `corrected_negative_loss` is being applied to negative logits and their associated weights. This feels like a critical area for optimization.
### Recorded reasoning summary
Adjusting Loss Weights
I'm questioning the simple averaging of positive and negative losses. I'm exploring the impact of introducing a tunable lambda parameter to weight the negative loss independently, considering that the mean of negatives might be a more direct measure of true negatives than the raw softplus.
### Recorded reasoning summary
Focusing on Hardest Negatives
I'm now concentrating on identifying and prioritizing the hardest negatives within each batch, rather than averaging all of them. The hypothesis is that focusing gradients on these more challenging examples will drive more significant improvements in retrieval ranking, mirroring strategies from metric learning.
### Recorded reasoning summary
Investigating Training Length
I've discovered a critical oversight: the baseline configuration only trains for a single epoch with an early stopping patience of one. This severely limited the model's learning, particularly with InfoNCE loss, which showed minimal improvement within that single epoch. I'm now assessing how many epochs we can realistically train within our time budget.
I will test running 3 epochs with `--target-weighting discovery-graded` to see if additional epochs improve validation performance.
## Recorded session usage
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