yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-r04-b9049e57
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T02:03:56.271Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
## Step 2 · user · 2026-09-29T02:03:56.313Z
# Improve a production music-discovery two-tower
You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team's next launch gate.
Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.
The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.
## Production Context
The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.
The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.
## What You Have
All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` window. For the final submission, that
validation window becomes observed history, and another 30-minute gap precedes
the hidden seven-day outcome window.
The package includes:
- `/task/current_model/train_current_model.py`: the production training and
ranking script. It can reproduce the public validation ranking and produce a
hidden-test submission after refitting.
- `/task/current_model/two_tower_model.py`: model primitives used by the
production script.
- `/task/evaluate_public.py`: the public validation evaluator for development.
- `/task/data/validation_eligible_candidates.npz` and
`/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for
the validation and hidden-test cutoffs.
- `/task/data/validation_preference_state.parquet` and
`/task/data/test_preference_state.parquet`: active explicit preference state
at the corresponding cutoffs.
- `/task/requirements.txt`: the exact offline Python distribution inventory.
You can reproduce the production public backtest with:
```sh
python /task/current_model/train_current_model.py \
--stage validation \
--public-dir /task \
--output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet
```
## Data
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action came
through a recommendation surface. A listen is a conventional positive at 50%
completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. For model development and selection, train only from `train_*` and score the
subsequent `val_*` outcomes.
2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
Validation candidates exclude all `train_*` encounters. Test candidates exclude
all `train_*` and `val_*` encounters. Future test interactions are hidden and
used only by the separate Harbor verifier.
## Discovery Objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.
## Deliverable
Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.
The ranking must:
- include the columns `uid`, `item_id`, and `rank`; only these columns are
evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
candidate set.
Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model-provider
connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
the deadline.
## Step 3 · agent · 2026-09-29T02:04:10.785Z
### Recorded reasoning summary
Exploring Model Enhancements
My current focus is on dissecting the existing two-tower retrieval model for production music discovery. I'm delving into the problem statement, identifying key constraints, and clarifying the expected deliverables. Concurrently, I'm conducting an in-depth review of the current codebase to lay the groundwork for improvements.
### Recorded reasoning summary
Investigating Negative Sampling
I'm currently deep-diving into the production music retrieval model, specifically analyzing its negative sampling strategy. My aim is to pinpoint limitations within the current approach and devise improvements, all while adhering to the specified CPU budget and environment constraints.
### update_topic · 2026-09-29T02:04:10.785Z
{
"title": "Baseline Investigation and Benchmarking",
"strategic_intent": "Explore the codebase, inspect the files in /task, and run the baseline validation backtest to establish the initial benchmark.",
"summary": "Starting the investigation into the production two-tower music recommendation model. We will inspect /task files, understand the data structures and baseline performance, and run the baseline validation evaluation."
}
### Tool output · 2026-09-29T02:04:10.794Z
Current topic: "Baseline Investigation and Benchmarking"
Topic summary: Starting the investigation into the production two-tower music recommendation model. We will inspect /task files, understand the data structures and baseline performance, and run the baseline validation evaluation.
Strategic Intent: Explore the codebase, inspect the files in /task, and run the baseline validation backtest to establish the initial benchmark.
## Step 4 · agent · 2026-09-29T02:04:12.560Z
### list_directory · 2026-09-29T02:04:12.560Z
{
"dir_path": "/task"
}
### Tool error · 2026-09-29T02:04:12.563Z
Path not in workspace: Attempted path "/task" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app
## Step 5 · agent · 2026-09-29T02:04:15.416Z
### Recorded reasoning summary
Investigating File Access
I'm currently exploring the limitations of file system access, specifically whether functions like `list_directory` and `read_file` are restricted to the `/app` directory or the agent's temporary directory. I'll be checking the behavior of `read_file` and `run_shell_command` to confirm these potential constraints.
### run_shell_command · 2026-09-29T02:04:15.416Z
{
"command": "ls -la /task && ls -la /task/current_model && ls -la /task/data",
"description": "List files in /task and subdirectories to inspect repository structure"
}
### Tool output · 2026-09-29T02:04:15.448Z
Output: total 40
dr-xr-xr-x 1 root root 4096 Sep 29 02:00 .
drwxr-xr-x 1 root root 4096 Sep 29 02:03 ..
-r--r--r-- 1 root root 0 Sep 29 01:58 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:58 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 01:59 data
-r--r--r-- 1 root root 5774 Sep 29 01:58 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:58 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:58 requirements.txt
total 104
dr-xr-xr-x 1 root root 4096 Sep 29 01:58 .
dr-xr-xr-x 1 root root 4096 Sep 29 02:00 ..
-r--r--r-- 1 root root 80058 Sep 29 01:58 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 01:58 two_tower_model.py
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:59 .
dr-xr-xr-x 1 root root 4096 Sep 29 02:00 ..
-r--r--r-- 1 root root 349352 Sep 29 01:58 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:58 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:58 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:59 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:58 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:58 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:58 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:58 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:58 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:58 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:58 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:58 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:58 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:58 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:58 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:59 validation_preference_state.parquet
Process Group PGID: 1612
## Step 6 · agent · 2026-09-29T02:04:18.215Z
### Recorded reasoning summary
Exploring Model Training
I'm currently examining the Python scripts within the `/task/` directory, specifically focusing on `train_current_model.py` and `two_tower_model.py`. My immediate goal is to understand the model's architecture and training procedures. I will then proceed to copy relevant code segments.
### run_shell_command · 2026-09-29T02:04:18.215Z
{
"description": "Copy current model files to /app/solution and verify",
"command": "cp -r /task/current_model/* /app/solution/ && ls -la /app/solution"
}
### Tool output · 2026-09-29T02:04:18.232Z
Output: total 108
drwxr-xr-x 1 agent agent 4096 Sep 29 02:04 .
drwxr-xr-x 1 agent agent 4096 Sep 29 02:00 ..
-r--r--r-- 1 agent agent 80058 Sep 29 02:04 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 02:04 two_tower_model.py
Process Group PGID: 1617
## Step 7 · agent · 2026-09-29T02:04:21.615Z
### run_shell_command · 2026-09-29T02:04:21.615Z
{
"description": "Make files writable in /app/solution",
"command": "chmod u+w /app/solution/* && ls -la /app/solution"
}
### Tool output · 2026-09-29T02:04:21.631Z
Output: total 108
drwxr-xr-x 1 agent agent 4096 Sep 29 02:04 .
drwxr-xr-x 1 agent agent 4096 Sep 29 02:00 ..
-rw-r--r-- 1 agent agent 80058 Sep 29 02:04 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 29 02:04 two_tower_model.py
Process Group PGID: 1621
## Step 8 · agent · 2026-09-29T02:04:23.666Z
### run_shell_command · 2026-09-29T02:04:23.666Z
{
"command": "lscpu | grep \"CPU(s):\\|Model name\" && free -h",
"description": "Check CPU cores and available memory"
}
### Tool output · 2026-09-29T02:04:23.686Z
Output: CPU(s): 8
Model name: AMD EPYC 7R13 Processor
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1625
## Step 9 · agent · 2026-09-29T02:04:25.485Z
### run_shell_command · 2026-09-29T02:04:25.485Z
{
"description": "Check memory via /proc/meminfo",
"command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\""
}
### Tool output · 2026-09-29T02:04:25.498Z
Output: MemTotal: 15988968 kB
MemAvailable: 14513188 kB
Process Group PGID: 1630
## Step 10 · agent · 2026-09-29T02:04:27.455Z
### run_shell_command · 2026-09-29T02:04:27.455Z
{
"description": "Check Python and PyTorch version and default threads",
"command": "python3 --version && python3 -c \"import torch; print(torch.__version__, torch.get_num_threads())\""
}
### Tool output · 2026-09-29T02:04:30.400Z
Output: Python 3.12.13
2.8.0+cpu 4
Process Group PGID: 1634
## Step 11 · agent · 2026-09-29T02:04:32.439Z
### run_shell_command · 2026-09-29T02:04:32.439Z
{
"description": "Inspect evaluate_public.py",
"command": "cat /task/evaluate_public.py"
}
### Tool output · 2026-09-29T02:04:32.451Z
Output: #!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
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 "
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1645
## Step 12 · agent · 2026-09-29T02:04:34.095Z
### run_shell_command · 2026-09-29T02:04:34.095Z
{
"command": "cat /task/ranking_evaluation.py",
"description": "Inspect ranking_evaluation.py"
}
### Tool output · 2026-09-29T02:04:34.109Z
Output: """Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
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"
)
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),
}
Process Group PGID: 1648
## Step 13 · agent · 2026-09-29T02:04:36.463Z
### run_shell_command · 2026-09-29T02:04:36.463Z
{
"description": "Check line counts of model and training script",
"command": "wc -l /app/solution/two_tower_model.py /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:04:36.476Z
Output: 433 /app/solution/two_tower_model.py
2164 /app/solution/train_current_model.py
2597 total
Process Group PGID: 1651
## Step 14 · agent · 2026-09-29T02:04:38.843Z
### Recorded reasoning summary
Running Baseline Validation
I'm initiating the baseline validation backtest now. My primary focus is to establish the execution time and the initial performance score using the provided command. This will serve as our benchmark for subsequent iterations.
### run_shell_command · 2026-09-29T02:04:38.843Z
{
"description": "Run the production baseline model on the validation stage",
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T02:10:44.995Z
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=10.0s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.2s
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=30.3s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.9s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.2s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.0s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=198.5s
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=211.5 validation_seconds=15.8
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": 361.753,
"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": 0.5398380464060987,
"positive_logit": 0.7427799578629043,
"negative_logit": -0.6222247021203207,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 211.47897834499997,
"validation_loss": 0.49663621530354346,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.796481195999945
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497606277466,
"item_norm_mean": 1.7570281028747559,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1654
## Step 15 · agent · 2026-09-29T02:11:31.488Z
### run_shell_command · 2026-09-29T02:11:31.488Z
{
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet",
"description": "Evaluate public validation ranking of the baseline model"
}
### Tool output · 2026-09-29T02:11:34.884Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1685
## Step 16 · agent · 2026-09-29T02:11:39.776Z
### grep_search · 2026-09-29T02:11:39.776Z
{
"dir_path": "/app/solution",
"pattern": "def "
}
### Tool output · 2026-09-29T02:11:39.796Z
Found 57 matches for pattern "def " in path "/app/solution":
---
File: train_current_model.py
L82: def read(cls, path: Path) -> "CandidateSet":
L118: def lengths(self) -> np.ndarray:
L122: def target_count(self) -> int:
L136: def selected_count(self) -> int:
L140: def log(message: str) -> None:
L144: def seed_everything(seed: int) -> None:
L150: def model_config(config: Config) -> base.TowerConfig:
L174: def append_state_snapshot(
L190: def causal_activity_features(
L247: def build_sequence_parts(
L314: def final_user_features(
L341: def prepare_data(
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L481: def save_cache(
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L511: def load_or_prepare(
L534: def build_item_correction(
L578: def build_known_positive_filter(
L609: def invalid_full_history_negatives(
L630: def draw_full_history_filtered_negatives(
L724: def draw_training_negatives(
L763: def event_sequence(
L774: def pool_explicit_states(
L798: def user_queries(
L813: def candidate_vectors(
L828: def history_windows(
L845: def corrected_negative_loss(
L867: def retained_target_window_starts(
L888: def chronological_target_partition(
L914: def load_organic_like_outcomes(
L955: def build_target_weights(
L1035: def build_dense_block(
L1111: def score_target_microbatches(
L1198: def release_accelerator_cache(device: torch.device) -> None:
L1203: def run_training_epoch(
L1364: def evaluate_validation_bce(
L1488: def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
L1495: def save_epoch_checkpoint(
L1521: def train_model(
L1782: def inference_queries(
L1861: def rank_candidates(
L1912: def write_ranking(frame: pd.DataFrame, output: Path) -> None:
L1922: def main() -> int:
---
File: two_tower_model.py
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(
L287: def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
L332: def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
L339: def encode_items(self, items: torch.Tensor) -> torch.Tensor:
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---
## Step 17 · agent · 2026-09-29T02:11:42.931Z
### Recorded reasoning summary
Exploring Model Building Blocks
I'm currently examining the function names within `train_current_model.py`. This is providing initial insights into the model's architecture and the distinct steps involved in its construction and training process, particularly around data filtering and negative sample generation.
### read_file · 2026-09-29T02:11:42.931Z
{
"end_line": 433,
"file_path": "/app/solution/two_tower_model.py",
"start_line": 280
}
### Tool output · 2026-09-29T02:11:42.938Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 280-433 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: 434.
--- FILE CONTENT (truncated) ---
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,
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 18 · agent · 2026-09-29T02:12:11.094Z
### read_file · 2026-09-29T02:12:11.094Z
{
"end_line": 650,
"start_line": 530,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:12:11.102Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 530-650 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: 651.
--- 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",
)
## Step 19 · agent · 2026-09-29T02:12:21.588Z
### read_file · 2026-09-29T02:12:21.588Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 651,
"end_line": 900
}
### Tool output · 2026-09-29T02:12:21.594Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 651-900 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: 901.
--- FILE CONTENT (truncated) ---
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,
)
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)
## Step 20 · agent · 2026-09-29T02:12:23.768Z
### read_file · 2026-09-29T02:12:23.768Z
{
"end_line": 1200,
"file_path": "/app/solution/train_current_model.py",
"start_line": 901
}
### Tool output · 2026-09-29T02:12:23.775Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 901-1200 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: 1201.
--- FILE CONTENT (truncated) ---
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 = (
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()
## Step 21 · agent · 2026-09-29T02:12:27.661Z
### read_file · 2026-09-29T02:12:27.661Z
{
"end_line": 1550,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1201
}
### Tool output · 2026-09-29T02:12:27.668Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1201-1550 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: 1551.
--- FILE CONTENT (truncated) ---
def run_training_epoch(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
generator: torch.Generator,
device: torch.device,
epoch: int,
) -> dict[str, float]:
model.train()
train_users = np.flatnonzero(target_counts > 0)
sorted_users = train_users[
np.argsort(target_counts[train_users], kind="stable")
]
groups = [
sorted_users[start : start + config.user_batch_size]
for start in range(0, len(sorted_users), config.user_batch_size)
]
group_order = torch.randperm(len(groups), generator=generator).numpy()
negative_count = config.uniform_negatives + config.inbatch_negatives
total_loss = 0.0
total_targets = 0
total_weight = 0.0
total_positive = 0.0
total_negative = 0.0
total_pair_correct = 0.0
batches = 0
started = time.monotonic()
for group_number in group_order:
users = groups[int(group_number)]
group_counts = target_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_counts.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_counts,
np.zeros(len(users), dtype=np.int64),
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0).detach()
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
"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):
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
)
## Step 22 · agent · 2026-09-29T02:12:32.896Z
### read_file · 2026-09-29T02:12:32.896Z
{
"end_line": 1930,
"start_line": 1551,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:12:32.902Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1551-1930 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: 1931.
--- FILE CONTENT (truncated) ---
generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
item_correction = build_item_correction(
prepared,
config,
partition.window_starts,
training_counts,
)
target_weights = build_target_weights(
public,
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
"negative_filter_scope", "last-100"
)
stored_target_weighting = stored_config.get(
"target_weighting", "uniform"
)
stored_weight_values = (
stored_config.get("ordinary_target_weight", 1.0),
stored_config.get("organic_strong_target_weight", 1.0),
stored_config.get("organic_like_target_weight", 3.0),
stored_config.get("discovery_horizon_days", 7),
)
current_weight_values = (
config.ordinary_target_weight,
config.organic_strong_target_weight,
config.organic_like_target_weight,
config.discovery_horizon_days,
)
stored_window_values = (
stored_config.get("validation_fraction", 0.10),
stored_config.get("max_train_targets_per_user", 0),
stored_config.get("max_validation_targets_per_user", 0),
)
current_window_values = (
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
objective_changed = (
stored_filter_scope != config.negative_filter_scope
or stored_target_weighting != config.target_weighting
or stored_weight_values != current_weight_values
or stored_window_values != current_window_values
)
model.load_state_dict(payload["model_state"])
if "optimizer_state" in payload and not objective_changed:
optimizer.load_state_dict(payload["optimizer_state"])
for group in optimizer.param_groups:
group["lr"] = config.learning_rate
group["weight_decay"] = config.weight_decay
log(f"restored optimizer state from {resume_checkpoint}")
else:
log(
f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
)
if "generator_state" in payload and not objective_changed:
generator.set_state(payload["generator_state"])
start_epoch = int(payload["epoch"])
if start_epoch >= config.epochs:
raise RuntimeError(
f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
)
prior = {
str(name): float(value)
for name, value in payload.get("diagnostics", {}).items()
}
if objective_changed:
prior.pop("validation_loss", None)
log(
"BCE objective changed: filter "
f"{stored_filter_scope}->{config.negative_filter_scope}, "
"target weighting "
f"{stored_target_weighting}->{config.target_weighting}, "
f"target window {stored_window_values}->{current_window_values}; "
"recomputing the resume checkpoint's validation BCE"
)
if "validation_loss" not in prior:
prior.update(
evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
)
history.append(prior)
best_loss = float(prior["validation_loss"])
best_epoch = start_epoch
best_state = snapshot_model(model)
log(
f"resumed validation-BCE training: epoch={start_epoch} "
f"loss={best_loss:.6f}"
)
for epoch in range(start_epoch + 1, config.epochs + 1):
diagnostics = run_training_epoch(
model,
optimizer,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
item_correction,
known_positive_packed,
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(
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")
## Step 23 · agent · 2026-09-29T02:12:34.955Z
### read_file · 2026-09-29T02:12:34.955Z
{
"start_line": 1,
"end_line": 100,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:12:34.963Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-100 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: 101.
--- 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
## Step 24 · agent · 2026-09-29T02:12:37.247Z
### read_file · 2026-09-29T02:12:37.247Z
{
"end_line": 2165,
"start_line": 1930,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:12:37.255Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1930-2165 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) ---
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)
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 25 · agent · 2026-09-29T02:12:40.903Z
### Recorded reasoning summary
Investigating Caching Mechanism
I'm currently digging into the `load_or_prepare` function, specifically examining how the caching mechanism (`--prepared-cache`) operates. My focus is on verifying the integrity and functionality of `save_cache` and `load_cache` within this process.
### read_file · 2026-09-29T02:12:40.903Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 470,
"end_line": 535
}
### Tool output · 2026-09-29T02:12:40.909Z
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 26 · agent · 2026-09-29T02:12:44.731Z
### Recorded reasoning summary
Optimizing Data Handling
I'm considering caching the data preparation step to bypass the ~50-second overhead per experiment. I'm currently investigating the potential size of the cache file for `SequenceData` arrays to assess the feasibility of this optimization.
### run_shell_command · 2026-09-29T02:12:44.731Z
{
"command": "df -h",
"description": "Check available disk space"
}
### Tool output · 2026-09-29T02:12:44.748Z
Output: Filesystem Size Used Avail Use% Mounted on
overlay 96G 14G 83G 15% /
tmpfs 64M 0 64M 0% /dev
shm 64M 0 64M 0% /dev/shm
/dev/root 96G 14G 83G 15% /etc/hosts
tmpfs 7.7G 0 7.7G 0% /proc/acpi
tmpfs 7.7G 0 7.7G 0% /proc/scsi
tmpfs 7.7G 0 7.7G 0% /sys/firmware
Process Group PGID: 1707
## Step 27 · agent · 2026-09-29T02:12:50.685Z
### Recorded reasoning summary
Analyzing Problem Constraints
I'm focusing on the request-time latency budget and the precomputation of item vectors. My current thoughts are centered on how the single user vector computation impacts this.
### Recorded reasoning summary
Improving Negative Sampling
My current focus is on refining the training objective, specifically how negative examples are selected and utilized. I'm diagnosing limitations within the existing negative-sampling strategy, aiming to optimize it within the given CPU budget.
### grep_search · 2026-09-29T02:12:50.685Z
{
"pattern": "dislike",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T02:12:50.701Z
Found 40 matches for pattern "dislike" in path "/app/solution":
---
File: train_current_model.py
L110: disliked_states: np.ndarray
L114: inference_disliked_items: np.ndarray
L176: active_dislikes: dict[int, int],
L179: disliked_states: list[np.ndarray],
L183: disliked_states.append(
L184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186: state_counts.append((len(active_likes), len(active_dislikes)))
L252: disliked_states: list[np.ndarray],
L279: active_dislikes: dict[int, int] = {}
L293: else active_dislikes
L304: active_dislikes,
L307: disliked_states,
L320: dislike_count: int,
L337: result[5] = np.log1p(dislike_count) / 4.0
L369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383: disliked_states = [
L384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405: disliked_states,
L427: int(dislike_counts[inference_row]),
L440: np.log1p(dislike_counts[missing]) / 4.0
L455: disliked_states=np.stack(disliked_states),
L459: inference_disliked_items=inference_dislikes,
L784: disliked_items = torch.from_numpy(
L785: prepared.disliked_states[unique_states].astype(np.int64)
L788: disliked_vectors = model._mean_context_channel(disliked_items)
L793: disliked_vectors[inverse_rows],
L802: disliked_vectors: torch.Tensor,
L807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159: disliked_vectors,
L1844: disliked_items = torch.from_numpy(
L1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851: model._mean_context_channel(disliked_items),
---
File: two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184: ("dislikes", 1, 1, 0),
L185: ("undislikes", 1, 0, 1),
L265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281: return liked, disliked, like_counts, dislike_counts
---
## Step 28 · agent · 2026-09-29T02:13:09.873Z
### Recorded reasoning summary
Clarifying Vector Usage
I'm realizing disliked items aren't negative training targets but context within the user tower (`disliked_vectors`). This prompts me to re-examine how negative examples are actually incorporated during the training phase.
### Recorded reasoning summary
Investigating Negative Sampling
I'm digging into `train_current_model.py`, specifically `invalid_full_history_negatives`. It appears to be filtering out negatives already present in the user's history or liked items. I'm now cross-referencing this with `draw_full_history_filtered_negatives` to fully grasp the sampling logic.
### Recorded reasoning summary
Refining Negative Sampling
I'm now focusing on the `draw_mixed_negatives` function. I'm analyzing how uniform negatives are sampled and how in-batch negatives are considered, especially when `config.inbatch_negatives` is active, to ensure comprehensive negative example generation.
### Recorded reasoning summary
Analyzing In-Batch Negatives
I'm scrutinizing the logic for drawing in-batch negatives within `draw_training_negatives`. Specifically, I'm tracing the `targets` variable and how `flat_scored` masks are applied to `sequence_outputs` to understand the selection process.
### Recorded reasoning summary
Examining Positive Item Sources
I'm currently scrutinizing the origin of `positive_items` within the training loop. It appears its length directly corresponds to `flat_scored`, indicating it's derived from a subset of the data, which I'm now tracing back to understand its composition.
### Recorded reasoning summary
Revising Negative Sampling Logic
I'm recalibrating my understanding of negative sampling. It seems `draw_training_negatives` is invoked per target, generating a substantial number of uniform negatives. I'm now scrutinizing how these, along with in-batch negatives, are utilized in `score_target_microbatches`, especially concerning unique item selection and candidate vector generation.
### Recorded reasoning summary
Questioning Loss Calculation
I'm re-examining the `score_target_microbatches` loss function. The specific calculation involving `positive_loss` and `corrected_negative_loss` is raising questions, and I'm now deeply scrutinizing the implications of `softplus` and the `corrected_negative_loss` implementation.
### Recorded reasoning summary
Analyzing Loss Components
I'm now dissecting the `corrected_negative_loss` function in detail. The calculation of `raw` as `F.softplus(logits)` appears equivalent to `-log(sigmoid(logits))`. I'm tracing how the weighted sum of in-batch negatives and the mean of uniform negatives contribute to the final loss value.
### Recorded reasoning summary
Revising Loss Averaging
I'm re-evaluating how negative losses are averaged. It appears the calculation divides the sum of uniform negative softplus values and a weighted average of in-batch negative softplus values by the total number of negatives. This results in an average negative loss per sample. Then, this average is combined with the positive loss, effectively balancing one positive against the average of many negatives.
### Recorded reasoning summary
Evaluating Retrieval Objective
I'm questioning if BCE with average negative loss is standard for retrieval when dealing with large catalogs. My focus is now shifting to understanding typical two-tower training objectives like InfoNCE and sampled softmax, and how they contrast with the current model's approach, especially in light of the prompt's specific constraints.
### grep_search · 2026-09-29T02:13:09.873Z
{
"dir_path": "/app/solution",
"pattern": "BCE|CrossEntropy|loss"
}
### Tool output · 2026-09-29T02:13:09.889Z
Found 58 matches for pattern "BCE|CrossEntropy|loss" in path "/app/solution":
---
File: train_current_model.py
L6: loss. The fast default retains each user's most recent 128 training targets and
L9: computes all of its timestep losses and carries the detached GRU hidden state
L55: loss_microbatch_size: int = 65_536
L128: """A contiguous recent target window and its chronological BCE split."""
L590: # chronological BCE suffix starts at T + 1 and is intentionally absent.
L845: def corrected_negative_loss(
L894: """Select a recent contiguous window with a final out-of-time BCE suffix."""
L1125: """Compute one logical block's exact BCE in bounded-memory slices."""
L1132: "loss_sum": 0.0,
L1138: for begin in range(0, target_count, config.loss_microbatch_size):
L1139: end = min(begin + config.loss_microbatch_size, target_count)
L1171: positive_loss = F.softplus(-positive_logits)
L1172: negative_loss = corrected_negative_loss(
L1178: loss_sum = (
L1179: selected_weights * (positive_loss + negative_loss) / 2.0
L1181: if not bool(torch.isfinite(loss_sum)):
L1182: raise RuntimeError("dense next-item training produced non-finite loss")
L1184: (loss_sum / total_weight).backward()
L1188: statistics["loss_sum"] += float(loss_sum.detach().cpu())
L1228: total_loss = 0.0
L1327: total_loss += statistics["loss_sum"]
L1337: batch_bce = statistics["loss_sum"] / max(
L1340: running_bce = total_loss / max(total_weight, 1.0e-12)
L1344: f"batch_bce={batch_bce:.6f} "
L1345: f"running_bce={running_bce:.6f} "
L1351: "loss": total_loss / max(total_weight, 1.0e-12),
L1364: def evaluate_validation_bce(
L1376: """Measure deterministic BCE on each user's chronological target suffix."""
L1388: total_loss = 0.0
L1475: total_loss += statistics["loss_sum"]
L1481: "validation_loss": total_loss / max(total_weight, 1.0e-12),
L1539: f"chronological BCE window: train={int(training_counts.sum()):,} "
L1571: best_loss = float("inf")
L1657: prior.pop("validation_loss", None)
L1659: "BCE objective changed: filter "
L1664: "recomputing the resume checkpoint's validation BCE"
L1666: if "validation_loss" not in prior:
L1668: evaluate_validation_bce(
L1682: best_loss = float(prior["validation_loss"])
L1686: f"resumed validation-BCE training: epoch={start_epoch} "
L1687: f"loss={best_loss:.6f}"
L1705: validation = evaluate_validation_bce(
L1731: diagnostics["validation_loss"]
L1732: < best_loss - config.early_stopping_min_delta
L1735: best_loss = diagnostics["validation_loss"]
L1754: f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
L1755: f"validation_loss={diagnostics['validation_loss']:.5f} "
L1766: f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
L1775: f"restored best validation-BCE checkpoint: epoch={best_epoch} "
L1776: f"loss={best_loss:.6f}"
L1939: "--loss-microbatch-size",
L1941: default=Config.loss_microbatch_size,
L2012: or args.loss_microbatch_size < 1
L2033: loss_microbatch_size=args.loss_microbatch_size,
L2126: "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
L2128: "model_selection": "chronological-validation-BCE-early-stopping",
L2129: "loss_execution": "gradient-accumulated-microbatches",
L2135: "objective": "sample-weighted-BCE-at-every-timestep",
---
## Step 29 · agent · 2026-09-29T02:14:27.845Z
### Recorded reasoning summary
Refining Architectural Details
I'm currently focusing on the specifics of the "pure-windowed-sequence-two-tower" architecture, particularly how sequence training integrates within this structure. The key is to ensure efficient data flow and model convergence within these constraints.
### Recorded reasoning summary
Integrating Model Configuration
I'm now examining the specific configuration parameters related to temporal modeling. My focus is on how the "every-adjacent-target-within-recent-chronological-window" strategy, coupled with the GRU encoder and specific BPTT handling, impacts overall sequence training.
### Recorded reasoning summary
Exploring Target Weighting
I'm now delving into the implications of the `discovery-graded` target weighting strategy. My current focus is on understanding its underlying logic and how it differs from the `organic-strong` baseline, particularly in relation to `load_organic_like_outcomes` and the `discovery_horizon_days` parameter.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now meticulously examining the current trainer's objective function, specifically how it selects and utilizes negative examples during training. My immediate focus is on diagnosing limitations within the existing negative-sampling strategy, with the goal of improving it under the fixed CPU budget. I'll be delving into the details of `draw_training_negatives`, `draw_full_history_filtered_negatives`, `draw_filtered_mixed_negatives`, and `build_item_correction`.
### Recorded reasoning summary
Analyzing Correction Logic
I am currently investigating the `build_item_correction` function to understand its role in estimating in-batch propensities. My focus is on how it aggregates item counts and calculates probabilities, specifically the `+1.0` and `+len(counts)` additions, to refine the training targets.
### Recorded reasoning summary
Refining Correction Mechanisms
I'm currently analyzing the `build_item_correction` function in detail. My focus is on understanding the mathematical underpinnings of in-batch negative correction, specifically how the normalization and clipping operations, influenced by `inbatch_correction_power`, `inbatch_correction_min`, and `inbatch_correction_max`, contribute to refining training targets.
### Recorded reasoning summary
Analyzing Correction Integration
I am now examining how the `item_correction` values are integrated into the training process. My focus is on their use within `draw_full_history_filtered_negatives` and subsequently in `corrected_negative_loss`, where they modulate the `negative_weights` and directly influence the loss calculation for negative samples.
### Recorded reasoning summary
Examining Correction Weights
I'm now scrutinizing how `correction` weights are applied within `corrected_negative_loss`. Specifically, I'm tracing their lineage from `build_item_correction` and their usage in modulating the in-batch negative loss contribution, noting how the sum of weights is used for normalization.
### Recorded reasoning summary
Revising Negative Sampling Logic
I'm rethinking the importance sampling weights for in-batch negatives. My current analysis suggests the calculated `raw` weights are inversely proportional to item popularity, meaning popular items get smaller weights, which seems counterintuitive for an unbiased estimator.
### Recorded reasoning summary
Revising Importance Sampling
I'm re-evaluating how importance sampling weights for in-batch negatives are computed. My current hypothesis is that the inverse proportionality to item popularity may not be the desired outcome for an unbiased estimator, prompting a deeper dive into the sampling mechanism itself.
### Recorded reasoning summary
Revising In-Batch Sampling
I've realized that my previous assessment of importance sampling was inaccurate. The current mechanism samples uniformly from unique targets, not proportionally to frequency, which contradicts the intention of `item_correction`'s frequency-based calculations.
### Recorded reasoning summary
Clarifying In-Batch Negatives
I'm now clarifying what constitutes in-batch negative sampling within the standard two-tower architecture. My focus is on how the similarity matrix inherently creates negatives from off-diagonal elements, and how this contrasts with current candidate vector generation strategies.
### Recorded reasoning summary
Analyzing Loss Calculation
I am now examining the loss calculation, specifically the use of `F.softplus(-positive_logits)` for positive examples and `corrected_negative_loss` for negatives. I'm also looking at how `selected_weights` and division by 2 contribute to the final `loss_sum`.
### Recorded reasoning summary
Investigating Objective Function
I am now thoroughly examining the trainer's objective function, particularly how it incorporates negative examples. My immediate concern is diagnosing any limitations within the current negative-sampling strategy to improve its efficiency while staying within the fixed CPU budget.
### run_shell_command · 2026-09-29T02:14:27.845Z
{
"command": "python3 -c \"import pandas as pd\ndf = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(df.info())\nprint(df.head(10))\n\"",
"description": "Inspect candidate_catalog.parquet"
}
### Tool output · 2026-09-29T02:14:29.410Z
Output:
RangeIndex: 30000 entries, 0 to 29999
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 item_id 30000 non-null uint32
1 positive_listens 30000 non-null int64
2 organic_positive_listens 30000 non-null uint64
3 popularity_rank 30000 non-null int32
dtypes: int32(1), int64(1), uint32(1), uint64(1)
memory usage: 703.3 KB
None
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
5 5463340 22746 17398 6
6 8213481 22505 17071 7
7 906358 22238 17183 8
8 2859641 18380 13704 9
9 3033749 16682 12267 10
Process Group PGID: 1714
## Step 30 · agent · 2026-09-29T02:14:31.931Z
### read_file · 2026-09-29T02:14:31.931Z
{
"file_path": "/app/solution/two_tower_model.py",
"end_line": 100,
"start_line": 60
}
### Tool output · 2026-09-29T02:14:31.938Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 60-100 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: 101.
--- FILE CONTENT (truncated) ---
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],
## Step 31 · agent · 2026-09-29T02:14:35.011Z
### Recorded reasoning summary
Developing Item Features
I've finalized the creation of four distinct item features. These include log transformations of positive and organic counts, a ratio of organic to positive values, and a normalized popularity rank metric.
### run_shell_command · 2026-09-29T02:14:35.011Z
{
"command": "python3 -c \"import pandas as pd\nfor name in ['train_likes', 'train_dislikes', 'train_unlikes', 'train_undislikes']:\n df = pd.read_parquet(f'/task/data/{name}.parquet')\n print(name, len(df), 'unique users:', df.uid.nunique(), 'unique items:', df.item_id.nunique())\n\"",
"description": "Check counts and stats of explicit actions"
}
### Tool output · 2026-09-29T02:14:39.026Z
Output: train_likes 814831 unique users: 8144 unique items: 172303
train_dislikes 101075 unique users: 5732 unique items: 50876
train_unlikes 292665 unique users: 6197 unique items: 113072
train_undislikes 19960 unique users: 2769 unique items: 14725
Process Group PGID: 1731
## Step 32 · agent · 2026-09-29T02:14:44.678Z
### run_shell_command · 2026-09-29T02:14:44.678Z
{
"command": "python3 -c \"import pandas as pd\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint('total listens:', len(df))\nprint('played_ratio_pct summary:\\n', df['played_ratio_pct'].describe())\nprint('is_organic counts:\\n', df['is_organic'].value_counts())\nprint('listens >= 50%:', (df['played_ratio_pct'] >= 50).sum())\nprint('listens < 50% (skipped):', (df['played_ratio_pct'] < 50).sum())\nprint('listens < 20% (hard skips):', (df['played_ratio_pct'] < 20).sum())\n\"",
"description": "Inspect train_listens stats"
}
### Tool output · 2026-09-29T02:14:47.508Z
Output: total listens: 43484159
played_ratio_pct summary:
count 4.348416e+07
mean 6.465021e+01
std 4.412183e+01
min 0.000000e+00
25% 7.000000e+00
50% 1.000000e+02
75% 1.000000e+02
max 1.590000e+02
Name: played_ratio_pct, dtype: float64
is_organic counts:
is_organic
1 22477622
0 21006537
Name: count, dtype: int64
listens >= 50%: 27500755
listens < 50% (skipped): 15983404
listens < 20% (hard skips): 13293566
Process Group PGID: 1750
## Step 33 · agent · 2026-09-29T02:14:58.670Z
### grep_search · 2026-09-29T02:14:58.670Z
{
"dir_path": "/app/solution",
"pattern": "LISTEN_POSITIVE_THRESHOLD"
}
### Tool output · 2026-09-29T02:14:58.686Z
Found 3 matches for pattern "LISTEN_POSITIVE_THRESHOLD" in path "/app/solution":
---
File: train_current_model.py
L240- (completion_sum - before_completion)
L241- / np.maximum(counts, 1)
L242- / 100.0
L243- ).astype(np.float16)
L244- return result
L245-
L246-
L247- def build_sequence_parts(
L248- listens: base.ListenEvents,
L249- explicit: base.ExplicitEvents,
L250- n_items: int,
L251- liked_states: list[np.ndarray],
L252- disliked_states: list[np.ndarray],
L253- state_counts: list[tuple[int, int]],
L254- ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
L255: qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
L256- timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
L257- items = listens.items[qualifying].astype(np.uint16, copy=False)
L258- organic = listens.organic[qualifying].astype(np.float32, copy=False)
L259- completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
L260- if not len(items):
L261- return None
L262-
L263- event_features = np.zeros((len(items), 3), dtype=np.float16)
L264- event_features[:, 0] = organic.astype(np.float16)
L265- event_features[:, 1] = (
L266- np.minimum(completion, 100.0) / 100.0
L267- ).astype(np.float16)
L268- if len(items) > 1:
L269- gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
L270- event_features[1:, 2] = np.exp(
L460- inference_user_features=inference_user_features,
L461- )
L462- log(
L463- f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
L464- f"tokens={len(prepared.sequence_items):,} "
L465- f"next_item_targets={prepared.target_count:,} "
L466- f"states={len(prepared.liked_states):,}"
L467- )
L468- return prepared, eligibility
L469-
L470-
L471- def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L472- return {
L473- "cache_version": CACHE_VERSION,
L474- "stage": stage,
L475: "positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
L476- "profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
L477- "event_time_feature": "exp-negative-gap-over-60-days",
L478- }
L479-
L480-
L481- def save_cache(
L482- path: Path, prepared: SequenceData, stage: str, config: Config
L483- ) -> None:
L484- path.parent.mkdir(parents=True, exist_ok=True)
L485- np.savez(
L486- path,
L487- metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
L488- **prepared.__dict__,
L489- )
L490- log(f"saved public-derived full-sequence cache: {path}")
---
File: two_tower_model.py
L4- import sys
L5- from dataclasses import dataclass
L6- from pathlib import Path
L7- from typing import Iterator
L8-
L9- import numpy as np
L10- import pandas as pd
L11- import pyarrow.parquet as pq
L12- import torch
L13- from torch import nn
L14-
L15-
L16- DAY = 86_400
L17- TRAIN_END = 24_786_800
L18- VALIDATION_END = 25_393_400
L19: LISTEN_POSITIVE_THRESHOLD = 50
L20- PROFILE_LOOKBACK_DAYS = 120
L21- LIKE_ITEMS = 48
L22- DISLIKE_ITEMS = 24
L23- TOP_N = 100
L24-
L25-
L26- @dataclass(frozen=True)
L27- class TowerConfig:
L28- sequence_length: int = 100
L29- embedding_dim: int = 192
L30- hidden_dim: int = 192
L31- user_hidden_1: int = 512
L32- user_hidden_2: int = 256
L33- item_hidden_1: int = 512
L34- item_hidden_2: int = 256
---
## Step 34 · agent · 2026-09-29T02:15:02.639Z
### read_file · 2026-09-29T02:15:02.639Z
{
"start_line": 100,
"file_path": "/app/solution/two_tower_model.py",
"end_line": 170
}
### Tool output · 2026-09-29T02:15:02.648Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 100-170 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: 171.
--- FILE CONTENT (truncated) ---
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
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])
## Step 35 · agent · 2026-09-29T02:15:04.727Z
### read_file · 2026-09-29T02:15:04.727Z
{
"end_line": 460,
"file_path": "/app/solution/train_current_model.py",
"start_line": 340
}
### Tool output · 2026-09-29T02:15:04.734Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 340-460 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: 461.
--- FILE CONTENT (truncated) ---
def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
if n_items >= np.iinfo(np.uint16).max:
raise RuntimeError("uint16 item storage is too small for this catalog")
if stage == "validation":
prefixes = ("train",)
history_end = base.TRAIN_END
candidate_name = "validation_eligible_candidates.npz"
state_name = "validation_preference_state.parquet"
else:
prefixes = ("train", "val")
history_end = base.VALIDATION_END
candidate_name = "test_eligible_candidates.npz"
state_name = "test_preference_state.parquet"
eligibility = CandidateSet.read(data / candidate_name)
if not np.array_equal(eligibility.item_ids, item_ids):
raise RuntimeError("catalog and candidate artifact item order differ")
explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
inference_likes, inference_dislikes, like_counts, dislike_counts = (
base.load_inference_state(
data / state_name,
item_index,
eligibility.uids,
n_items,
)
)
inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
# Snapshot zero is the shared empty explicit state.
liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
disliked_states = [
np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
]
state_counts: list[tuple[int, int]] = [(0, 0)]
user_ids: list[int] = []
offsets = [0]
timestamp_parts: list[np.ndarray] = []
item_parts: list[np.ndarray] = []
event_parts: list[np.ndarray] = []
activity_parts: list[np.ndarray] = []
state_parts: list[np.ndarray] = []
listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
started = time.monotonic()
for seen_users, (uid, listens) in enumerate(
base.merge_listen_streams(listen_paths, item_index), start=1
):
parts = build_sequence_parts(
listens,
explicit_by_user.get(uid, base.empty_explicit_events()),
n_items,
liked_states,
disliked_states,
state_counts,
)
if parts is None:
continue
sequence_row = len(user_ids)
user_ids.append(uid)
timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
item_parts.append(parts[1])
event_parts.append(parts[2])
activity_parts.append(parts[3])
state_parts.append(parts[4])
offsets.append(offsets[-1] + len(parts[1]))
inference_row = inference_rows.get(uid)
if inference_row is not None:
inference_sequence_rows[inference_row] = sequence_row
inference_user_features[inference_row] = final_user_features(
parts[0],
parts[1],
parts[2],
history_end,
int(like_counts[inference_row]),
int(dislike_counts[inference_row]),
)
if seen_users % 1_000 == 0:
log(
f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
f"states={len(liked_states):,} "
f"elapsed={time.monotonic() - started:.1f}s"
)
missing = np.flatnonzero(inference_sequence_rows < 0)
if len(missing):
inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
inference_user_features[missing, 5] = (
np.log1p(dislike_counts[missing]) / 4.0
)
log(f"target users without qualifying catalog sequence={len(missing):,}")
prepared = SequenceData(
item_ids=item_ids,
item_features=base.build_item_features(catalog),
user_ids=np.asarray(user_ids, dtype=np.int64),
offsets=np.asarray(offsets, dtype=np.int64),
sequence_timestamps=np.concatenate(timestamp_parts),
sequence_items=np.concatenate(item_parts),
event_features=np.concatenate(event_parts),
activity_features=np.concatenate(activity_parts),
state_ids=np.concatenate(state_parts),
liked_states=np.stack(liked_states),
disliked_states=np.stack(disliked_states),
state_counts=np.asarray(state_counts, dtype=np.int32),
inference_user_rows=inference_sequence_rows,
inference_liked_items=inference_likes,
inference_disliked_items=inference_dislikes,
inference_user_features=inference_user_features,
## Step 36 · agent · 2026-09-29T02:15:07.787Z
### Recorded reasoning summary
Optimizing Caching Strategy
I'm focusing on refining the data preparation and caching process. Specifically, I've identified that `prepare_data` currently filters for `completion_pct >= 50` catalog listens. My immediate priority is to implement and save the validation cache now to significantly speed up subsequent validation runs, which were previously taking around 50 seconds each. I'm also about to test running with this optimization.
### run_shell_command · 2026-09-29T02:15:07.787Z
{
"description": "Prepare and cache sequence data for validation",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/dummy.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --prepare-only"
}
### Tool output · 2026-09-29T02:16:02.100Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.7s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.8s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.2s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.0s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.7s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.1s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.4s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.0s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
{
"stage": "validation",
"sequence_users": 9135,
"sequence_tokens": 20056329,
"next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"target_users": 8456,
"runtime_seconds": 50.262,
"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: 1773
## Step 37 · agent · 2026-09-29T02:16:14.239Z
### Recorded reasoning summary
Caching Validation Data
The validation cache is now successfully loaded in seconds using the prepared cache flag, significantly speeding up this step. My focus is now shifting to a detailed exploration of the negative sampling and loss function mechanisms.
### Recorded reasoning summary
Investigating Negative Sampling
I'm delving into the specifics of how negative examples are chosen, particularly focusing on the uniform sampling strategy. My current concern is understanding the implications of sampling from the entire catalog of 30,000 items and whether this distribution aligns with item popularity, a crucial aspect for effective training.
### Recorded reasoning summary
Refining Negative Sampling
I'm realizing my uniform negative sampling might be problematic. Popular items get lost amongst obscure ones, hindering my ability to learn crucial distinctions. I'm now exploring how in-batch negatives, which naturally reflect item popularity, could be a better approach, but their current low count is concerning.
### Recorded reasoning summary
Revising In-Batch Sampling
I've re-examined the in-batch negative sampling implementation and identified a flaw. It appears the priority assignment is treating each unique target identically, regardless of its actual frequency or popularity within the batch. This is not what I intended.
### Recorded reasoning summary
Correcting Sampling Bias
I've discovered a critical flaw: uniform sampling from unique batch targets, then applying frequency-based corrections, actually exacerbates popularity bias. This means popular items are not penalized enough, potentially skewing recommendations. I'm rethinking the entire sampling and correction logic.
### run_shell_command · 2026-09-29T02:16:14.239Z
{
"command": "python3 -c \"import pandas as pd\ndf = pd.read_parquet('/app/solution/production_validation.parquet')\nprint('Top 20 most frequently recommended items:')\nprint(df['item_id'].value_counts().head(20))\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntop_rec_ids = df['item_id'].value_counts().head(20).index\nprint('Their popularity ranks in catalog:')\nprint(catalog[catalog.item_id.isin(top_rec_ids)][['item_id', 'popularity_rank', 'positive_listens']])\n\"",
"description": "Inspect top recommended items from baseline"
}
### Tool output · 2026-09-29T02:16:15.839Z
Output: Top 20 most frequently recommended items:
item_id
6896222 5089
7565403 4970
2055777 4647
8722394 4627
5010168 4572
5134208 4397
3968387 4160
8823268 4157
6414220 4155
8350060 4132
7227266 4091
8729910 4036
3375935 4003
6625048 3992
6006390 3988
6813373 3960
2408923 3865
2705460 3815
1034521 3747
781438 3710
Name: count, dtype: int64
Their popularity ranks in catalog:
item_id popularity_rank positive_listens
74 6006390 75 8970
75 8823268 76 8956
85 781438 86 8410
110 2408923 111 7515
125 6813373 126 7158
127 6625048 128 7133
188 1034521 189 6203
281 3375935 282 5213
305 5134208 306 5028
317 3968387 318 4914
563 8729910 564 3651
633 8722394 634 3464
817 6896222 818 3020
918 7227266 919 2823
1169 2055777 1170 2465
1528 8350060 1529 2115
2061 7565403 2062 1763
2946 5010168 2947 1385
3015 2705460 3016 1361
3206 6414220 3207 1305
Process Group PGID: 1798
## Step 38 · agent · 2026-09-29T02:16:20.277Z
### Recorded reasoning summary
Analyzing Recommendation Diversity
It seems a significant portion of users are receiving very similar top recommendations. I'm now shifting focus to identify what true discovery looks like within the `val_listens` and `val_likes` datasets to understand this better.
### run_shell_command · 2026-09-29T02:16:20.277Z
{
"command": "python3 -c \"import pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\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)\nprint('Number of users with validation truth:', len(truth))\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\ngrades = []\npop_ranks = []\nfor uid, items in truth.items():\n for item, grade in items.items():\n grades.append(grade)\n pop_ranks.append(item_pop.get(item, 99999))\nimport numpy as np\ngrades = np.array(grades)\npop_ranks = np.array(pop_ranks)\nprint('Total true items:', len(grades), 'Grade 3 items:', (grades==3).sum(), 'Grade 1 items:', (grades==1).sum())\nprint('Popularity rank quantiles for ground truth items:')\nprint(np.quantile(pop_ranks, [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]))\n\"",
"description": "Analyze ground truth validation targets distribution"
}
### Tool output · 2026-09-29T02:16:21.776Z
Output: Traceback (most recent call last):
File "", line 2, in
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1818
## Step 39 · agent · 2026-09-29T02:16:26.533Z
### run_shell_command · 2026-09-29T02:16:26.533Z
{
"command": "PYTHONPATH=/task python3 -c \"import pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\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)\nprint('Number of users with validation truth:', len(truth))\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\ngrades = []\npop_ranks = []\nfor uid, items in truth.items():\n for item, grade in items.items():\n grades.append(grade)\n pop_ranks.append(item_pop.get(item, 99999))\nimport numpy as np\ngrades = np.array(grades)\npop_ranks = np.array(pop_ranks)\nprint('Total true items:', len(grades), 'Grade 3 items:', (grades==3).sum(), 'Grade 1 items:', (grades==1).sum())\nprint('Popularity rank quantiles for ground truth items:')\nprint(np.quantile(pop_ranks, [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]))\n\"",
"description": "Analyze ground truth validation targets with PYTHONPATH set"
}
### Tool output · 2026-09-29T02:16:28.633Z
Output: Number of users with validation truth: 4263
Total true items: 37769 Grade 3 items: 4417 Grade 1 items: 33352
Popularity rank quantiles for ground truth items:
[1.00000e+00 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04
3.00000e+04]
Process Group PGID: 1829
## Step 40 · agent · 2026-09-29T02:17:42.003Z
### Recorded reasoning summary
Analyzing Popularity Distributions
I'm observing the popularity distribution for ground truth items. The median rank is 5,622, with the 75th percentile at 13,374 and the 90th at 21,680. This indicates that ground truth items are spread widely across the catalog, not confined to just the top 500.
### Recorded reasoning summary
Investigating Negative Sampling
My focus is now on the training objective and how it selects negatives. I'm dissecting the `score_target_microbatches` function to understand the logic behind negative example usage and identifying potential limitations within the current sampling strategy.
### Recorded reasoning summary
Examining Loss Components
I'm scrutinizing the loss calculations, specifically the interplay between positive and negative item contributions. My attention is drawn to the `corrected_negative_loss` function and its precise implementation, as it seems to be a critical differentiator in the overall objective.
### Recorded reasoning summary
Refining Loss Calculations
I'm revisiting the `corrected_negative_loss` function. It appears I might have been over-weighting uniform negatives; I'm examining how the mean of uniform negative losses is multiplied by `config.uniform_negatives`. This needs precise correction for accurate loss attribution.
### Recorded reasoning summary
Clarifying Loss Aggregation
I've pinpointed a misunderstanding in how losses are aggregated. Previously, I thought `config.uniform_negatives` and `config.inbatch_negatives` were multipliers; they are actually counts used in calculating the weighted average loss across all negatives. The current `corrected_negative_loss` effectively computes the mean loss over all 32 negatives. The subsequent addition of this mean negative loss to the positive loss, followed by averaging, is what needs critical re-evaluation regarding its gradient implications.
### Recorded reasoning summary
Analyzing Gradient Magnitudes
I'm now examining the gradients for positive and negative items within the loss function. It appears the negative item gradients might be excessively scaled down by a factor of 64, potentially diminishing their impact on learning, and I'm questioning the standard practice for BCE loss with multiple negatives.
### Recorded reasoning summary
Reconsidering Loss Functions
I'm questioning if BCE is the optimal objective for retrieval tasks. Standard practice often uses Softmax Cross-Entropy (InfoNCE) or Margin-based losses like BPR. The current approach, with a positive logit averaging only 0.74 and a significant negative logit of -0.62, suggests a potential mismatch between the chosen loss and the desired ranking outcome.
### Recorded reasoning summary
Evaluating Loss Effectiveness
I'm questioning the effectiveness of the current loss function given the low pair accuracy of 75.4% and the average negative logit of -0.62. This suggests a potential mismatch with the retrieval task's goals.
### Recorded reasoning summary
Revising Objective Function
I'm now exploring alternative objective functions beyond BCE, considering InfoNCE and margin-based losses. The current logit values and low pair accuracy suggest a need for a more suitable approach to optimize retrieval performance within the given constraints.
### Recorded reasoning summary
Investigating Negative Selection
I'm delving into the specifics of how negative examples are chosen. My current focus is on the `score_target_microbatches` function to fully grasp the negative sampling logic and identify any inherent limitations within the existing approach.
### Recorded reasoning summary
Exploring Negative Sampling Logic
I'm examining the distinctions between `draw_full_history_filtered_negatives` and `base.draw_filtered_mixed_negatives` within `two_tower_model.py`. My current focus is on how `base.draw_filtered_mixed_negatives` uses `invalid_negative_mask` to identify and filter out duplicates, targets, and known positives from the sampled negatives.
### Recorded reasoning summary
Refining Negative Filtering
I'm analyzing the `build_known_positive_filter` function in `train_current_model.py`. Specifically, I'm examining how it packs a user's entire public-training positive-item set using bitwise operations to efficiently filter known positives from sampled negatives.
### Recorded reasoning summary
Filtering Known Positives
I'm re-examining `build_known_positive_filter` and its handling of `target_count`. It appears I may have misunderstood how it processes sequence items, potentially not including the entire training history due to offsets and how `target_count` is determined by `chronological_target_partition`, which itself seems to discard earlier targets.
### Recorded reasoning summary
Revising Positive Filtering
I'm re-examining how known positives are filtered. It appears the `history_training_counts` are used, but these might exclude earlier targets due to chronological partitioning, and I need to confirm the exact mechanism.
### Recorded reasoning summary
Clarifying Data Slicing
I'm re-examining how sequences are sliced. It appears `prepared.sequence_items` is indexed from `offset` up to `offset + history_training_counts + 1`, which correctly captures items up to the validation split. I need to confirm how target rows within training are handled.
### run_shell_command · 2026-09-29T02:17:42.003Z
{
"command": "python3 -c \"import numpy as np, torch\nfrom train_current_model import Config, build_item_correction, load_cache\nconfig = Config()\nprepared = load_cache('/app/solution/validation_cache.npz', 'validation', config)\nfrom train_current_model import chronological_target_partition\npartition = chronological_target_partition(prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user)\ncorr = build_item_correction(prepared, config, partition.window_starts, partition.training_counts)\nprint('corr shape:', corr.shape)\nprint('min:', corr.min().item(), 'max:', corr.max().item(), 'mean:', corr.mean().item(), 'median:', corr.median().item())\nprint('corr for top 10 popular items:', corr[:10].numpy())\nprint('corr for bottom 10 items:', corr[-10:].numpy())\n\"",
"description": "Inspect item_correction values for popular vs tail items"
}
### Tool output · 2026-09-29T02:17:44.933Z
Output: Traceback (most recent call last):
File "", line 2, in
ModuleNotFoundError: No module named 'train_current_model'
Exit Code: 1
Process Group PGID: 1849
## Step 41 · agent · 2026-09-29T02:17:48.031Z
### run_shell_command · 2026-09-29T02:17:48.031Z
{
"command": "PYTHONPATH=/app/solution python3 -c \"import numpy as np, torch\nfrom train_current_model import Config, build_item_correction, load_cache, chronological_target_partition\nfrom pathlib import Path\nconfig = Config()\nprepared = load_cache(Path('/app/solution/validation_cache.npz'), 'validation', config)\npartition = chronological_target_partition(prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user)\ncorr = build_item_correction(prepared, config, partition.window_starts, partition.training_counts)\nprint('corr shape:', corr.shape)\nprint('min:', corr.min().item(), 'max:', corr.max().item(), 'mean:', corr.mean().item(), 'median:', corr.median().item())\nprint('corr for top 10 popular items:', corr[:10].numpy())\nprint('corr for bottom 10 items:', corr[-10:].numpy())\n\"",
"description": "Inspect item_correction values with PYTHONPATH set"
}
### Tool output · 2026-09-29T02:17:52.642Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
corr shape: torch.Size([30000])
min: 0.25 max: 4.0 mean: 1.8048696517944336 median: 1.702347755432129
corr for top 10 popular items: [0.25 0.25 0.25 0.25 0.25 0.28220448
0.25 0.25 0.25927085 0.25 ]
corr for bottom 10 items: [4. 2.4074833 2.5535216 2.1776505 2.0031471 1.9302809 1.8648285
3.2299778 2.1776505 1.8648285]
Process Group PGID: 1859
## Step 42 · agent · 2026-09-29T02:19:06.197Z
### Recorded reasoning summary
Analyzing Loss Function
I'm currently dissecting the `corrected_negative_loss` function, specifically examining the role of `correction` and how it modifies the `raw` softplus output. The goal is to understand its impact on the overall loss calculation.
### Recorded reasoning summary
Investigating Weighting Strategy
I'm discovering a critical flaw: popular items are effectively never penalized when used as in-batch negatives due to a 16-fold downweighting. This allows the model to assign excessively high scores to them without consequence, contradicting the goal of learning accurate representations.
### Recorded reasoning summary
Revising Loss Calculation
I've realized the foundational assumption about ground truth data was incorrect; users haven't exclusively listened to the same limited items. This necessitates a re-evaluation of why the `item_correction` was implemented, as the original paper's rationale for Softmax cross-entropy bias correction doesn't align with the codebase's use of BCE and uniform sampling.
### Recorded reasoning summary
Identifying Hard Negative Impact
I'm realizing that sampling popular items as negatives, even proportionally, still presents them as the hardest negatives. By downweighting their loss signal, the system inadvertently removes the crucial learning opportunity for distinguishing user preferences within popular items.
### Recorded reasoning summary
Examining Configuration Hardcoding
I'm discovering that crucial training parameters like `inbatch_negatives` and `uniform_negatives` aren't exposed via CLI flags but are instead hardcoded within the `Config` object, limiting flexibility and making it difficult to experiment with different sampling strategies.
### Recorded reasoning summary
Investigating Sampling Logic
I'm currently examining the code responsible for drawing in-batch negatives and suspect a fundamental misunderstanding of how they should be utilized. My current focus is on lines 652-675 of `train_current_model.py` to pinpoint the exact logic.
### Recorded reasoning summary
Refining Negative Sampling
I'm currently evaluating the `_full_history_filtered_negatives` function. My focus is on the sampling of uniform negatives and the conditional inclusion of in-batch negatives, aiming to ensure a robust strategy for selecting negative examples.
### Recorded reasoning summary
Selecting In-Batch Negatives
I'm re-examining the selection of in-batch negatives. It appears each target is treated independently, drawing a fixed number of negatives from `unique_targets` rather than considering the overall batch context for sampling.
### Recorded reasoning summary
Correcting Correction Logic
I'm noticing that rejected in-batch items are being reset to a `correction` of 1.0. This seems counterintuitive, as these were likely popular items that should still retain some special handling rather than being treated as ordinary uniform draws.
### Recorded reasoning summary
Adjusting Negative Sample Handling
I'm realizing that when an in-batch negative is invalid, it's currently being replaced by a uniform draw with its correction reset to 1.0. This seems to undermine the intended handling of these specific negative examples.
### Recorded reasoning summary
Analyzing Loss Value
I'm seeing that a single high-scoring negative item, even if only one of many, can disproportionately inflate the total loss. This is because the `negative_loss` averages across all negatives, masking the severe ranking issue caused by that outlier.
### Recorded reasoning summary
Comparing Loss Functions
I am now comparing the behavior of InfoNCE/Sampled Softmax against Binary Cross-Entropy. InfoNCE's formula directly penalizes large score differences between positives and negatives, driving exponential loss and large gradients when a negative is ranked higher than a positive, unlike the averaged loss in BCE.
### Recorded reasoning summary
Evaluating Loss Strategy
I'm now assessing InfoNCE and Softmax cross-entropy as potential loss functions. My focus is on how their inherent penalty mechanisms differ from Binary Cross-Entropy and whether they align better with learning accurate representations by directly addressing large score discrepancies.
### Recorded reasoning summary
Revising Training Objective
I'm realizing my previous analysis focused too narrowly on sampling, overlooking a fundamental aspect: the objective itself. I need to re-evaluate how negative examples are actually used within the loss calculation, shifting focus from *selection* to *utilization*.
### Recorded reasoning summary
Revising Loss Calculation
I'm realizing my initial assessment was too focused on sampling mechanics. The core issue lies in how the loss is calculated. The division by `total` in `corrected_negative_loss` effectively reduces the weight of individual negatives, while the absence of temperature scaling in the current BCE setup prevents proper handling of score discrepancies, especially with popular item biases.
### Recorded reasoning summary
Adjusting Similarity Calculation
I'm realizing that the raw dot product is being used for scoring, which, without temperature scaling, results in a limited range for exponential calculations. This significantly impacts how the loss function penalizes incorrect rankings compared to modern two-tower models.
### Recorded reasoning summary
Examining Sampling Proportions
I'm now investigating the mix of uniform versus in-batch negatives used in training. My focus is on the current configuration of 24 uniform negatives alongside 8 in-batch negatives, and how this specific ratio impacts learning.
### Recorded reasoning summary
Revising In-Batch Sampling
I'm realizing the term "in-batch negatives" is being used misleadingly. The current approach samples uniformly from unique targets, unlike true two-tower models where negatives arise from other users' positive items, naturally weighting popular items. This distinction is crucial for accurate representation learning.
### Recorded reasoning summary
Correcting Correction Assumptions
I'm realizing my understanding of the in-batch negative sampling was incomplete. The `build_item_correction` function erroneously assumed popular items would be sampled more frequently, a premise contradicted by the uniform sampling from `unique_targets`. This invalidates the `raw` correction calculation.
### Recorded reasoning summary
Identifying Invalid Negatives
I've discovered that `invalid_full_history_negatives` marks items as invalid if they appear in known positives, are the target itself, or are duplicates within the negative set. This significantly affects the pool of usable in-batch negatives.
### Recorded reasoning summary
Replacing Invalid Negatives
I'm discovering that when an in-batch negative is deemed invalid, it's replaced by a uniform random draw from the entire item pool. This indiscriminate replacement, along with resetting its correction to 1.0, appears to bypass the intended handling of specific negative examples.
### Recorded reasoning summary
Incorporating Dislikes
I'm realizing that explicit dislikes, a powerful negative signal, are currently only used as a user feature and never as actual negative training examples. This oversight means the model is missing a crucial opportunity to learn from strong negative preferences.
### Recorded reasoning summary
Re-evaluating Positive Signals
I'm realizing that all skipped tracks, even those with significant engagement before being abandoned, were entirely discarded as training positives. This fundamentally misinterprets user engagement and likely hinders the model's ability to learn nuanced preferences beyond just 80% completion or organic listens.
### Recorded reasoning summary
Analyzing Target Weighting
I'm realizing that under the default "organic-strong" weighting, explicit likes aren't receiving their intended 3.0 multiplier. This oversight means a significant positive signal isn't being leveraged effectively in training.
### Recorded reasoning summary
Clarifying Target Weighting
I'm realizing my previous assumptions about target weighting were incomplete. While the code mentions "organic-strong" with a 1.0 multiplier for listens, and "discovery-graded" with a 3.0 multiplier for Grade 3 items, it seems the author simply implemented the latter without fully justifying its application beyond its existence.
### Recorded reasoning summary
Refining Objective Logic
I'm now focusing on the training objective's core, specifically how negative examples are incorporated. My priority is understanding the impact of weighting and temperature scaling on the loss calculation, moving beyond solely sampling mechanics.
### Recorded reasoning summary
Analyzing Loss Formula
I am now examining the `corrected_negative_loss` function, focusing on how the `correction` tensor interacts with the `raw` softplus output to influence the final loss calculation.
### Recorded reasoning summary
Revising Loss Calculation
I'm realizing my initial assessment was too focused on sampling mechanics. The core issue lies in how the loss is calculated. The division by `total` in `corrected_negative_loss` effectively reduces the weight of individual negatives, while the absence of temperature scaling in the current BCE setup prevents proper handling of score discrepancies, especially with popular item biases.
### Recorded reasoning summary
Reducing Negative Loss Impact
I'm realizing the current loss calculation heavily favors positive items. Each of the 32 negatives carries a weight of approximately 1/64, while the positive item's weight is 0.5. This 32x disparity means the model's push against negatives is incredibly weak, especially in a large catalog.
### Recorded reasoning summary
Comparing Loss Dynamics
I am now analyzing how InfoNCE's gradients differ from BCE. In InfoNCE, the gradients for negatives balance the positive's gradient and are concentrated on the highest-scoring negatives, driving steeper corrections for poorly ranked items, unlike the current averaged and diluted BCE loss signal.
### Recorded reasoning summary
Analyzing Gradient Saturation
I'm realizing that my current Binary Cross-Entropy loss function is severely limiting my ability to correct poorly ranked negatives, capping gradients and requiring hundreds of steps to address even a single catastrophic negative. This is why I'm investigating alternative loss functions like BPR and InfoNCE.
### Recorded reasoning summary
Adjusting for Popularity Bias
I'm realizing the crucial insight that subtracting $\log p_j$ from logits in InfoNCE is a correction for item popularity. This adjustment accounts for the higher sampling frequency of popular items, preventing them from being disproportionately penalized or favored due to their prevalence in the dataset.
### Recorded reasoning summary
Analyzing Correction Logic
I'm discovering that the `build_item_correction` function calculates a `correction` tensor based on item counts and probabilities. This `correction` is then applied within the `corrected_negative_loss` function, directly modifying the `raw` softplus output to influence the final loss value.
### Recorded reasoning summary
Analyzing Loss Weighting
I'm realizing that the `correction` tensor, intended to adjust for item popularity, is being used to weight the `softplus` output of logits. This means popular items, which receive a low `correction` (e.g., 0.25), have their loss signal significantly reduced, while rare items with high corrections (e.g., 4.0) have their signal amplified. This is compounded by the uniform sampling of in-batch negatives, which fails to expose popular items more frequently.
### Recorded reasoning summary
Testing Correction Weights
I'm discovering a critical double penalty on training against popular items. They aren't sampled proportionally, and their loss signal is heavily downweighted. I'm testing the impact of setting `inbatch_correction_power` to 0.0 and sampling in-batch negatives proportionally to targets.
### Recorded reasoning summary
Encoding Unique Items
I'm discovering that the `candidate_vectors` function encodes only unique items. This means in-batch negatives, already present as positives, cost almost nothing to encode. Uniform negatives, however, add a significant number of new items to the encoding process.
### run_shell_command · 2026-09-29T02:19:06.197Z
{
"command": "PYTHONPATH=/app/solution python3 -c \"import torch\nbatch_size = 6000\nuniform = torch.randint(30000, (batch_size, 24))\nunique_uniform = len(torch.unique(uniform))\nprint('Unique items from 24 uniform negatives with batch_size 6000:', unique_uniform)\n\"",
"description": "Check number of unique items encoded with 24 uniform negatives"
}
### Tool output · 2026-09-29T02:19:09.153Z
Output: Unique items from 24 uniform negatives with batch_size 6000: 29745
Process Group PGID: 1870
## Step 43 · agent · 2026-09-29T02:19:35.487Z
### Recorded reasoning summary
Processing More Items
I've just processed a significant portion of the data. Nearly all 29,745 items from the item tower have been encoded within each batch, which is excellent progress.
### Recorded reasoning summary
Optimizing Item Encodings
I've identified a significant performance bottleneck: the repeated encoding of almost the entire item catalog on the CPU for every batch. I'm exploring the use of in-batch negatives, which could drastically reduce the number of item encodings needed per step. This should significantly speed up training epochs.
### Recorded reasoning summary
Investigating Target Weighting
My focus has shifted to understanding how the training objective selects and utilizes negative examples, specifically exploring the `discovery-graded` target weighting. I'm diagnosing its limitations and seeking improvements within the existing CPU budget, aiming to enhance the baseline score by analyzing how this strategy impacts item selection and performance.
### run_shell_command · 2026-09-29T02:19:35.487Z
{
"description": "Check recent git commits",
"command": "git log -p -2"
}
### Tool output · 2026-09-29T02:19:35.502Z
Output: /usr/bin/bash: line 4: git: command not found
Exit Code: 127
Process Group PGID: 1883
## Step 44 · agent · 2026-09-29T02:19:49.246Z
### Recorded reasoning summary
Exploring Validation Configurations
I'm currently investigating how different knobs affect the validation score, prioritizing understanding before making significant changes. I'm also curious about the duration of a cached sequence validation run.
### Recorded reasoning summary
Analyzing Training Bottlenecks
I'm examining the training process, specifically the `draw_training_negatives` function, to identify performance bottlenecks. My goal is to significantly reduce the time it takes to train a single epoch for faster hypothesis testing during research.
### Recorded reasoning summary
Refining Negative Sampling
I'm delving into `draw_full_history_filtered_negatives`, observing the steps involving `torch.randint`, unique target identification, priority assignment, and the iterative process within `invalid_full_history_negatives`. The creation of `candidate_vectors` with `torch.unique` on a large item set and subsequent item encoding are key areas of focus.
### run_shell_command · 2026-09-29T02:19:49.246Z
{
"description": "Benchmark individual BPTT step components",
"command": "PYTHONPATH=/app/solution python3 -c \"import time, torch, numpy as np\nfrom train_current_model import Config, load_cache, chronological_target_partition, build_target_weights, build_dense_block, event_sequence, draw_training_negatives, build_item_correction, build_known_positive_filter, score_target_microbatches, model_config\nimport two_tower_model as base\nfrom pathlib import Path\n\nconfig = Config()\nprepared = load_cache(Path('/app/solution/validation_cache.npz'), 'validation', config)\npartition = chronological_target_partition(prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user)\nitem_correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts)\ntarget_weights = build_target_weights(Path('/task'), 'validation', prepared, partition.history_training_counts, config)\nknown_positive_packed = build_known_positive_filter(prepared, partition.history_training_counts)\n\nmodel = base.SequentialTwoTower(prepared.item_features, model_config(config))\ndevice = torch.device('cpu')\ngenerator = torch.Generator(device='cpu').manual_seed(29)\n\ntrain_users = np.flatnonzero(partition.training_counts > 0)\nusers = train_users[:64]\ngroup_counts = partition.training_counts[users]\nhidden = torch.zeros(1, len(users), config.embedding_dim)\n\nt0 = time.monotonic()\nblock = build_dense_block(prepared, target_weights, users, partition.window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), 0, 100, config)\nt_block = time.monotonic() - t0\n\nt0 = time.monotonic()\nseq_out, _ = event_sequence(model, torch.from_numpy(block[0].astype(np.int64)), torch.from_numpy(block[1].astype(np.float32)), hidden)\nt_gru = time.monotonic() - t0\n\nflat_scored = block[5].reshape(-1)\npositive_items = torch.from_numpy(block[4].reshape(-1)[flat_scored].astype(np.int64))\nknown_history = torch.from_numpy(block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(np.int64))\nliked_items = torch.from_numpy(prepared.liked_states[block[3].reshape(-1)[flat_scored]].astype(np.int64))\nflat_user_rows = np.broadcast_to(users[:, None], block[5].shape).reshape(-1)[flat_scored]\n\nt0 = time.monotonic()\nnegs, neg_weights = draw_training_negatives(positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device)\nt_neg_draw = time.monotonic() - t0\n\nsequence_vectors = seq_out.reshape(-1, config.embedding_dim)[torch.from_numpy(flat_scored)]\nflat_states = block[3].reshape(-1)[flat_scored]\n\nt0 = time.monotonic()\nstats, seq_grad = score_target_microbatches(model, prepared, config, sequence_vectors, flat_states, block[2].reshape(-1, 4)[flat_scored].astype(np.float32), positive_items, negs, neg_weights, block[8].reshape(-1)[flat_scored], device, backward=True)\nt_score_bwd = time.monotonic() - t0\n\nprint(f'Timing per BPTT block (batch of 64 users, ~{len(positive_items)} targets):')\nprint(f' dense block build: {t_block*1000:.1f} ms')\nprint(f' GRU forward: {t_gru*1000:.1f} ms')\nprint(f' negatives draw: {t_neg_draw*1000:.1f} ms')\nprint(f' score + backward: {t_score_bwd*1000:.1f} ms')\n\""
}
### Tool output · 2026-09-29T02:19:55.630Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=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
Timing per BPTT block (batch of 64 users, ~5644 targets):
dense block build: 2.8 ms
GRU forward: 44.2 ms
negatives draw: 235.1 ms
score + backward: 796.5 ms
Process Group PGID: 1886
## Step 45 · agent · 2026-09-29T02:19:59.976Z
### Recorded reasoning summary
Analyzing Performance Bottlenecks
I'm currently investigating performance, finding that "negatives draw" and "score + backward" are the main culprits, consuming significant time per block. This is significantly impacting overall processing speed, far exceeding the GRU's forward pass.
### run_shell_command · 2026-09-29T02:19:59.976Z
{
"description": "Benchmark candidate_vectors encoding time",
"command": "PYTHONPATH=/app/solution python3 -c \"import time, torch\nfrom train_current_model import candidate_vectors, model_config, Config, load_cache\nimport two_tower_model as base\nfrom pathlib import Path\n\nconfig = Config()\nprepared = load_cache(Path('/app/solution/validation_cache.npz'), 'validation', config)\nmodel = base.SequentialTwoTower(prepared.item_features, model_config(config))\n\npositives = torch.randint(30000, (5644,))\nnegatives = torch.randint(30000, (5644, 32))\n\nt0 = time.monotonic()\npv, nv = candidate_vectors(model, positives, negatives)\nt_cand = time.monotonic() - t0\nprint(f'candidate_vectors took: {t_cand*1000:.1f} ms')\n\""
}
### Tool output · 2026-09-29T02:20:04.748Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
candidate_vectors took: 334.2 ms
Process Group PGID: 1900
## Step 46 · agent · 2026-09-29T02:20:18.755Z
### Recorded reasoning summary
Analyzing Encoding Time
I'm observing that the candidate vector encoding, both forward and backward passes, is consuming over 600ms of the total 796ms. This significant chunk of processing time requires immediate attention. I need to assess if these encoding steps are the primary bottleneck.
### Recorded reasoning summary
Investigating Negative Sampling
I'm re-reading the prompt's core directives, specifically zeroing in on the selection and utilization of negative examples within the training objective. My immediate focus is to diagnose and then enhance the existing negative-sampling strategy, all while respecting the CPU constraints and serving requirements.
### Recorded reasoning summary
Refining Negative Sampling
I'm realizing my current negative sampling isn't ideal. Uniform sampling over the entire catalog heavily favors unpopular items. In-batch negatives aren't weighted by frequency, making popular items just as likely to be chosen as rare ones. The baseline also completely ignores explicit dislikes and low-completion listens, which are valuable signals. I need to implement more sophisticated strategies.
### Recorded reasoning summary
Addressing Popularity Bias
I'm realizing my current negative sampling isn't effective. Uniform sampling over the entire catalog heavily favors unpopular items. In-batch negatives aren't weighted by frequency, making popular items just as likely to be chosen as rare ones. The baseline also completely ignores explicit dislikes and low-completion listens, which are valuable signals. I need to implement more sophisticated strategies.
### Recorded reasoning summary
Correcting Loss Calculations
I've discovered a significant issue in how popular items are treated as negatives. The `build_item_correction` function appears to incorrectly downweight the loss for popular in-batch negatives, effectively telling the model to ignore them. This is causing a strong bias towards recommending the same few popular items to all users.
### Recorded reasoning summary
Revising Loss Function
I'm realizing my current loss calculation's averaging of negatives and its reliance on BCE are suboptimal. This is diminishing the impact of individual negatives and not directly encouraging the positive to outperform all negatives. I need to switch to a more suitable loss like InfoNCE.
### update_topic · 2026-09-29T02:20:18.755Z
{
"summary": "Baseline evaluation yielded quality_ndcg@10 of 0.02128. Analysis of negative sampling identified key limitations: (1) item_correction severely downweights popular in-batch negatives (0.25x vs 4.0x) while sampling from unique_targets uniformly, causing the model to over-recommend popular items to >60% of users; (2) uniform negatives (24/32) are overwhelmingly tail items, missing hard negatives and costing huge CPU overhead; (3) the BCE objective averages 32 negative losses into 1, giving each negative only 1/64 gradient weight and failing to penalize high-scoring negatives; (4) explicit dislikes are ignored as negatives. We are now testing targeted improvements to negative selection and loss.",
"title": "Diagnosing and Experimenting with Negative Sampling and Loss",
"strategic_intent": "Formulate and test specific improvements to the negative sampling strategy and training objective."
}
### Tool output · 2026-09-29T02:20:20.712Z
Current topic: "Diagnosing and Experimenting with Negative Sampling and Loss"
Topic summary: Baseline evaluation yielded quality_ndcg@10 of 0.02128. Analysis of negative sampling identified key limitations: (1) item_correction severely downweights popular in-batch negatives (0.25x vs 4.0x) while sampling from unique_targets uniformly, causing the model to over-recommend popular items to >60% of users; (2) uniform negatives (24/32) are overwhelmingly tail items, missing hard negatives and costing huge CPU overhead; (3) the BCE objective averages 32 negative losses into 1, giving each negative only 1/64 gradient weight and failing to penalize high-scoring negatives; (4) explicit dislikes are ignored as negatives. We are now testing targeted improvements to negative selection and loss.
Strategic Intent: Formulate and test specific improvements to the negative sampling strategy and training objective.
## Step 47 · agent · 2026-09-29T02:20:40.486Z
### Recorded reasoning summary
Testing Caching Speed
I'm now focusing on optimizing execution times. My current thought is to run a quick experiment to measure the duration of a validation stage directly from the cached file. This will provide a baseline for performance.
### Recorded reasoning summary
Investigating Negative Sampling
My thoughts are now honing in on the prompt's explicit directive: to scrutinize and enhance the negative sampling strategy. I'm prioritizing an analysis of how the training objective selects and utilizes these negatives, aiming to pinpoint current limitations within a fixed CPU budget.
### Recorded reasoning summary
Analyzing Negative Sampling Logic
I'm now deeply examining the `corrected_negative_loss` function. My focus is on how negative examples are selected and weighted, particularly under a fixed CPU budget, and I'm exploring the impact of not downweighting `weights`.
### Recorded reasoning summary
Exploring Loss Functions
I am now considering alternative loss functions, specifically InfoNCE (Sampled Softmax). I'm comparing its formulation and potential gradient behavior against Binary Cross-Entropy, and exploring the impact of the temperature parameter $\tau$ on the logits.
### Recorded reasoning summary
Refining Negative Selection
I'm now questioning the current in-batch negative selection mechanism. It appears to sample from `unique_targets` rather than all other items in the batch, which deviates from standard practice. I need to investigate the rationale and implications of this approach.
### Recorded reasoning summary
Revising Negative Sampling
I'm now rethinking the negative sampling strategy in BPTT. It seems sampling uniformly from `unique_targets` might not be optimal. I'm exploring alternatives like sampling proportional to popularity or increasing the number of in-batch negatives.
### Recorded reasoning summary
Understanding Popularity Bias
I'm now realizing that sampling popular items as negatives is crucial for retrieval tasks to combat popularity bias. The literature emphasizes that without hard negatives, models default to recommending universally popular items, mirroring our baseline's observed behavior.
### Recorded reasoning summary
Investigating Popularity Bias
I'm now realizing that the baseline's extreme popularity bias stems from how negatives are selected and weighted. The current setup heavily downweights popular items, effectively shielding them from negative feedback and reinforcing their dominance. I'm planning experiments to test the impact of removing this downweighting, increasing in-batch negatives, and sampling negatives proportionally to popularity.
### run_shell_command · 2026-09-29T02:20:40.486Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/baseline_repro.parquet \\\n --prepared-cache /app/solution/validation_cache.npz",
"description": "Verify training from cached data and measure exact runtime"
}
### Tool output · 2026-09-29T02:25:55.908Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=197.4s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.7 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/baseline_repro.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 311.079,
"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": 0.5398380468700822,
"positive_logit": 0.742779956283339,
"negative_logit": -0.6222247051055542,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 210.7108823000001,
"validation_loss": 0.4966362183089643,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.389231074999998
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497606277466,
"item_norm_mean": 1.7570278644561768,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1914
## Step 48 · agent · 2026-09-29T02:25:59.366Z
### run_shell_command · 2026-09-29T02:25:59.366Z
{
"command": "python /task/evaluate_public.py /app/solution/baseline_repro.parquet",
"description": "Evaluate baseline reproduction ranking"
}
### Tool output · 2026-09-29T02:26:02.703Z
Output: {
"model": "/app/solution/baseline_repro.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1939
## Step 49 · agent · 2026-09-29T02:26:08.195Z
### Recorded reasoning summary
Testing Weighting Strategies
My current focus is on systematically testing our hypotheses, starting with an investigation into the `--target-weighting discovery-graded` parameter. I'm examining its implementation within `train_current_model.py` to understand its exact behavior and potential impact.
### run_shell_command · 2026-09-29T02:26:08.195Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/exp1_discovery_graded.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded && python /task/evaluate_public.py /app/solution/exp1_discovery_graded.parquet",
"description": "Test target-weighting discovery-graded"
}
### Tool output · 2026-09-29T02:31:30.990Z
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=196.6s
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=209.8 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.504831
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp1_discovery_graded.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 315.063,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5421154219032777,
"positive_logit": 0.7774311394458894,
"negative_logit": -0.6407561916408621,
"pair_accuracy": 0.7602795720701684,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.7952582150001,
"validation_loss": 0.5048305763960504,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.412744868999653
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7521849870681763,
"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/exp1_discovery_graded.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 1959
## Step 50 · agent · 2026-09-29T02:31:37.316Z
### Recorded reasoning summary
Improving Quality Score
My analysis shows a significant 10.5% uplift in quality_ndcg@10, reaching 0.023522, directly attributed to the discovery-graded target weighting. My next steps involve evaluating negative sampling strategies and refining the training objective.
### read_file · 2026-09-29T02:31:37.316Z
{
"start_line": 1930,
"end_line": 1990,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:31:37.324Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1930-1990 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: 1991.
--- FILE CONTENT (truncated) ---
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(
## Step 51 · agent · 2026-09-29T02:31:39.431Z
### read_file · 2026-09-29T02:31:39.431Z
{
"end_line": 2050,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1970
}
### Tool output · 2026-09-29T02:31:39.439Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1970-2050 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) ---
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 52 · agent · 2026-09-29T02:31:55.311Z
### Recorded reasoning summary
Refining Training Parameters
I'm focusing on enhancing `train_current_model.py` by integrating new arguments for `--uniform-negatives`, `--inbatch-negatives`, and `--inbatch-correction-power`. This will significantly streamline and accelerate experimental iterations. I'm also reviewing the `replace` functionality within the script.
### Recorded reasoning summary
Exploring Loss Function Variations
I'm currently investigating how to incorporate multiple loss functions and negative sampling strategies. My focus is on understanding the implications of switching between InfoNCE and BCE, particularly in how the logits are structured and how the target indices would need to be adjusted. This exploration aims to increase model flexibility.
### Recorded reasoning summary
Investigating Sampling Strategies
I'm diving deeper into the nuances of in-batch negative sampling. Specifically, I'm examining how different approaches to sampling bias correction, like subtracting log probabilities from logits versus applying a scaling factor to the loss, impact model performance. I plan to implement direct comparisons in Python.
### Recorded reasoning summary
Revising Sampling Logic
I'm re-examining the `draw_full_history_filtered_negatives` function, particularly lines 650-685 in `train_current_model.py`. My current focus is on understanding the logic for `corrected_count` and how it influences the sampling of in-batch negatives, aiming for more robust strategies.
### Recorded reasoning summary
Adjusting Negative Sampling
I'm refining the in-batch negative sampling logic. I've noticed the use of `masked_fill_` with -1.0 to exclude the user's positive target from negative sampling, which is correct. However, I am questioning why `unique_targets` is sampled from in this step and will investigate further to ensure optimal negative selection.
### Recorded reasoning summary
Analyzing Sampling Bias
I'm realizing that using `torch.unique` on targets before sampling in-batch negatives removes the crucial popularity distribution. This means popular items aren't sampled as negatives more often, defeating a key purpose of this strategy. I need to address this bias.
### Recorded reasoning summary
Correcting Sampling Bias
I've identified a critical flaw: using `torch.unique` on targets before sampling in-batch negatives eliminates the popularity distribution. This means popular items aren't sampled as negatives proportionally. I'm now exploring sampling random indices from the batch's targets to preserve this distribution and ensure popular items are appropriately penalized, even after filtering invalid negatives.
### read_file · 2026-09-29T02:31:55.311Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 65,
"start_line": 35
}
### Tool output · 2026-09-29T02:31:55.320Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 35-65 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: 66.
--- FILE CONTENT (truncated) ---
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
## Step 53 · agent · 2026-09-29T02:32:06.328Z
### read_file · 2026-09-29T02:32:06.328Z
{
"end_line": 720,
"start_line": 650,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:32:06.335Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 650-720 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 721.
--- FILE CONTENT (truncated) ---
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
## Step 54 · agent · 2026-09-29T02:32:21.410Z
### Recorded reasoning summary
Refining Negative Sample Handling
I'm currently zeroing in on how we're managing negative samples, specifically within lines 698-701. My focus is on optimizing the `correction` array's application to ensure accurate adjustments based on `corrected_count` and the `config.uniform_negatives` boundary.
### Recorded reasoning summary
Adjusting Loss Calculations
I'm now examining the `corrected_negative_loss` function, paying close attention to how `correction` and `config` parameters influence the raw loss. The `softplus` output and the conditional application of uniform versus in-batch negatives are key areas of focus.
### Recorded reasoning summary
Implementing Negative Sample Strategies
I'm implementing the CLI options for training negative sample strategies in `train_current_model.py`. My focus is on integrating the `full-training-history` filter and testing various configurations.
### Recorded reasoning summary
Configuring Negative Sampling
I'm now defining the CLI arguments related to negative sample configurations in `parse_args`, including options for uniform and in-batch negatives, correction parameters, loss types, and temperature. This step is crucial for enabling flexible experimentation with different sampling strategies.
### Recorded reasoning summary
Analyzing Loss Functions
I'm currently analyzing the `infonce` loss function, specifically how positive and negative logits are concatenated and the target indexing. I'm also investigating the calculation for pair accuracy and the statistics collected for positive and negative logits, noting their identical nature. Additionally, I'm beginning to examine the Bayesian Personalized Ranking (BPR) loss.
### Recorded reasoning summary
Refining Loss Calculations
I'm currently refining the loss calculations, specifically how `selected_weights` are applied to `sample_loss`. I've also been examining the `corrected_negative_loss` function within BCE, noting the use of `softplus` for positive and negative logits, and how `selected_weights` impact the summed loss. My focus has also shifted to how in-batch negatives are sampled, with a detailed look at `draw_full_history_filtered_negatives` and the logic for `corrected_count` and priority assignment.
### Recorded reasoning summary
Simplifying In-Batch Negative Sampling
I've identified a much simpler and faster approach for in-batch negative sampling. Instead of complex priority calculations, I can directly sample indices from `targets_cpu`. This leverages the natural distribution of targets within the batch, effectively increasing the likelihood of sampling popular items as negatives. The existing invalid filtering mechanism will handle cases where a sampled negative is the same as the target or already in history, ensuring robustness and efficiency.
### run_shell_command · 2026-09-29T02:32:21.410Z
{
"command": "python3 -c \"import time, torch\nbatch_size = 5600\nunique_targets = torch.randint(30000, (1500,))\ntargets_cpu = torch.randint(30000, (5600,))\nk = 8\n\n# Old way:\nt0 = time.monotonic()\npriorities = torch.rand((batch_size, len(unique_targets)))\npriorities.masked_fill_(unique_targets[None, :].eq(targets_cpu[:, None]), -1.0)\ncolumns = torch.topk(priorities, k=k, dim=1).indices\ninbatch1 = unique_targets[columns]\nt_old = time.monotonic() - t0\n\n# Direct sampling way:\nt0 = time.monotonic()\nidx = torch.randint(0, batch_size, (batch_size, k))\ninbatch2 = targets_cpu[idx]\nt_new = time.monotonic() - t0\n\nprint(f'Old way: {t_old*1000:.1f} ms')\nprint(f'New way: {t_new*1000:.1f} ms ({t_old/t_new:.1f}x faster!)')\n\"",
"description": "Benchmark old vs new in-batch sampling speed"
}
### Tool output · 2026-09-29T02:32:24.426Z
Output: Old way: 53.7 ms
New way: 0.2 ms (218.0x faster!)
Process Group PGID: 2008
## Step 55 · agent · 2026-09-29T02:32:30.372Z
### Recorded reasoning summary
Optimizing BPTT Speed
I've achieved a significant speedup in BPTT, reducing each step from 53.7 ms to a mere 0.2 ms. This translates to a 14-second saving over 266 steps without any memory spikes. I'm now investigating drawing performance from `targets_cpu`.
### read_file · 2026-09-29T02:32:30.372Z
{
"end_line": 70,
"file_path": "/app/solution/train_current_model.py",
"start_line": 35
}
### Tool output · 2026-09-29T02:32:30.380Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 35-70 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: 71.
--- FILE CONTENT (truncated) ---
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
## Step 56 · agent · 2026-09-29T02:32:33.079Z
### read_file · 2026-09-29T02:32:33.079Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 865,
"start_line": 845
}
### Tool output · 2026-09-29T02:32:33.085Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 845-865 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: 866.
--- FILE CONTENT (truncated) ---
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
## Step 57 · agent · 2026-09-29T02:32:36.890Z
### read_file · 2026-09-29T02:32:36.890Z
{
"end_line": 1190,
"start_line": 1160,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:32:36.897Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1160-1190 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: 1191.
--- FILE CONTENT (truncated) ---
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
## Step 58 · agent · 2026-09-29T02:32:42.907Z
### Recorded reasoning summary
Exploring InfoNCE Logic
I'm currently focused on the `infonce` loss type within `score_target_microbatches`. My analysis is zeroed in on how the `positive_logits` are scaled before being used in the calculation.
### Recorded reasoning summary
Refining Temperature Scaling
My current focus is on the temperature scaling within the InfoNCE loss calculation. I'm dissecting how the `temperature` parameter influences both the positive and negative logits before they are combined for the cross-entropy loss. I am also examining the BPR loss implementation and its use of negative_logits minus positive_logits.
### read_file · 2026-09-29T02:32:42.907Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 75,
"start_line": 45
}
### Tool output · 2026-09-29T02:32:42.915Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 45-75 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) ---
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 59 · agent · 2026-09-29T02:32:45.357Z
### read_file · 2026-09-29T02:32:45.357Z
{
"start_line": 1935,
"end_line": 2050,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:32:45.365Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1935-2050 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) ---
)
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 60 · agent · 2026-09-29T02:33:00.066Z
### Recorded reasoning summary
Exploring Configuration Options
I'm currently examining the `Config` dataclass, focusing on parameters like `bptt_steps`, `user_batch_size`, `embedding_dim`, and `hidden_dim`. I'm also reviewing the initial layers of user-specific hidden dimensions.
### Recorded reasoning summary
Refining Hyperparameters
I'm now deeply engrossed in fine-tuning the hyperparameters. My focus has shifted to the nuances of negative sampling strategies and target weighting, specifically exploring values for `uniform_negatives`, `inbatch_negatives`, and the various `target_weighting` parameters.
### Recorded reasoning summary
Testing Inbatch Sampling
I'm now investigating the `inbatch_sampling` functionality. Specifically, I'm analyzing the "batch-frequency" logic for negative sampling and how it interacts with `inbatch_negatives`, while also noting that "unique-targets" retains prior behavior. I'm also looking at the support for different `loss_type` values in `score_target_microbatches`.
### Recorded reasoning summary
Implementing Loss Functions
I'm now implementing the core logic for both "infonce" and "bpr" loss functions. My current focus is on correctly calculating and applying the temperature scaling for logits, and then utilizing `F.cross_entropy` for the "infonce" loss. I'm also setting up the difference calculation for the "bpr" loss.
### replace · 2026-09-29T02:33:00.066Z
{
"old_string": " early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29",
"instruction": "Add loss_type, temperature, and inbatch_sampling parameters to Config",
"file_path": "/app/solution/train_current_model.py",
"new_string": " early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n loss_type: str = \"bce\"\n temperature: float = 1.0\n inbatch_sampling: str = \"unique-targets\""
}
### Tool output · 2026-09-29T02:33:00.088Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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
loss_type: str = "bce"
temperature: float = 1.0
inbatch_sampling: str = "unique-targets"
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
...
## Step 61 · agent · 2026-09-29T02:33:02.694Z
### read_file · 2026-09-29T02:33:02.694Z
{
"end_line": 685,
"file_path": "/app/solution/train_current_model.py",
"start_line": 650
}
### Tool output · 2026-09-29T02:33:04.690Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 650-685 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) ---
(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 62 · agent · 2026-09-29T02:33:12.221Z
### Recorded reasoning summary
Refining Sampling Logic
I'm currently digging into the `draw_full_history_filtered_negatives` function, specifically examining how the `batch-frequency` in-batch sampling is handled. My focus is on refining the initialization of the `inbatch` tensor and ensuring the `corrected_count` is correctly managed under this sampling strategy.
### Recorded reasoning summary
Revising Batch-Frequency Logic
I'm now focused on the `batch-frequency` in-batch sampling. My current efforts are directed at the precise initialization of the `inbatch` tensor and ensuring the `corrected_count` is accurately determined within this specific sampling mechanism.
### replace · 2026-09-29T02:33:12.221Z
{
"file_path": "/app/solution/train_current_model.py",
"instruction": "Support batch-frequency inbatch sampling in draw_full_history_filtered_negatives",
"old_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"new_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n if config.inbatch_sampling == \"batch-frequency\":\n corrected_count = config.inbatch_negatives\n indices = torch.randint(\n 0, batch_size, (batch_size, config.inbatch_negatives), generator=generator, device=\"cpu\"\n )\n inbatch = torch.from_numpy(targets_cpu)[indices]\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)"
}
### Tool output · 2026-09-29T02:33:12.241Z
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 == "batch-frequency":
corrected_count = config.inbatch_negatives
indices = torch.randint(
0, batch_size, (batch_size, config.inbatch_negatives), generator=generator, device="cpu"
)
inbatch = torch.from_numpy(targets_cpu)[indices]
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:
correction[
...
## Step 63 · agent · 2026-09-29T02:33:14.387Z
### read_file · 2026-09-29T02:33:14.387Z
{
"end_line": 1195,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1165
}
### Tool output · 2026-09-29T02:33:14.393Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1165-1195 of 2175 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1196.
--- FILE CONTENT (truncated) ---
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:
## Step 64 · agent · 2026-09-29T02:33:21.041Z
### Recorded reasoning summary
Refining Logit Calculation
I'm currently examining the `score_target_microbatches` function, specifically how the positive and negative logits are computed. My focus is on the efficiency and accuracy of the matrix operations involved, particularly the `torch.einsum` call.
### Recorded reasoning summary
Optimizing Loss Computation
My current focus is on streamlining the `infonce` and `bpr` loss calculations. I'm exploring how to efficiently apply weights and temperature scaling within these operations, aiming for both speed and precision.
### replace · 2026-09-29T02:33:21.041Z
{
"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 selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_type == \"infonce\":\n scaled_pos = (positive_logits / config.temperature).unsqueeze(1)\n scaled_neg = negative_logits / config.temperature\n logits = torch.cat([scaled_pos, scaled_neg], dim=1)\n targets_zero = torch.zeros(len(logits), dtype=torch.long, device=device)\n sample_loss = F.cross_entropy(logits, targets_zero, reduction=\"none\")\n loss_sum = (selected_weights * sample_loss).sum()\n elif config.loss_type == \"bpr\":\n diff = (negative_logits - positive_logits.unsqueeze(1)) / config.temperature\n sample_loss = F.softplus(diff).mean(dim=1)\n loss_sum = (selected_weights * sample_loss).sum()\n else:\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"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()",
"instruction": "Support loss_type in score_target_microbatches",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:33:21.055Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
if config.loss_type == "infonce":
scaled_pos = (positive_logits / config.temperature).unsqueeze(1)
scaled_neg = negative_logits / config.temperature
logits = torch.cat([scaled_pos, scaled_neg], dim=1)
targets_zero = torch.zeros(len(logits), dtype=torch.long, device=device)
sample_loss = F.cross_entropy(logits, targets_zero, reduction="none")
loss_sum = (selected_weights * sample_loss).sum()
elif config.loss_type == "bpr":
diff = (negative_logits - positive_logits.unsqueeze(1)) / config.temperature
sample_loss = F.softplus(diff).mean(dim=1)
loss_sum = (selected_weights * sample_loss).sum()
else:
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
...
## Step 65 · agent · 2026-09-29T02:33:23.782Z
### read_file · 2026-09-29T02:33:23.782Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 2070,
"start_line": 1970
}
### Tool output · 2026-09-29T02:33:23.789Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1970-2070 of 2187 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(
"--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)
## Step 66 · agent · 2026-09-29T02:33:38.304Z
### Recorded reasoning summary
Adding Arguments
I'm currently focused on enhancing argument parsing within the `parse_args` function. My latest refinement involves integrating a new `--uniform-negatives` argument, allowing for integer specification and defaulting to `Config.uniform_negatives`. I'm also preparing to introduce the `--inbatch-` argument.
### replace · 2026-09-29T02:33:38.304Z
{
"file_path": "/app/solution/train_current_model.py",
"new_string": " parser.add_argument(\n \"--uniform-negatives\",\n type=int,\n default=Config.uniform_negatives,\n )\n parser.add_argument(\n \"--inbatch-negatives\",\n type=int,\n default=Config.inbatch_negatives,\n )\n parser.add_argument(\n \"--inbatch-correction-power\",\n type=float,\n default=Config.inbatch_correction_power,\n )\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce\", \"infonce\", \"bpr\"),\n default=Config.loss_type,\n )\n parser.add_argument(\n \"--temperature\",\n type=float,\n default=Config.temperature,\n )\n parser.add_argument(\n \"--inbatch-sampling\",\n choices=(\"batch-frequency\", \"unique-targets\"),\n default=Config.inbatch_sampling,\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\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 ):\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 negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n loss_type=args.loss_type,\n temperature=args.temperature,\n inbatch_sampling=args.inbatch_sampling,\n )",
"instruction": "Add arguments for negative sampling and loss in parse_args and pass to Config",
"old_string": " parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\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 ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )"
}
### Tool output · 2026-09-29T02:33:38.322Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
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(
"--loss-type",
choices=("bce", "infonce", "bpr"),
default=Config.loss_type,
)
parser.add_argument(
"--temperature",
type=float,
default=Config.temperature,
)
parser.add_argument(
"--inbatch-sampling",
choices=("batch-frequency", "unique-targets"),
default=Config.inbatch_sampling,
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
...
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
uniform_negatives=args.uniform_negatives,
inbatch_negatives=args.inbatch_negatives,
inbatch_correction_power=args.inbatch_correction_power,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
...
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,
loss_type=args.loss_type,
temperature=args.temperature,
inbatch_sampling=args.inbatch_sampling,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
...
## Step 67 · agent · 2026-09-29T02:33:42.105Z
### Recorded reasoning summary
Checking Defaults Carefully
I am currently verifying that all default parameters, including `loss_type`, `temperature`, `inbatch_sampling`, `uniform_negatives`, and `inbatch_negatives`, precisely align with the established baseline. This meticulous cross-referencing ensures foundational consistency before proceeding.
### run_shell_command · 2026-09-29T02:33:42.105Z
{
"description": "Test inbatch-correction-power 0.0 with discovery-graded weighting",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/exp2_corr0.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --inbatch-correction-power 0.0 && python /task/evaluate_public.py /app/solution/exp2_corr0.parquet"
}
### Tool output · 2026-09-29T02:39:07.770Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.563095 running_bce=0.563300 elapsed=197.8s
epoch=1/1 train_loss=0.56103 validation_loss=0.52015 pair_accuracy=0.763 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.9 validation_seconds=15.6
restored best validation-BCE checkpoint: epoch=1 loss=0.520147
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp2_corr0.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 317.922,
"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": 0.0,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29,
"loss_type": "bce",
"temperature": 1.0,
"inbatch_sampling": "unique-targets"
},
"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": 0.5610289423063375,
"positive_logit": 0.6264491060736955,
"negative_logit": -0.6609029005465753,
"pair_accuracy": 0.7632028091472269,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 210.9071019930002,
"validation_loss": 0.5201467737373525,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.63540272199998
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.8937625885009766,
"query_norm_std": 0.849907636642456,
"item_norm_mean": 1.7925585508346558,
"item_norm_std": 0.7955368161201477
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp2_corr0.parquet",
"scores": {
"quality_ndcg@10": 0.015049,
"quality_users": 4263
}
}
Process Group PGID: 2021
## Step 68 · agent · 2026-09-29T02:39:14.072Z
### Recorded reasoning summary
Investigating NDCG Drop
I'm digging into why NDCG plummeted to 0.015049 when `inbatch-correction-power` was zeroed out in BCE. It seems related to the `softplus` operation on the logits within the BCE loss calculation.
### run_shell_command · 2026-09-29T02:39:14.072Z
{
"description": "Inspect recommendations from exp2_corr0",
"command": "python3 -c \"import pandas as pd\ndf = pd.read_parquet('/app/solution/exp2_corr0.parquet')\nprint('Top 10 recommended items:')\nprint(df['item_id'].value_counts().head(10))\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntop_rec_ids = df['item_id'].value_counts().head(10).index\nprint(catalog[catalog.item_id.isin(top_rec_ids)][['item_id', 'popularity_rank', 'positive_listens']])\n\""
}
### Tool output · 2026-09-29T02:39:15.673Z
Output: Top 10 recommended items:
item_id
6896222 2859
3970945 2727
8722394 2466
6006390 2363
2705460 2311
5010168 2287
7227266 2271
6625048 2255
2055777 2243
8647463 2143
Name: count, dtype: int64
item_id popularity_rank positive_listens
74 6006390 75 8970
127 6625048 128 7133
407 8647463 408 4381
633 8722394 634 3464
817 6896222 818 3020
918 7227266 919 2823
1169 2055777 1170 2465
2946 5010168 2947 1385
3015 2705460 3016 1361
3594 3970945 3595 1206
Process Group PGID: 2073
## Step 69 · agent · 2026-09-29T02:39:25.748Z
### Recorded reasoning summary
Analyzing Correction Impact
I'm reviewing the logs from `exp2_corr0.parquet` and noticed a shift in positive logits compared to `exp1`. This seems to confirm my suspicion that the Binary Cross-Entropy loss was disproportionately penalizing popular items, thus suppressing positive logit values. I'm now proceeding to investigate Hypothesis 2.
### run_shell_command · 2026-09-29T02:39:25.748Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/exp3_infonce_t1.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --loss-type infonce \\\n --temperature 1.0 && python /task/evaluate_public.py /app/solution/exp3_infonce_t1.parquet",
"description": "Test InfoNCE loss with temperature 1.0"
}
### Tool output · 2026-09-29T02:44:48.951Z
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=2.949887 running_bce=2.911879 elapsed=196.8s
epoch=1/1 train_loss=2.90219 validation_loss=2.73334 pair_accuracy=0.762 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.9 validation_seconds=15.3
restored best validation-BCE checkpoint: epoch=1 loss=2.733343
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp3_infonce_t1.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 315.468,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29,
"loss_type": "infonce",
"temperature": 1.0,
"inbatch_sampling": "unique-targets"
},
"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": 2.9021913749375017,
"positive_logit": 0.7543545090706908,
"negative_logit": -0.5814579418972261,
"pair_accuracy": 0.761820951409987,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.94195712699957,
"validation_loss": 2.7333433528989968,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.349176456999885
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7673325538635254,
"query_norm_std": 0.7159656286239624,
"item_norm_mean": 1.7566349506378174,
"item_norm_std": 0.8756554126739502
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp3_infonce_t1.parquet",
"scores": {
"quality_ndcg@10": 0.016056,
"quality_users": 4263
}
}
Process Group PGID: 2092
## Step 70 · agent · 2026-09-29T02:44:57.638Z
### Recorded reasoning summary
Analyzing Loss Behavior
My current focus is on understanding the discrepancy in performance between InfoNCE and BCE. It appears InfoNCE's raw dot product and limited negatives may be causing representation collapse. I'm now re-evaluating BCE to pinpoint its strengths in this context.
### Recorded reasoning summary
Exploring BCE Stability
I'm now investigating why BCE might be outperforming InfoNCE. The key seems to be BCE's inherent stability, stemming from its direct calibration of positive versus negative predictions. This contrasts with InfoNCE's potential for representation collapse due to raw dot products and limited negatives.
### read_file · 2026-09-29T02:44:57.638Z
{
"end_line": 765,
"start_line": 724,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T02:44:57.646Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 724-765 of 2223 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: 766.
--- FILE CONTENT (truncated) ---
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,
## Step 71 · agent · 2026-09-29T02:45:09.171Z
### Recorded reasoning summary
Analyzing Negative Filter Scopes
I'm currently scrutinizing the implications of employing `full-training-history` as the `negative_filter_scope`. My attention is specifically directed at the `draw_full_history_filtered_negatives` function, and within that, a closer examination of the `invalid_full_history_negatives` subroutine is underway.
### Recorded reasoning summary
Refining Invalid Negative Logic
I'm now examining how `known_positive_packed` interacts with `byte_columns` and `bit_columns` to construct the `known` array. My focus is also on the subsequent logical OR operations that incorporate `targets` and `liked_items` to define `invalid`. I'll be cross-referencing this with `build_known_positive_filter` in `train_current_model.py` to ensure comprehensive coverage.
### Recorded reasoning summary
Investigating Training Counts
I'm now scrutinizing how `training_counts` is utilized within the `build_known_positive` logic. Specifically, I'm examining its role in the calculation of `items` and how it impacts the `packed` array construction, paying attention to the `target_count` to `offset` relationship.
### Recorded reasoning summary
Exploring Training Counts Origin
I am now investigating the origin of `partition.history_training_counts` called in `train_model`. My focus is on how `chronological_target_partition` calculates `history_training` by subtracting `validation` from `totals`, and its interaction with `max_train_targets_per_user`.
### Recorded reasoning summary
Confirming Known Positive Packing
I've confirmed that `known_positive_packed` indeed incorporates all user listening history from the training period. This means any sampled negative item present in this set is correctly identified and discarded as a false negative. My next step is to analyze the logic governing the selection of negatives that *are* sampled.
### Recorded reasoning summary
Adjusting Negative Sampling Ratios
I'm now exploring the impact of altering the ratio between uniform and in-batch negatives. Specifically, I'm considering combinations like 8 uniform and 24 in-batch, or 16 and 16, to see how this affects training time and the model's exposure to harder negative examples.
### run_shell_command · 2026-09-29T02:45:09.171Z
{
"description": "Test uniform-negatives 8 and inbatch-negatives 24 with discovery-graded weighting",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/exp4_u8_ib24.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 8 \\\n --inbatch-negatives 24 && python /task/evaluate_public.py /app/solution/exp4_u8_ib24.parquet"
}
### Tool output · 2026-09-29T02:50:03.929Z
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=0.563400 running_bce=0.568936 elapsed=169.8s
epoch=1/1 train_loss=0.56679 validation_loss=0.53532 pair_accuracy=0.692 train_targets=1,052,953 validation_targets=130,103 train_seconds=181.3 validation_seconds=12.4
restored best validation-BCE checkpoint: epoch=1 loss=0.535323
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp4_u8_ib24.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 286.978,
"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": 8,
"inbatch_negatives": 24,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29,
"loss_type": "bce",
"temperature": 1.0,
"inbatch_sampling": "unique-targets"
},
"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": 0.5667940349984629,
"positive_logit": 0.6244476271847336,
"negative_logit": -0.37425631376128593,
"pair_accuracy": 0.6924440715777437,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 181.34427809499994,
"validation_loss": 0.5353229273367104,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 12.429464327000005
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7650986909866333,
"query_norm_std": 0.9053598046302795,
"item_norm_mean": 1.7711832523345947,
"item_norm_std": 0.778113842010498
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp4_u8_ib24.parquet",
"scores": {
"quality_ndcg@10": 0.018846,
"quality_users": 4263
}
}
Process Group PGID: 2141
## Step 72 · agent · 2026-09-29T02:50:14.978Z
### Recorded reasoning summary
Analyzing Performance Degradation
I'm investigating a puzzling performance drop. While training time improved, both NDCG and pair accuracy have decreased significantly. My current hypothesis centers on the interaction between `inbatch_negatives` and `uniform_negatives`, as the increase in negative logits appears correlated with these metrics' decline.
### run_shell_command · 2026-09-29T02:50:14.978Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/exp5_u32_ib0.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 32 \\\n --inbatch-negatives 0 && python /task/evaluate_public.py /app/solution/exp5_u32_ib0.parquet",
"description": "Test 32 uniform negatives and 0 inbatch negatives"
}
### Tool output · 2026-09-29T02:55:23.899Z
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=0.542264 running_bce=0.527347 elapsed=181.8s
epoch=1/1 train_loss=0.52544 validation_loss=0.48448 pair_accuracy=0.798 train_targets=1,052,953 validation_targets=130,103 train_seconds=193.6 validation_seconds=16.2
restored best validation-BCE checkpoint: epoch=1 loss=0.484480
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp5_u32_ib0.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 301.225,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 32,
"inbatch_negatives": 0,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29,
"loss_type": "bce",
"temperature": 1.0,
"inbatch_sampling": "unique-targets"
},
"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": 0.5254377371913913,
"positive_logit": 0.9194933282592246,
"negative_logit": -0.8096884129139443,
"pair_accuracy": 0.7977172592223964,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 193.605077661,
"validation_loss": 0.4844796389366467,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 16.17138235099992
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.4759684801101685,
"query_norm_std": 0.6978867053985596,
"item_norm_mean": 1.9009125232696533,
"item_norm_std": 0.8285666108131409
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp5_u32_ib0.parquet",
"scores": {
"quality_ndcg@10": 0.022328,
"quality_users": 4263
}
}
Process Group PGID: 2190
## Step 73 · agent · 2026-09-29T02:55:30.423Z
### Recorded reasoning summary
Testing Negative Sampling
My recent experiments show that a mix of in-batch and uniform negative sampling improves NDCG. Specifically, 8 in-batch and 24 uniform negatives yielded the best result, outperforming other combinations.
### run_shell_command · 2026-09-29T02:55:30.423Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/exp6_bfreq.parquet \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --inbatch-sampling batch-frequency \\\n --uniform-negatives 24 \\\n --inbatch-negatives 8 && python /task/evaluate_public.py /app/solution/exp6_bfreq.parquet",
"description": "Test inbatch-sampling batch-frequency"
}
### Tool output · 2026-09-29T03:00:33.933Z
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=0.556322 running_bce=0.550833 elapsed=176.9s
epoch=1/1 train_loss=0.54868 validation_loss=0.50483 pair_accuracy=0.753 train_targets=1,052,953 validation_targets=130,103 train_seconds=189.0 validation_seconds=15.0
restored best validation-BCE checkpoint: epoch=1 loss=0.504830
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp6_bfreq.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 295.765,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29,
"loss_type": "bce",
"temperature": 1.0,
"inbatch_sampling": "batch-frequency"
},
"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": 0.5486822848377702,
"positive_logit": 0.7235536047729897,
"negative_logit": -0.6189869582545096,
"pair_accuracy": 0.7531304816074411,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 188.995452786,
"validation_loss": 0.5048298135501154,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 14.992467433000002
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.639589548110962,
"query_norm_std": 0.8099101781845093,
"item_norm_mean": 1.9633010625839233,
"item_norm_std": 0.8413249254226685
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp6_bfreq.parquet",
"scores": {
"quality_ndcg@10": 0.01834,
"quality_users": 4263
}
}
Process Group PGID: 2241
## Step 74 · agent · 2026-09-29T03:00:41.712Z
### Recorded reasoning summary
Exploring Unique Targets
My recent analysis reveals that `unique-targets` with inbatch correction outperforms batch-frequency, achieving a score of 0.01834. I'm now delving into why the 24 uniform and 8 inbatch configuration for `unique-targets` is proving so effective.
### read_file · 2026-09-29T03:00:41.712Z
{
"start_line": 915,
"end_line": 990,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T03:00:41.727Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 915-990 of 2223 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: 991.
--- FILE CONTENT (truncated) ---
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 75 · agent · 2026-09-29T03:01:48.357Z
### Recorded reasoning summary
Refining Feature Weighting
I'm currently zeroing in on how to best assign weights based on specific feature thresholds. My focus is on refining the logic within `build_target_weights` to accurately capture 'organic_strong' instances by adjusting the bounds for the first two event features.
### Recorded reasoning summary
Adjusting Organic Weighting
I'm refining the logic to specifically capture 'organic_strong' cases. My current efforts are focused on tweaking the bounds within `build_target_weights` for the initial event features, aiming for a more precise assignment of weights based on these thresholds.
### Recorded reasoning summary
Evaluating Novelty Impact
I'm analyzing how item novelty within sequences affects target weighting, specifically when an item appears for the first time. My current thought process involves examining the `timestamps` and `horizon` to determine the appropriate weight for these novel occurrences, considering their recency relative to training and history boundaries.
### Recorded reasoning summary
Adjusting Organic Weights
I'm re-evaluating the `organic_like_target_weight` and `ordinary_target_weight` parameters. Considering the NDCG gain associated with 'grade 3' (organic-like) instances, I'm exploring increasing this weight significantly. I'm also contemplating decreasing the weight for 'ordinary' instances to encourage a stronger focus on true discovery targets.
### Recorded reasoning summary
Investigating Negative Sampling
I'm focusing on the training objective's use of negative examples. My current thoughts revolve around diagnosing limitations in the existing negative sampling strategy and exploring potential improvements, specifically considering an increase from the current 32 negatives to 48.
### Recorded reasoning summary
Exploring Negative Sampling Efficiency
I'm reassessing the impact of negative sampling numbers. It seems increasing uniform negatives from 24 to 48 or 64 won't significantly affect training time because the candidate catalog encoding already covers almost all items. The core computation increase is minor compared to MLP passes.
### Recorded reasoning summary
Analyzing Negative Loss Calculation
I'm examining the `corrected_negative_loss` function, specifically how it incorporates positive and negative logits within BCE. My current focus is on understanding the role of `correction` and `weights` in the in-batch negative sampling, and how this relates to the `score_target_microbatches` calculation.
### Recorded reasoning summary
Revising Loss Calculation
I'm reassessing the loss calculation, specifically the averaging of positive and negative losses. The current approach averages one positive example's loss with the average loss of 32 negatives. I'm considering weighting the negative loss more heavily to address potential class imbalance and improve learning.
### Recorded reasoning summary
Adjusting Loss Weighting
I'm investigating why negative examples aren't pushing logits down sufficiently. It appears the loss calculation gives the positive example 32 times more gradient influence than any single negative, leading to a poor prediction probability for random negatives.
### Recorded reasoning summary
Increasing Negative Loss Weight
I'm realizing the current loss calculation unfairly favors positive examples. By not dividing the negative loss by the number of negatives, or by applying a multiplier, I can force the model to significantly reduce the logits for negative items. This will directly penalize false positives and better separate positives from negatives.
### Recorded reasoning summary
Refining Negative Sampling
I'm reassessing how negative examples are selected and utilized within the training objective. My focus is on diagnosing current limitations and exploring enhancements to the negative sampling strategy to improve performance within our CPU constraints.
### Recorded reasoning summary
Analyzing Data Inclusion
I'm examining the data feeding into the model. It seems `prepared.sequence_items` might exclude user likes, and `liked_items` may only consider a subset of active likes, potentially skewing the learning process by not fully capturing positive interactions.
### Recorded reasoning summary
Examining Hard Negatives
I'm now considering the concept of hard negatives, particularly how they relate to popular items being predicted for all users. My focus is on understanding why this occurs and how the candidate catalog's popularity distribution might be contributing to this uniform prediction behavior during training.
### Recorded reasoning summary
Sampling Hard Negatives
I'm realizing that my current negative sampling strategy isn't effectively penalizing high dot products for popular items. The model rarely encounters these popular items as negatives, and even then, their loss is heavily downweighted. I need to explore sampling negatives from a popularity distribution or increasing in-batch negatives.
### Recorded reasoning summary
Adjusting Correction Factors
I'm re-examining the `inbatch_correction_power` in `corrected_negative_loss`, which was incorrectly set to 0.5 in `exp4_u8_ib24`. This led to rare items being over-weighted and popular items under-weighted, negatively impacting NDCG. I need to correct this to properly balance the influence of in-batch negatives.
### Recorded reasoning summary
Analyzing Loss Calculation Terms
I'm dissecting the loss calculation formula, specifically the terms contributing to the final loss. I've realized the uniform negative term effectively sums the raw values, and the in-batch term calculates a weighted average. This is crucial for understanding how each negative type influences the overall loss.
### Recorded reasoning summary
Correcting Loss Averaging
I've identified a critical flaw: my loss calculation incorrectly averages negative losses instead of summing them. Standard BCE sums negative losses, but mine divides by the total number of negatives. This severely underweights the penalty for negative examples, allowing incorrect predictions to persist.
### Recorded reasoning summary
Increasing Negative Weighting
I've realized the loss calculation gives positive examples significantly more weight than individual negatives. My current focus is on testing different multipliers to increase the negative loss's influence, potentially by adjusting the weight of each negative example relative to the positive.
### Recorded reasoning summary
Scaling Negative Influence
I'm considering introducing a `negative_weight_scale` parameter to the loss function to amplify the penalty for negative examples. Testing values like 1.5, 2.0, and 3.0 could significantly push down negative logits and improve NDCG, as seen in prior experiments.
### Recorded reasoning summary
Revising Negative Sampling Strategy
I'm analyzing the substantial CPU time dedicated to drawing negatives and its impact on training. My focus is on identifying inefficiencies within `draw_full_history_filtered_negatives` and improving the strategy to fit within our existing computational constraints.
### Recorded reasoning summary
Correcting Negative Loss Calculation
I've identified a critical flaw where negative losses are averaged rather than summed. This severely underweights their penalty, allowing incorrect predictions to persist. I am now exploring how to scale their influence to better penalize false positives and improve NDCG.
### Recorded reasoning summary
Optimizing Negative Item Processing
I'm analyzing the computational overhead introduced by processing negative items, particularly the boolean operations and duplicate checks which create large intermediate tensors and loops. The repeated encoding of a large number of unique items in `score + backward` also significantly impacts training time. I am exploring a redesigned negative sampling strategy to mitigate these inefficiencies.
### Recorded reasoning summary
Increasing Training Epochs
I've discovered the model was only trained for a single epoch in the baseline due to computational constraints. Crucially, validation loss was still decreasing, indicating it hadn't converged. I'm now exploring the impact of increasing epochs to 2 or 3 to allow for further learning.
### Recorded reasoning summary
Optimizing Negative Drawing
I'm realizing that the majority of training time is spent drawing and encoding negatives. A batch of 5600 targets doesn't need 134,400 independent uniform draws. I'm exploring sampling a shared pool of uniform negatives for the entire batch to drastically reduce processing time.
### Recorded reasoning summary
Speeding Up Training
I've identified a massive bottleneck in drawing and encoding negative examples. By implementing a shared pool of uniform negatives per batch, I can reduce the number of items to encode from ~30,000 to ~2,000, resulting in a potential 7x overall speedup. This approach aligns with standard two-tower retrieval system practices.
### Recorded reasoning summary
Improving Negative Sampling Efficiency
I'm realizing that the current practice of drawing and encoding negatives independently for each target is incredibly inefficient. By implementing a shared pool of uniform negatives for the entire batch, I can drastically reduce the number of items needing encoding, potentially achieving a significant speedup without compromising model performance.
### Recorded reasoning summary
Diagnosing Sampling Inefficiencies
I'm realizing the current negative sampling is a massive CPU bottleneck. Drawing and encoding individual negatives for each target, especially with the extensive rejection sampling and propensity corrections, is consuming the entire budget and limiting training to just one epoch. I need a more efficient strategy.
## Recorded session usage
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