yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-r03-b9049e57
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T01:21:41.387Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
## Step 2 · user · 2026-09-29T01:21:41.428Z
# Improve a production music-discovery two-tower
You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team's next launch gate.
Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.
The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.
## Production Context
The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.
The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.
## What You Have
All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` window. For the final submission, that
validation window becomes observed history, and another 30-minute gap precedes
the hidden seven-day outcome window.
The package includes:
- `/task/current_model/train_current_model.py`: the production training and
ranking script. It can reproduce the public validation ranking and produce a
hidden-test submission after refitting.
- `/task/current_model/two_tower_model.py`: model primitives used by the
production script.
- `/task/evaluate_public.py`: the public validation evaluator for development.
- `/task/data/validation_eligible_candidates.npz` and
`/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for
the validation and hidden-test cutoffs.
- `/task/data/validation_preference_state.parquet` and
`/task/data/test_preference_state.parquet`: active explicit preference state
at the corresponding cutoffs.
- `/task/requirements.txt`: the exact offline Python distribution inventory.
You can reproduce the production public backtest with:
```sh
python /task/current_model/train_current_model.py \
--stage validation \
--public-dir /task \
--output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet
```
## Data
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action came
through a recommendation surface. A listen is a conventional positive at 50%
completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. For model development and selection, train only from `train_*` and score the
subsequent `val_*` outcomes.
2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
Validation candidates exclude all `train_*` encounters. Test candidates exclude
all `train_*` and `val_*` encounters. Future test interactions are hidden and
used only by the separate Harbor verifier.
## Discovery Objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.
## Deliverable
Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.
The ranking must:
- include the columns `uid`, `item_id`, and `rank`; only these columns are
evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
candidate set.
Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model-provider
connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
the deadline.
## Step 3 · agent · 2026-09-29T01:21:49.484Z
### Recorded reasoning summary
Refining Retrieval Performance
I'm now focusing on the production context and the strict performance requirements for our music discovery model. This involves carefully reviewing all prompt details to ensure our improvements align with real-world deployment needs, particularly concerning latency and accuracy.
### Recorded reasoning summary
Optimizing Ranking Logic
I'm digging into the negative sampling strategy within the training objective. My current focus is on diagnosing limitations and devising improvements that fit our strict CPU budget, all while adhering to the established serving and data boundaries.
### Recorded reasoning summary
Validating Model Outputs
I'm currently executing the reproduction commands to validate the model's performance using the provided public datasets and specific stage cutoffs. My focus is on ensuring the generated outputs accurately reflect the training and validation data parameters, especially the discovery quality objective.
### Recorded reasoning summary
Finalizing Submission Details
I'm now consolidating the implementation for final submission, ensuring all deliverables meet the exact specifications. This includes correctly formatting the submission file, including the required columns and adhering to the user and item constraints, while also preparing the improved training script.
### update_topic · 2026-09-29T01:21:49.484Z
{
"strategic_intent": "Research the codebase, reproduce the baseline validation score, and understand the model and negative sampling implementation.",
"title": "Initial Research and Baseline Reproduction",
"summary": "Starting the music-discovery two-tower project. We will inspect the task files, understand the data structures and model implementation, reproduce the baseline validation performance, and analyze the negative sampling and training objective."
}
### Tool output · 2026-09-29T01:21:49.493Z
Current topic: "Initial Research and Baseline Reproduction"
Topic summary: Starting the music-discovery two-tower project. We will inspect the task files, understand the data structures and model implementation, reproduce the baseline validation performance, and analyze the negative sampling and training objective.
Strategic Intent: Research the codebase, reproduce the baseline validation score, and understand the model and negative sampling implementation.
## Step 4 · agent · 2026-09-29T01:21:59.563Z
### list_directory · 2026-09-29T01:21:59.563Z
{
"dir_path": "/task"
}
### Tool error · 2026-09-29T01:21:59.566Z
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 · 2026-09-29T01:22:01.547Z
### run_shell_command · 2026-09-29T01:22:01.547Z
{
"command": "ls -la /task; ls -la /task/current_model; ls -la /task/data",
"description": "Inspect task directory, current_model directory, and data directory"
}
### Tool output · 2026-09-29T01:22:01.586Z
Output: total 40
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 .
drwxr-xr-x 1 root root 4096 Sep 29 01:21 ..
-r--r--r-- 1 root root 0 Sep 29 01:16 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:16 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 data
-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:16 requirements.txt
total 104
dr-xr-xr-x 1 root root 4096 Sep 29 01:16 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 ..
-r--r--r-- 1 root root 80058 Sep 29 01:16 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 01:16 two_tower_model.py
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 ..
-r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet
Process Group PGID: 1612
## Step 6 · agent · 2026-09-29T01:22:05.816Z
### run_shell_command · 2026-09-29T01:22:05.816Z
{
"description": "List files in /app and /app/solution",
"command": "ls -la /app; ls -la /app/solution"
}
### Tool output · 2026-09-29T01:22:05.831Z
Output: total 12
drwxr-xr-x 3 agent agent 4096 Sep 29 01:18 .
drwxr-xr-x 1 root root 4096 Sep 29 01:21 ..
drwxr-xr-x 2 agent agent 4096 Sep 29 01:18 solution
total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 01:18 .
drwxr-xr-x 3 agent agent 4096 Sep 29 01:18 ..
Process Group PGID: 1617
## Step 7 · agent · 2026-09-29T01:22:08.278Z
### Recorded reasoning summary
Organizing workspace and resources
I'm currently copying the code from the `/task/current_model/` directory to `/app/solution/` for easier access. Simultaneously, I'm reviewing the available CPU cores and memory to get a clear picture of my computational environment.
### run_shell_command · 2026-09-29T01:22:08.278Z
{
"description": "Copy code to /app/solution/ and check system resources",
"command": "cp -r /task/current_model/* /app/solution/ && cp /task/evaluate_public.py /app/solution/ && cp /task/ranking_evaluation.py /app/solution/\nlscpu | grep \"Model name\\|CPU(s):\"\nfree -h\n"
}
### Tool output · 2026-09-29T01:22:08.305Z
Output: CPU(s): 8
Model name: AMD EPYC 7R13 Processor
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 6: free: command not found
Exit Code: 127
Process Group PGID: 1621
## Step 8 · agent · 2026-09-29T01:22:09.894Z
### run_shell_command · 2026-09-29T01:22:09.894Z
{
"command": "cat /proc/meminfo | head -n 5",
"description": "Check system memory"
}
### Tool output · 2026-09-29T01:22:09.906Z
Output: MemTotal: 15988968 kB
MemFree: 1419612 kB
MemAvailable: 14482864 kB
Buffers: 247820 kB
Cached: 12490908 kB
Process Group PGID: 1629
## Step 9 · agent · 2026-09-29T01:22:12.296Z
### Recorded reasoning summary
Running Baseline Reproduction
I'm initiating the baseline reproduction command to assess initial performance. This involves running the provided Python script to establish a performance benchmark on the current hardware configuration.
### read_file · 2026-09-29T01:22:12.296Z
{
"end_line": 150,
"start_line": 1,
"file_path": "/app/solution/evaluate_public.py"
}
### Tool output · 2026-09-29T01:22:12.302Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py "
)
## Step 10 · agent · 2026-09-29T01:22:15.137Z
### run_shell_command · 2026-09-29T01:22:15.137Z
{
"command": "wc -l /app/solution/train_current_model.py /app/solution/two_tower_model.py",
"description": "Count lines in train_current_model.py and two_tower_model.py"
}
### Tool output · 2026-09-29T01:22:15.148Z
Output: 2164 /app/solution/train_current_model.py
433 /app/solution/two_tower_model.py
2597 total
Process Group PGID: 1633
## Step 11 · agent · 2026-09-29T01:22:17.058Z
### grep_search · 2026-09-29T01:22:17.058Z
{
"dir_path": "/app/solution",
"pattern": "^(class |def |if __name__)",
"include_pattern": "train_current_model.py"
}
### Tool output · 2026-09-29T01:22:17.072Z
Found 44 matches for pattern "^(class |def |if __name__)" in path "/app/solution" (filter: "train_current_model.py"):
---
File: train_current_model.py
L41: class Config:
L76: class CandidateSet:
L99: class SequenceData:
L127: class TargetPartition:
L140: def log(message: str) -> None:
L144: def seed_everything(seed: int) -> None:
L150: def model_config(config: Config) -> base.TowerConfig:
L174: def append_state_snapshot(
L190: def causal_activity_features(
L247: def build_sequence_parts(
L314: def final_user_features(
L341: def prepare_data(
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L481: def save_cache(
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L511: def load_or_prepare(
L534: def build_item_correction(
L578: def build_known_positive_filter(
L609: def invalid_full_history_negatives(
L630: def draw_full_history_filtered_negatives(
L724: def draw_training_negatives(
L763: def event_sequence(
L774: def pool_explicit_states(
L798: def user_queries(
L813: def candidate_vectors(
L828: def history_windows(
L845: def corrected_negative_loss(
L867: def retained_target_window_starts(
L888: def chronological_target_partition(
L914: def load_organic_like_outcomes(
L955: def build_target_weights(
L1035: def build_dense_block(
L1111: def score_target_microbatches(
L1198: def release_accelerator_cache(device: torch.device) -> None:
L1203: def run_training_epoch(
L1364: def evaluate_validation_bce(
L1488: def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
L1495: def save_epoch_checkpoint(
L1521: def train_model(
L1782: def inference_queries(
L1861: def rank_candidates(
L1912: def write_ranking(frame: pd.DataFrame, output: Path) -> None:
L1922: def main() -> int:
L2163: if __name__ == "__main__":
---
## Step 12 · agent · 2026-09-29T01:22:20.885Z
### Recorded reasoning summary
Refining Negative Sampling
I'm currently focused on optimizing how we handle negative samples. My latest efforts involve examining specific function names, like `invalid_full_history_negatives` and `draw_training_negatives`, to identify potential improvements in their logic and efficiency, particularly around `corrected_negative_loss` and weight building.
### run_shell_command · 2026-09-29T01:22:20.885Z
{
"description": "Run baseline production validation training and evaluate public score",
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T01:28:27.566Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=198.0s
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=211.0 validation_seconds=15.5
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": 358.832,
"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": 0.5398380451095077,
"positive_logit": 0.742779958851944,
"negative_logit": -0.6222247018676265,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 211.018207221,
"validation_loss": 0.4966362150129709,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.520032933000039
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497010231018,
"item_norm_mean": 1.7570281028747559,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1638
## Step 13 · agent · 2026-09-29T01:28:31.584Z
### Recorded reasoning summary
Completing Baseline Reproduction
The initial reproduction run has concluded. I observed a runtime of approximately 358 seconds. The quality_ndcg@10 score registered at 0.02128 for 4,263 users. I'm now examining the generated JSON output and diagnostics.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now diving into `train_current_model.py` and `two_tower_model.py` to scrutinize the negative sampling strategy and its impact on the training objective. My focus is on understanding how these samples are selected and utilized within the loss calculation, aiming to identify potential limitations for future improvements.
### read_file · 2026-09-29T01:28:31.584Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 40,
"end_line": 140
}
### Tool output · 2026-09-29T01:28:31.592Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 40-140 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: 141.
--- FILE CONTENT (truncated) ---
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
item_ids: np.ndarray
packed: np.ndarray
@classmethod
def read(cls, path: Path) -> "CandidateSet":
with np.load(path, allow_pickle=False) as artifact:
result = cls(
uids=artifact["uids"].astype(np.int64, copy=True),
item_ids=artifact["item_ids"].astype(np.int64, copy=True),
packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
)
expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
if result.packed.shape != expected:
raise ValueError(
f"invalid candidate artifact shape {result.packed.shape}; "
f"expected {expected}"
)
return result
@dataclass(frozen=True)
class SequenceData:
item_ids: np.ndarray
item_features: np.ndarray
user_ids: np.ndarray
offsets: np.ndarray
sequence_timestamps: np.ndarray
sequence_items: np.ndarray
event_features: np.ndarray
activity_features: np.ndarray
state_ids: np.ndarray
liked_states: np.ndarray
disliked_states: np.ndarray
state_counts: np.ndarray
inference_user_rows: np.ndarray
inference_liked_items: np.ndarray
inference_disliked_items: np.ndarray
inference_user_features: np.ndarray
@property
def lengths(self) -> np.ndarray:
return np.diff(self.offsets)
@property
def target_count(self) -> int:
return int(np.maximum(self.lengths - 1, 0).sum())
@dataclass(frozen=True)
class TargetPartition:
"""A contiguous recent target window and its chronological BCE split."""
window_starts: np.ndarray
training_counts: np.ndarray
validation_counts: np.ndarray
history_training_counts: np.ndarray
@property
def selected_count(self) -> int:
return int(self.training_counts.sum() + self.validation_counts.sum())
def log(message: str) -> None:
## Step 14 · agent · 2026-09-29T01:28:33.899Z
### Recorded reasoning summary
Analyzing Negative Sampling
I'm currently examining the functions responsible for negative sampling and loss calculation, specifically focusing on `build_item_correction`, `build_known_positive_filter`, `invalid_full_history_negatives`, `draw_full_history_filtered_negatives`, `draw_training_negatives`, and `corrected_negative_loss`. This analysis will help refine our approach to generating and incorporating negative samples into the training process.
### read_file · 2026-09-29T01:28:33.899Z
{
"start_line": 530,
"file_path": "/app/solution/train_current_model.py",
"end_line": 650
}
### Tool output · 2026-09-29T01:28:33.905Z
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 15 · agent · 2026-09-29T01:28:54.417Z
### read_file · 2026-09-29T01:28:54.417Z
{
"end_line": 780,
"start_line": 651,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:28:54.423Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 651-780 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: 781.
--- 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)
## Step 16 · agent · 2026-09-29T01:28:58.791Z
### read_file · 2026-09-29T01:28:58.791Z
{
"end_line": 950,
"file_path": "/app/solution/train_current_model.py",
"start_line": 780
}
### Tool output · 2026-09-29T01:28:58.798Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 780-950 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 951.
--- FILE CONTENT (truncated) ---
unique_states, inverse = np.unique(state_ids, return_inverse=True)
liked_items = torch.from_numpy(
prepared.liked_states[unique_states].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.disliked_states[unique_states].astype(np.int64)
).to(device)
liked_vectors = model._mean_context_channel(liked_items)
disliked_vectors = model._mean_context_channel(disliked_items)
inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
counts = prepared.state_counts[state_ids]
return (
liked_vectors[inverse_rows],
disliked_vectors[inverse_rows],
torch.from_numpy(counts.astype(np.float32)).to(device),
)
def user_queries(
model: base.SequentialTwoTower,
sequence_vectors: torch.Tensor,
liked_vectors: torch.Tensor,
disliked_vectors: torch.Tensor,
user_features: torch.Tensor,
) -> torch.Tensor:
return model.user_tower(
torch.cat(
[sequence_vectors, liked_vectors, disliked_vectors, user_features],
dim=1,
)
)
def candidate_vectors(
model: base.SequentialTwoTower,
positives: torch.Tensor,
negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
all_items = torch.cat([positives, negatives.reshape(-1)])
unique_items, inverse = torch.unique(all_items, return_inverse=True)
encoded = model.encode_items(unique_items)
positive_vectors = encoded[inverse[: len(positives)]]
negative_vectors = encoded[inverse[len(positives) :]].reshape(
len(positives), negatives.shape[1], -1
)
return positive_vectors, negative_vectors
def history_windows(
sequence: np.ndarray,
start: int,
count: int,
width: int,
padding_item: int,
) -> np.ndarray:
base_start = max(0, start - width + 1)
segment = sequence[base_start : start + count]
padded = np.concatenate(
[np.full(width - 1, padding_item, dtype=np.uint16), segment]
)
windows = np.lib.stride_tricks.sliding_window_view(padded, width)
offset = start - base_start
return windows[offset : offset + count].copy()
def corrected_negative_loss(
logits: torch.Tensor,
correction: torch.Tensor,
config: Config,
) -> torch.Tensor:
raw = F.softplus(logits)
total = config.uniform_negatives + config.inbatch_negatives
result = torch.zeros(len(logits), device=logits.device)
if config.uniform_negatives:
result += config.uniform_negatives * raw[
:, : config.uniform_negatives
].mean(dim=1)
if config.inbatch_negatives:
selected = slice(config.uniform_negatives, total)
weights = correction[:, selected]
result += config.inbatch_negatives * (
(raw[:, selected] * weights).sum(dim=1)
/ weights.sum(dim=1).clamp_min(1.0e-6)
)
return result / total
def retained_target_window_starts(
total_targets: np.ndarray,
validation_counts: np.ndarray,
training_counts: np.ndarray,
) -> np.ndarray:
"""Return the discarded prefix before the retained chronological suffix."""
training_region_ends = total_targets - validation_counts
window_starts = training_region_ends - training_counts
if (
np.any(window_starts < 0)
or np.any(training_counts < 0)
or np.any(validation_counts < 0)
):
raise RuntimeError("chronological target partition produced negative counts")
if not np.array_equal(
window_starts + training_counts + validation_counts, total_targets
):
raise RuntimeError("chronological target window is not a final contiguous suffix")
return window_starts
def chronological_target_partition(
prepared: SequenceData,
validation_fraction: float,
max_train_targets_per_user: int,
max_validation_targets_per_user: int,
) -> TargetPartition:
"""Select a recent contiguous window with a final out-of-time BCE suffix."""
totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
validation = np.floor(totals * validation_fraction).astype(np.int64)
validation[(totals >= 2) & (validation == 0)] = 1
if max_validation_targets_per_user:
validation = np.minimum(validation, max_validation_targets_per_user)
history_training = totals - validation
training = history_training.copy()
if max_train_targets_per_user:
training = np.minimum(training, max_train_targets_per_user)
window_starts = retained_target_window_starts(totals, validation, training)
return TargetPartition(
window_starts=window_starts,
training_counts=training,
validation_counts=validation,
history_training_counts=history_training,
)
def load_organic_like_outcomes(
public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
"""Load mapped organic-like timestamps from public fitting history."""
prefixes = ("train",) if stage == "validation" else ("train", "val")
frames = [
pd.read_parquet(
public / "data" / f"{prefix}_likes.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
for prefix in prefixes
]
likes = pd.concat(frames, ignore_index=True)
likes = likes[likes.is_organic == 1].copy()
mapped = pd.Index(item_ids).get_indexer(
likes.item_id.to_numpy(dtype=np.int64)
)
likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
nested: dict[int, dict[int, list[int]]] = {}
for raw_uid, raw_timestamp, raw_item in likes.itertuples(
index=False, name=None
):
nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
int(raw_timestamp)
)
result = {
uid: {
item: np.asarray(timestamps, dtype=np.int64)
for item, timestamps in items.items()
}
for uid, items in nested.items()
}
log(
f"loaded organic-like outcomes: rows={len(likes):,} "
f"users={len(result):,}"
## Step 17 · agent · 2026-09-29T01:29:00.897Z
### read_file · 2026-09-29T01:29:00.897Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 951,
"end_line": 1100
}
### Tool output · 2026-09-29T01:29:00.905Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 951-1100 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: 1101.
--- FILE CONTENT (truncated) ---
)
return result
def build_target_weights(
public: Path,
stage: str,
prepared: SequenceData,
training_counts: np.ndarray,
config: Config,
) -> np.ndarray:
"""Build nonzero per-token weights without dropping adjacent targets."""
if config.target_weighting == "uniform":
weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
else:
weights = np.full(
len(prepared.sequence_items),
config.ordinary_target_weight,
dtype=np.float32,
)
organic_strong = (
(prepared.event_features[:, 0] > 0.5)
& (prepared.event_features[:, 1] >= 0.8)
)
weights[organic_strong] = config.organic_strong_target_weight
if config.target_weighting == "discovery-graded":
like_outcomes = load_organic_like_outcomes(
public, stage, prepared.item_ids
)
history_end = (
base.TRAIN_END if stage == "validation" else base.VALIDATION_END
)
horizon = config.discovery_horizon_days * base.DAY
grade_three = 0
for user_row, uid in enumerate(prepared.user_ids):
start = int(prepared.offsets[user_row])
end = int(prepared.offsets[user_row + 1])
if end - start < 2:
continue
last_training_target = start + int(training_counts[user_row])
first_validation_target = last_training_target + 1
split_timestamp = (
int(prepared.sequence_timestamps[first_validation_target])
if first_validation_target < end
else history_end
)
user_likes = like_outcomes.get(int(uid), {})
seen = {int(prepared.sequence_items[start])}
for position in range(start + 1, end):
item = int(prepared.sequence_items[position])
novel = item not in seen
seen.add(item)
if not novel:
# Repeats remain training examples, but they cannot be
# eligible discovery outcomes at the corresponding
# cutoff and therefore keep only the ordinary weight.
weights[position] = config.ordinary_target_weight
continue
timestamps = user_likes.get(item)
if timestamps is None:
continue
timestamp = int(prepared.sequence_timestamps[position])
upper = min(
timestamp + horizon,
split_timestamp
if position <= last_training_target
else history_end,
)
outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
weights[position] = config.organic_like_target_weight
grade_three += 1
log(
f"grade-3 first-encounter targets={grade_three:,} "
f"horizon_days={config.discovery_horizon_days}"
)
log(
f"target weighting={config.target_weighting} "
f"min={weights.min():.3f} mean={weights.mean():.3f} "
f"max={weights.max():.3f}"
)
return weights
def build_dense_block(
prepared: SequenceData,
target_weights: np.ndarray,
users: np.ndarray,
window_starts: np.ndarray,
process_counts: np.ndarray,
score_starts: np.ndarray,
start: int,
steps: int,
config: Config,
) -> tuple[np.ndarray, ...]:
"""Build one causal sequence block and mark the target rows to score."""
batch_size = len(users)
padding_item = len(prepared.item_ids)
input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
targets = np.zeros((batch_size, steps), dtype=np.uint16)
weights = np.ones((batch_size, steps), dtype=np.float32)
scored = np.zeros((batch_size, steps), dtype=bool)
histories = np.full(
(batch_size, steps, config.bptt_steps),
padding_item,
dtype=np.uint16,
)
processed = np.zeros(batch_size, dtype=np.int32)
lengths = prepared.lengths
for row, user_row in enumerate(users):
count = min(steps, max(int(process_counts[row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[user_row])
window_start = int(window_starts[row])
absolute_start = window_start + start
positions = slice(
offset + absolute_start,
offset + absolute_start + count,
)
target_positions = slice(
offset + absolute_start + 1,
offset + absolute_start + count + 1,
)
input_items[row, :count] = prepared.sequence_items[positions]
event_features[row, :count] = prepared.event_features[positions]
activity_features[row, :count] = prepared.activity_features[positions]
state_ids[row, :count] = prepared.state_ids[positions]
targets[row, :count] = prepared.sequence_items[target_positions]
weights[row, :count] = target_weights[target_positions]
score_from = min(count, max(int(score_starts[row]) - start, 0))
scored[row, score_from:count] = True
processed[row] = count
user_sequence = prepared.sequence_items[
offset : offset + int(lengths[user_row])
]
histories[row, :count] = history_windows(
user_sequence,
absolute_start,
count,
config.bptt_steps,
padding_item,
)
return (
input_items,
event_features,
## Step 18 · agent · 2026-09-29T01:29:02.646Z
### read_file · 2026-09-29T01:29:02.646Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1350,
"start_line": 1100
}
### Tool output · 2026-09-29T01:29:02.652Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1100-1350 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1351.
--- FILE CONTENT (truncated) ---
event_features,
activity_features,
state_ids,
targets,
scored,
histories,
processed,
weights,
)
def score_target_microbatches(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
sequence_vectors: torch.Tensor,
flat_states: np.ndarray,
flat_activity: np.ndarray,
positive_items: torch.Tensor,
negatives: torch.Tensor,
negative_weights: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
"""Compute one logical block's exact BCE in bounded-memory slices."""
target_count = len(positive_items)
total_weight = float(sample_weights.sum())
if total_weight <= 0:
raise RuntimeError("target block has no positive sample weight")
sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
statistics = {
"loss_sum": 0.0,
"positive_sum": 0.0,
"negative_sum": 0.0,
"pair_correct": 0.0,
"weight_sum": 0.0,
}
for begin in range(0, target_count, config.loss_microbatch_size):
end = min(begin + config.loss_microbatch_size, target_count)
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
def run_training_epoch(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
generator: torch.Generator,
device: torch.device,
epoch: int,
) -> dict[str, float]:
model.train()
train_users = np.flatnonzero(target_counts > 0)
sorted_users = train_users[
np.argsort(target_counts[train_users], kind="stable")
]
groups = [
sorted_users[start : start + config.user_batch_size]
for start in range(0, len(sorted_users), config.user_batch_size)
]
group_order = torch.randperm(len(groups), generator=generator).numpy()
negative_count = config.uniform_negatives + config.inbatch_negatives
total_loss = 0.0
total_targets = 0
total_weight = 0.0
total_positive = 0.0
total_negative = 0.0
total_pair_correct = 0.0
batches = 0
started = time.monotonic()
for group_number in group_order:
users = groups[int(group_number)]
group_counts = target_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_counts.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_counts,
np.zeros(len(users), dtype=np.int64),
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0).detach()
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
## Step 19 · agent · 2026-09-29T01:29:04.407Z
### read_file · 2026-09-29T01:29:04.407Z
{
"file_path": "/app/solution/two_tower_model.py",
"start_line": 1,
"end_line": 200
}
### Tool output · 2026-09-29T01:29:04.413Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-200 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: 201.
--- FILE CONTENT (truncated) ---
"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn
DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100
@dataclass(frozen=True)
class TowerConfig:
sequence_length: int = 100
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
seed: int = 29
@dataclass(frozen=True)
class ListenEvents:
timestamps: np.ndarray
items: np.ndarray
organic: np.ndarray
completion_pct: np.ndarray
@dataclass(frozen=True)
class ExplicitEvents:
timestamps: np.ndarray
items: np.ndarray
channel: np.ndarray
active: np.ndarray
organic_like: np.ndarray
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
"""Return four standardized features built only from the public catalog."""
positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
features = np.column_stack(
[
np.log1p(positives),
np.log1p(organic),
organic / np.maximum(positives, 1.0),
1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
]
)
means = features.mean(axis=0, keepdims=True)
scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
standardized = ((features - means) / scales).astype(np.float32)
if not np.isfinite(standardized).all():
raise RuntimeError("catalog features contain non-finite values")
return standardized
def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
if len(parts) == 1:
return parts[0]
timestamps = np.concatenate([part.timestamps for part in parts])
items = np.concatenate([part.items for part in parts])
organic = np.concatenate([part.organic for part in parts])
completion = np.concatenate([part.completion_pct for part in parts])
order = np.argsort(timestamps, kind="stable")
return ListenEvents(
timestamps=timestamps[order],
items=items[order],
organic=organic[order],
completion_pct=completion[order],
)
def iter_listen_users(
path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Stream one uid-sorted public listen file one user at a time."""
parquet = pq.ParquetFile(path)
pending_uid: int | None = None
pending: list[ListenEvents] = []
previous_uid = -1
for batch in parquet.iter_batches(
batch_size=750_000,
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
):
frame = batch.to_pandas()
mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
if not np.any(keep):
continue
uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
items = mapped[keep].astype(np.int32, copy=False)
organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
if np.any(uids[1:] < uids[:-1]):
raise RuntimeError(f"{path.name} is not sorted by uid")
boundaries = np.r_[
0,
np.flatnonzero(uids[1:] != uids[:-1]) + 1,
len(uids),
]
for start, end in zip(boundaries[:-1], boundaries[1:]):
uid = int(uids[start])
part = ListenEvents(
timestamps[start:end].copy(),
items[start:end].copy(),
organic[start:end].copy(),
completion[start:end].copy(),
)
if pending_uid is None:
pending_uid = uid
elif uid != pending_uid:
if pending_uid < previous_uid:
raise RuntimeError(f"{path.name} user order regressed")
yield pending_uid, _combine_listen_chunks(pending)
previous_uid = pending_uid
pending_uid = uid
pending = []
pending.append(part)
if pending_uid is not None:
yield pending_uid, _combine_listen_chunks(pending)
def merge_listen_streams(
paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Merge uid-sorted public listen files without materializing them."""
streams = [iter(iter_listen_users(path, item_index)) for path in paths]
heads: list[tuple[int, ListenEvents] | None] = [
next(stream, None) for stream in streams
]
while any(head is not None for head in heads):
uid = min(head[0] for head in heads if head is not None)
parts: list[ListenEvents] = []
for position, head in enumerate(heads):
if head is not None and head[0] == uid:
parts.append(head[1])
heads[position] = next(streams[position], None)
yield uid, _combine_listen_chunks(parts)
def load_explicit_events(
data: Path,
prefixes: tuple[str, ...],
item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
"""Load public explicit actions as chronological state changes."""
specifications = (
("likes", 0, 1, 0),
("unlikes", 0, 0, 1),
("dislikes", 1, 1, 0),
("undislikes", 1, 0, 1),
)
parts: list[pd.DataFrame] = []
for family, channel, active, tie_priority in specifications:
for prefix in prefixes:
frame = pd.read_parquet(
data / f"{prefix}_{family}.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
mapped = item_index.get_indexer(
frame.item_id.to_numpy(dtype=np.int64, copy=False)
)
keep = mapped >= 0
if not np.any(keep):
continue
retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
## Step 20 · agent · 2026-09-29T01:29:07.302Z
### read_file · 2026-09-29T01:29:07.302Z
{
"file_path": "/app/solution/two_tower_model.py",
"start_line": 201,
"end_line": 434
}
### Tool output · 2026-09-29T01:29:07.308Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 201-434 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 435.
--- FILE CONTENT (truncated) ---
retained["item"] = mapped[keep].astype(np.int32, copy=False)
retained["channel"] = np.int8(channel)
retained["active"] = np.int8(active)
retained["tie_priority"] = np.int8(tie_priority)
retained["organic_like"] = np.int8(
family == "likes"
) * retained.is_organic.astype(np.int8)
parts.append(retained.drop(columns="is_organic"))
if not parts:
return {}
events = pd.concat(parts, ignore_index=True)
events = events.sort_values(
["uid", "timestamp", "tie_priority"], kind="mergesort"
).reset_index(drop=True)
result: dict[int, ExplicitEvents] = {}
for uid, rows in events.groupby("uid", sort=False):
result[int(uid)] = ExplicitEvents(
timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
items=rows.item.to_numpy(dtype=np.int32, copy=True),
channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
active=rows.active.to_numpy(dtype=np.int8, copy=True),
organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
)
log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
return result
def empty_explicit_events() -> ExplicitEvents:
return ExplicitEvents(
timestamps=np.empty(0, dtype=np.int64),
items=np.empty(0, dtype=np.int32),
channel=np.empty(0, dtype=np.int8),
active=np.empty(0, dtype=np.int8),
organic_like=np.empty(0, dtype=np.int8),
)
def pack_active_items(
active: dict[int, int], width: int, padding_item: int
) -> np.ndarray:
packed = np.full(width, padding_item, dtype=np.uint16)
if not active:
return packed
ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
packed[: len(ordered)] = np.fromiter(
(item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
)
return packed
def load_inference_state(
path: Path,
item_index: pd.Index,
uids: np.ndarray,
n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Pack the task-provided active explicit state."""
state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
state = state.loc[keep, ["uid", "state"]].copy()
state["item"] = mapped[keep].astype(np.int32, copy=False)
user_rows = pd.Index(uids)
liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
like_counts = np.zeros(len(uids), dtype=np.int32)
dislike_counts = np.zeros(len(uids), dtype=np.int32)
for value, width, destination, counts in (
("liked", LIKE_ITEMS, liked, like_counts),
("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
):
selected = state[state.state == value]
for uid, rows in selected.groupby("uid", sort=False):
row = int(user_rows.get_indexer([int(uid)])[0])
if row < 0:
continue
items = np.sort(rows.item.unique())
counts[row] = len(items)
retained = items[:width]
destination[row, : len(retained)] = retained.astype(np.uint16)
return liked, disliked, like_counts, dislike_counts
class SequentialTwoTower(nn.Module):
"""Order-sensitive user tower and independent catalog-aware item tower."""
def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
super().__init__()
n_items, n_item_features = item_features.shape
dim = config.embedding_dim
self.n_items = n_items
self.context_embedding = nn.Embedding(
n_items + 1, dim, padding_idx=n_items
)
self.event_projection = nn.Sequential(
nn.Linear(dim + 3, config.hidden_dim),
nn.GELU(),
)
self.sequence_encoder = nn.GRU(
input_size=config.hidden_dim,
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 21 · agent · 2026-09-29T01:29:19.951Z
### Recorded reasoning summary
Analyzing Latency Budget
I'm currently dissecting the prompt's constraints, specifically the strict request-time latency budget. My focus is on understanding how the precomputation of item vectors offline and the on-the-fly computation of user vectors impacts this crucial performance metric.
### Recorded reasoning summary
Improving Negative Sampling
My current focus is on refining the negative sampling strategy within the training objective. I'm analyzing how the trainer selects and utilizes these examples, diagnosing existing limitations, and aiming to enhance the process within the fixed CPU budget. I will investigate the loss function, in-batch versus uniform negatives, and various negative weighting techniques to optimize performance.
### Recorded reasoning summary
Clarifying Training Objective
I'm currently investigating the specific training objective as defined in `train_current_model.py`. My immediate goal is to ascertain whether modifying the loss function falls within the scope of the existing objective or requires a broader re-evaluation of the training setup. I am examining the architecture summary for key details.
### Recorded reasoning summary
Investigating Loss Calculation
I'm currently examining the `corrected_negative_loss` function around lines 840-865 and its interplay with BCE loss. I'm also cross-referencing this with `score_target_microbatches`, specifically how `positive_logits` and `negative_logits` are computed to understand the full loss calculation process.
### Recorded reasoning summary
Refining Loss Computation
I'm currently examining the exact calculation within `corrected_negative_loss`. It appears `F.softplus(-positive_logits)` is equivalent to BCE loss for label 1, and `softplus(negative_logits)` is BCE loss for label 0. I need to understand how `negative_loss` is averaged and combined here, particularly when `uniform_negatives` is considered.
### grep_search · 2026-09-29T01:29:19.951Z
{
"include_pattern": "train_current_model.py",
"pattern": "add_argument",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T01:29:19.968Z
Found 27 matches for pattern "add_argument" in path "/app/solution" (filter: "train_current_model.py"):
---
File: train_current_model.py
L1924: parser.add_argument(
L1927: parser.add_argument("--output", type=Path, required=True)
L1928: parser.add_argument("--public-dir", type=Path, required=True)
L1929: parser.add_argument("--prepared-cache", type=Path)
L1930: parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
L1931: parser.add_argument("--threads", type=int, default=8)
L1932: parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
L1933: parser.add_argument(
L1936: parser.add_argument("--epochs", type=int, default=Config.epochs)
L1937: parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
L1938: parser.add_argument(
L1943: parser.add_argument(
L1948: parser.add_argument(
L1953: parser.add_argument(
L1958: parser.add_argument(
L1963: parser.add_argument(
L1968: parser.add_argument(
L1973: parser.add_argument(
L1978: parser.add_argument(
L1984: parser.add_argument(
L1990: parser.add_argument(
L1995: parser.add_argument(
L2000: parser.add_argument("--checkpoint-dir", type=Path)
L2001: parser.add_argument("--resume-checkpoint", type=Path)
L2002: parser.add_argument("--seed", type=int, default=Config.seed)
L2003: parser.add_argument("--ranking-batch-size", type=int, default=64)
L2004: parser.add_argument("--prepare-only", action="store_true")
---
## Step 22 · agent · 2026-09-29T01:29:25.995Z
### read_file · 2026-09-29T01:29:25.995Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1920,
"end_line": 2015
}
### Tool output · 2026-09-29T01:29:26.002Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1920-2015 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: 2016.
--- FILE CONTENT (truncated) ---
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
## Step 23 · agent · 2026-09-29T01:29:39.877Z
### read_file · 2026-09-29T01:29:39.877Z
{
"start_line": 2016,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2160
}
### Tool output · 2026-09-29T01:29:39.884Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2016-2160 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: 2161.
--- FILE CONTENT (truncated) ---
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
## Step 24 · agent · 2026-09-29T01:29:42.407Z
### read_file · 2026-09-29T01:29:42.407Z
{
"end_line": 535,
"start_line": 470,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:29:42.415Z
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 25 · agent · 2026-09-29T01:29:45.484Z
### Recorded reasoning summary
Caching Data Preparation
I've implemented caching for data preparation, allowing me to load pre-processed data using `--prepared-cache`. This significantly speeds things up, as evidenced by the recent validation run, which previously took about 50 seconds to process over 9,000 users and 20 million tokens.
### run_shell_command · 2026-09-29T01:29:45.484Z
{
"description": "Check available disk space",
"command": "df -h"
}
### Tool output · 2026-09-29T01:29:45.504Z
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: 1692
## Step 26 · agent · 2026-09-29T01:29:48.409Z
### Recorded reasoning summary
Preparing Validation Cache
I'm focusing on optimizing our iteration speed. To achieve this, I'm preparing and saving the validation cache. The plan is to run a specific script to ensure this crucial step is completed efficiently.
### run_shell_command · 2026-09-29T01:29:48.409Z
{
"description": "Prepare and cache validation sequence data",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --prepare-only"
}
### Tool output · 2026-09-29T01:29:52.453Z
Output: usage: train_current_model.py [-h] [--stage {validation,submission}] --output
OUTPUT --public-dir PUBLIC_DIR
[--prepared-cache PREPARED_CACHE]
[--device {cpu,mps}] [--threads THREADS]
[--bptt-steps BPTT_STEPS]
[--user-batch-size USER_BATCH_SIZE]
[--epochs EPOCHS]
[--learning-rate LEARNING_RATE]
[--loss-microbatch-size LOSS_MICROBATCH_SIZE]
[--negative-filter-scope {full-training-history,last-100}]
[--target-weighting {discovery-graded,organic-strong,uniform}]
[--ordinary-target-weight ORDINARY_TARGET_WEIGHT]
[--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]
[--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]
[--discovery-horizon-days DISCOVERY_HORIZON_DAYS]
[--validation-fraction VALIDATION_FRACTION]
[--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]
[--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]
[--early-stopping-patience EARLY_STOPPING_PATIENCE]
[--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]
[--checkpoint-dir CHECKPOINT_DIR]
[--resume-checkpoint RESUME_CHECKPOINT]
[--seed SEED]
[--ranking-batch-size RANKING_BATCH_SIZE]
[--prepare-only]
train_current_model.py: error: the following arguments are required: --output
Exit Code: 2
Process Group PGID: 1695
## Step 27 · agent · 2026-09-29T01:29:55.109Z
### run_shell_command · 2026-09-29T01:29:55.109Z
{
"description": "Build validation cache",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --output /app/solution/dummy.parquet \\\n --prepare-only"
}
### Tool output · 2026-09-29T01:30:49.504Z
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=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.9s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.3s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.1s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.8s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.3s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.5s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.2s
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/prepared_val.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": 50.384,
"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: 1706
## Step 28 · agent · 2026-09-29T01:30:51.603Z
### run_shell_command · 2026-09-29T01:30:51.603Z
{
"command": "ls -lh /app/solution/prepared_val.npz",
"description": "Check prepared_val.npz size"
}
### Tool output · 2026-09-29T01:30:51.620Z
Output: -rw-r--r-- 1 agent agent 511M Sep 29 01:30 /app/solution/prepared_val.npz
Process Group PGID: 1733
## Step 29 · agent · 2026-09-29T01:30:56.175Z
### Recorded reasoning summary
Optimizing Loading and Submission
I've significantly sped up the NPZ loading process, reducing it from 50 seconds to just 1. To ensure we always have a valid submission, I'm also implementing immediate generation of a baseline submission file as per the prompt's requirement to maintain the best valid submission throughout the attempt.
### Recorded reasoning summary
Validating Submission Logic
I'm now investigating the performance characteristics of the `--stage submission` command to understand its execution time. My current thought is to adapt `train_current_model.py` to leverage this stage, noting that it will train on combined train and validation data, a factor I need to account for.
### read_file · 2026-09-29T01:30:56.175Z
{
"start_line": 1850,
"file_path": "/app/solution/train_current_model.py",
"end_line": 1925
}
### Tool output · 2026-09-29T01:30:56.181Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1850-1925 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: 1926.
--- FILE CONTENT (truncated) ---
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"
## Step 30 · agent · 2026-09-29T01:31:01.946Z
### Recorded reasoning summary
Analyzing Candidate Ranking
I'm currently examining the `rank_candidates` function, specifically the matrix multiplication for scores and the unpacking of eligibility bits. My focus is on how these operations interact to determine candidate rankings.
### Recorded reasoning summary
Investigating Baseline Performance
I'm now dissecting how the `masked_fill` operation impacts candidate scores before top-k selection. This step seems crucial for understanding the initial filtering before ranking. I'm also noting the DataFrame creation and the specific requirement for submission stages.
### read_file · 2026-09-29T01:31:01.946Z
{
"end_line": 760,
"start_line": 570,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:31:01.952Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 570-760 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 761.
--- FILE CONTENT (truncated) ---
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
log(
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
)
return torch.from_numpy(correction)
def build_known_positive_filter(
prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
"""Pack each user's complete public-training positive-item set."""
width = (len(prepared.item_ids) + 7) // 8
packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
retained = 0
for user_row, target_count in enumerate(training_counts):
if not target_count:
continue
offset = int(prepared.offsets[user_row])
# T adjacent targets consume sequence positions 0 through T. The
# chronological BCE suffix starts at T + 1 and is intentionally absent.
items = np.unique(
prepared.sequence_items[
offset : offset + int(target_count) + 1
].astype(np.int64)
)
byte_columns = items >> 3
bit_values = np.left_shift(
np.uint8(1), (items & 7).astype(np.uint8)
)
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
retained += len(items)
log(
f"full-history false-negative filter: users={len(prepared.user_ids):,} "
f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
)
return packed
def invalid_full_history_negatives(
negatives: np.ndarray,
targets: np.ndarray,
user_rows: np.ndarray,
known_positive_packed: np.ndarray,
liked_items: np.ndarray,
) -> np.ndarray:
"""Return row-level invalid negatives using the complete training prefix."""
byte_columns = negatives >> 3
bit_columns = negatives & 7
known = known_positive_packed[user_rows[:, None], byte_columns]
invalid = ((known >> bit_columns) & 1).astype(bool)
invalid |= negatives == targets[:, None]
invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= (
negatives[:, column, None] == negatives[:, :column]
).any(axis=1)
return invalid
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
def draw_training_negatives(
positive_items: torch.Tensor,
known_history: torch.Tensor,
liked_items: torch.Tensor,
user_rows: np.ndarray,
known_positive_packed: np.ndarray | None,
prepared: SequenceData,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
if config.negative_filter_scope == "full-training-history":
if known_positive_packed is None:
raise RuntimeError("full-history negative filter was not built")
return draw_full_history_filtered_negatives(
positive_items,
user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
config,
item_correction,
generator,
device,
)
return base.draw_filtered_mixed_negatives(
positive_items,
known_history,
liked_items,
len(prepared.item_ids),
config.uniform_negatives,
config.inbatch_negatives,
item_correction,
generator,
device,
)
## Step 31 · agent · 2026-09-29T01:31:09.741Z
### Recorded reasoning summary
Exploring Negative Sampling
I'm currently delving into the intricacies of negative sampling during training. My focus is on understanding the mechanics of both uniform and in-batch negative selection strategies, specifically examining how `torch.randint` and in-batch draws contribute to the sampling process within the current batch configuration.
### Recorded reasoning summary
Refining Negative Sampling Scope
I'm realizing my previous assumption about the scope of negative sampling was incorrect. It's not just drawing from `n_items`; uniform negatives are sampled from the entire catalog of 30,000 items, and in-batch negatives are specifically drawn from the unique targets within the current batch. This significantly broadens the pool for negative examples.
### Recorded reasoning summary
Investigating Data Filtering
I'm now scrutinizing the `known_positive_packed` data structure and its role in filtering. My attention is drawn to how `build_known_positive_filter` constructs this packed representation, and the implications for identifying and excluding already-seen items from the negative sampling pool.
### read_file · 2026-09-29T01:31:09.741Z
{
"end_line": 370,
"file_path": "/app/solution/train_current_model.py",
"start_line": 240
}
### Tool output · 2026-09-29T01:31:12.315Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 240-370 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: 371.
--- FILE CONTENT (truncated) ---
(completion_sum - before_completion)
/ np.maximum(counts, 1)
/ 100.0
).astype(np.float16)
return result
def build_sequence_parts(
listens: base.ListenEvents,
explicit: base.ExplicitEvents,
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
items = listens.items[qualifying].astype(np.uint16, copy=False)
organic = listens.organic[qualifying].astype(np.float32, copy=False)
completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
if not len(items):
return None
event_features = np.zeros((len(items), 3), dtype=np.float16)
event_features[:, 0] = organic.astype(np.float16)
event_features[:, 1] = (
np.minimum(completion, 100.0) / 100.0
).astype(np.float16)
if len(items) > 1:
gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
event_features[1:, 2] = np.exp(
-gaps.astype(np.float64) / (60.0 * base.DAY)
).astype(np.float16)
activity = causal_activity_features(
timestamps, items, organic, completion
)
states = np.zeros(len(items), dtype=np.uint32)
active_likes: dict[int, int] = {}
active_dislikes: dict[int, int] = {}
explicit_position = 0
current_state = 0
for row, timestamp in enumerate(timestamps):
changed = False
while (
explicit_position < len(explicit.timestamps)
and int(explicit.timestamps[explicit_position]) < int(timestamp)
):
item = int(explicit.items[explicit_position])
event_timestamp = int(explicit.timestamps[explicit_position])
destination = (
active_likes
if int(explicit.channel[explicit_position]) == 0
else active_dislikes
)
if int(explicit.active[explicit_position]) == 1:
destination[item] = event_timestamp
else:
destination.pop(item, None)
explicit_position += 1
changed = True
if changed:
current_state = append_state_snapshot(
active_likes,
active_dislikes,
n_items,
liked_states,
disliked_states,
state_counts,
)
states[row] = np.uint32(current_state)
return timestamps, items.copy(), event_features, activity, states
def final_user_features(
timestamps: np.ndarray,
items: np.ndarray,
event_features: np.ndarray,
history_end: int,
like_count: int,
dislike_count: int,
) -> np.ndarray:
start = int(
np.searchsorted(
timestamps,
history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
)
result = np.zeros(6, dtype=np.float32)
recent_items = items[start:]
if len(recent_items):
result[0] = np.log1p(len(recent_items)) / 8.0
result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
result[2] = float(np.mean(event_features[start:, 0]))
result[3] = float(np.mean(event_features[start:, 1]))
result[4] = np.log1p(like_count) / 6.0
result[5] = np.log1p(dislike_count) / 4.0
return result
def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
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(
## Step 32 · agent · 2026-09-29T01:31:44.101Z
### Recorded reasoning summary
Analyzing Explicit Feedback
I'm currently digging into explicit dislikes and likes, specifically examining the `dislikes.parquet` and `undislikes.parquet` datasets. It's crucial to understand how these explicitly registered preferences align with or diverge from implicit listening data, especially considering that "listens" in `build_sequence_parts` are defined as positive interactions (>= 50% completion).
### Recorded reasoning summary
Investigating Negative Sampling
I'm scrutinizing how explicit dislikes are handled within the current negative sampling strategy. It appears `disliked_items` aren't directly incorporated as hard negatives in the training process, a potential area for improvement to refine our models.
### Recorded reasoning summary
Exploring Negative Sampling Strategy
I'm currently reviewing the negative sampling mechanisms within `train_current_model.py`, specifically `config.uniform_negatives` and `config.inbatch_negatives`. I'm also investigating the `draw_full_history_filtered_negatives` function to understand how in-batch negatives are presently selected and if this process can be enhanced to better incorporate explicit negative feedback while adhering to CPU constraints.
### Recorded reasoning summary
Refining Negative Sampling
I'm re-examining how in-batch negatives are currently sampled. It seems only a fixed number, 8, are randomly selected from unique targets for each target, which deviates from typical two-tower approaches where all items in a larger batch serve as negatives. I'm questioning this and the ratio of 8 in-batch to 24 uniform negatives.
### Recorded reasoning summary
Analyzing Loss Function
I'm scrutinizing the `corrected_negative_loss` and its calculation of `raw`, which is `softplus(query_vectors @ negative_vectors)`. I'm now focused on understanding the role of `correction` within this process and its source from `build_item_correction`.
### Recorded reasoning summary
Refining In-Batch Propensities
I'm analyzing the `estimate_in_batch_propensities` function. My current focus is understanding how `counts` are accumulated from `target_counts` and `prepared.sequence_items`. I'm also examining the calculation of `probabilities` and how `config.inbatch_correction_power` influences the `raw` score for in-batch negatives.
### Recorded reasoning summary
Adjusting Logits via Correction
I'm re-evaluating how the `correction` term, derived from `build_item_correction`, is applied within the loss calculation. The current implementation appears to multiply a `softplus(logits)` term by `weights`, which seems counter to the standard logQ correction where $\log(p_i)$ is subtracted from logits before applying softmax.
### Recorded reasoning summary
Revising Correction Logic
I'm re-evaluating the calculation of `raw` within the `estimate_in_batch_propensities` function. The current approach suggests smaller corrections for popular items due to the division by `probabilities`. I'm questioning the loss objective being used and if Sampled Softmax or InfoNCE is applicable given latency constraints and the prompt's emphasis on a two-tower architecture.
### Recorded reasoning summary
Investigating Negative Selection
I'm focusing on how negative examples are chosen for training. I'm examining the types of negatives used, their quantity, and whether explicit dislikes are properly incorporated. My goal is to improve the current sampling strategy within our CPU constraints.
### Recorded reasoning summary
Refining Negative Rejection
I'm scrutinizing how rejected in-batch negatives are handled. They're currently replaced with uniform random draws, and I'm questioning if this sufficiently addresses the underlying issue, particularly if the rejection mechanism itself is too broad.
### Recorded reasoning summary
Analyzing Filter Logic
I am examining the `invalid_full_history_negatives` function and questioning the origin and purpose of `known_positive_packed`. My next step is to investigate the `build_known_positive_filter` function to understand how this crucial filter is constructed.
### Recorded reasoning summary
Examining Filter Construction
I'm now dissecting `build_known_positive_filter` to fully grasp `known_positive_packed`. Understanding its creation is key to validating the `invalid_full_history_negatives` logic and its impact on negative sample selection.
### Recorded reasoning summary
Validating Filter Construction
I'm now analyzing the `build_known_positive_filter` function, specifically the `known_positive_packed` construct. Understanding its creation is vital to ensure the `invalid_full_history_negatives` logic accurately identifies and excludes relevant positives when selecting negative samples.
### Recorded reasoning summary
Analyzing Prefix Discarding
I'm noticing that the code appears to be discarding a significant prefix of user sequences, specifically items from index 0 up to the calculated `window_starts` for training. This prefix is then incorrectly used when building the `known_positive_filter`, leading to potential inaccuracies in negative sample selection.
### Recorded reasoning summary
Validating Prefix Usage
I'm re-examining how sequence prefixes are used for building the positive filter. It seems the chronological BCE suffix is intended, but the current implementation might be using a discarded prefix, causing potential inaccuracies in my positive sample identification.
### Recorded reasoning summary
Excluding Train Encounters
I'm confirming that all items encountered during training are rigorously excluded as candidates during validation and testing. This ensures the model isn't simply recommending what it's already seen, a critical aspect of candidate generation.
### grep_search · 2026-09-29T01:31:44.101Z
{
"include_pattern": "train_current_model.py",
"dir_path": "/app/solution",
"pattern": "build_known_positive_filter"
}
### Tool output · 2026-09-29T01:31:44.116Z
Found 2 matches for pattern "build_known_positive_filter" in path "/app/solution" (filter: "train_current_model.py"):
---
File: train_current_model.py
L563- )
L564- normalization = float(np.sum(counts * raw) / max(total, 1.0))
L565- correction = np.clip(
L566- raw / max(normalization, 1.0e-12),
L567- config.inbatch_correction_min,
L568- config.inbatch_correction_max,
L569- ).astype(np.float32)
L570- mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
L571- log(
L572- f"inbatch correction: min={correction.min():.3f} "
L573- f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
L574- )
L575- return torch.from_numpy(correction)
L576-
L577-
L578: def build_known_positive_filter(
L579- prepared: SequenceData, training_counts: np.ndarray
L580- ) -> np.ndarray:
L581- """Pack each user's complete public-training positive-item set."""
L582- width = (len(prepared.item_ids) + 7) // 8
L583- packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
L584- retained = 0
L585- for user_row, target_count in enumerate(training_counts):
L586- if not target_count:
L587- continue
L588- offset = int(prepared.offsets[user_row])
L589- # T adjacent targets consume sequence positions 0 through T. The
L590- # chronological BCE suffix starts at T + 1 and is intentionally absent.
L591- items = np.unique(
L592- prepared.sequence_items[
L593- offset : offset + int(target_count) + 1
L1551- generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
L1552- item_correction = build_item_correction(
L1553- prepared,
L1554- config,
L1555- partition.window_starts,
L1556- training_counts,
L1557- )
L1558- target_weights = build_target_weights(
L1559- public,
L1560- stage,
L1561- prepared,
L1562- partition.history_training_counts,
L1563- config,
L1564- )
L1565- known_positive_packed = (
L1566: build_known_positive_filter(prepared, partition.history_training_counts)
L1567- if config.negative_filter_scope == "full-training-history"
L1568- else None
L1569- )
L1570- history: list[dict[str, float]] = []
L1571- best_loss = float("inf")
L1572- best_epoch = 0
L1573- best_state: dict[str, torch.Tensor] | None = None
L1574- epochs_without_improvement = 0
L1575- start_epoch = 0
L1576-
L1577- if resume_checkpoint is not None:
L1578- payload = torch.load(
L1579- resume_checkpoint, map_location=device, weights_only=False
L1580- )
L1581- stored_config = payload.get("config", {})
---
## Step 33 · agent · 2026-09-29T01:32:13.120Z
### Recorded reasoning summary
Analyzing `history_training_counts`
I'm currently dissecting the `build_known_positive_filter` function, specifically focusing on how `partition.history_training_counts` is utilized. My attention is drawn to line 1566 where this parameter is passed. I'm investigating the exact composition of `history_training_counts`, with initial observations pointing to its derivation from `totals = lengths - 1` and `validation = floor(totals`.
### Recorded reasoning summary
Clarifying `history_training_counts`
I've further refined my understanding of `history_training_counts`. It appears to represent the initial 90% of a user's sequence, excluding the validation targets which are appended at the end. My next step is to examine how `negative_filter_scope` interacts with this, particularly in conjunction with the `draw_training_negatives` function.
### Recorded reasoning summary
Investigating `target_weighting`
I am now shifting my focus to `Config.target_weighting`, noting its assignment as "organic-strong" in `train_current_model.py`. I am cross-referencing this with its definition within the `build_target_weights` function, specifically examining the conditions that lead to the "organic-strong" weighting implementation.
### Recorded reasoning summary
Examining `Config.target_weighting`
My focus has shifted to `Config.target_weighting`, specifically the "organic-strong" setting found in `train_current_model.py`. I'm correlating this with its implementation within `build_target_weights`. I've also noted that `discovery-graded` is already an existing option.
### Recorded reasoning summary
Analyzing Negative Sampling
I'm now delving into how negative examples are selected and utilized within the training objective, aiming to identify and address limitations in the current strategy while respecting the CPU budget. My immediate focus is on the `corrected_negative_loss` and the interplay between `positive_loss` and `negative_loss` in `score_target_microbatches`. I am also investigating the composition of sampled negatives, noting the counts for uniform and in-batch negatives, and considering how explicit dislikes or impression data might inform this process.
### grep_search · 2026-09-29T01:32:13.120Z
{
"dir_path": "/app/solution",
"include_pattern": "train_current_model.py",
"pattern": "negative"
}
### Tool output · 2026-09-29T01:32:13.135Z
Found 88 matches for pattern "negative" in path "/app/solution" (filter: "train_current_model.py"):
---
File: train_current_model.py
L14: and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
L56: uniform_negatives: int = 24
L57: inbatch_negatives: int = 8
L61: negative_filter_scope: str = "full-training-history"
L165: uniform_negatives=config.uniform_negatives,
L166: inbatch_negatives=config.inbatch_negatives,
L477: "event_time_feature": "exp-negative-gap-over-60-days",
L603: f"full-history false-negative filter: users={len(prepared.user_ids):,} "
L609: def invalid_full_history_negatives(
L610: negatives: np.ndarray,
L616: """Return row-level invalid negatives using the complete training prefix."""
L617: byte_columns = negatives >> 3
L618: bit_columns = negatives & 7
L621: invalid |= negatives == targets[:, None]
L622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
L623: for column in range(1, negatives.shape[1]):
L625: negatives[:, column, None] == negatives[:, :column]
L630: def draw_full_history_filtered_negatives(
L641: """Draw mixed negatives and reject all known public-training positives."""
L647: (batch_size, config.uniform_negatives),
L653: if config.inbatch_negatives:
L656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)
L672: if corrected_count < config.inbatch_negatives:
L675: (batch_size, config.inbatch_negatives - corrected_count),
L681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()
L682: correction = np.ones_like(negatives, dtype=np.float32)
L685: :, config.uniform_negatives : config.uniform_negatives + corrected_count
L688: invalid = invalid_full_history_negatives(
L689: negatives,
L697: torch.from_numpy(negatives).to(device),
L706: negatives[invalid] = replacements
L709: invalid = invalid_full_history_negatives(
L710: negatives,
L717: raise RuntimeError("full-history negative resampling failed")
L719: torch.from_numpy(negatives).to(device),
L724: def draw_training_negatives(
L736: if config.negative_filter_scope == "full-training-history":
L738: raise RuntimeError("full-history negative filter was not built")
L739: return draw_full_history_filtered_negatives(
L750: return base.draw_filtered_mixed_negatives(
L755: config.uniform_negatives,
L756: config.inbatch_negatives,
L816: negatives: torch.Tensor,
L818: all_items = torch.cat([positives, negatives.reshape(-1)])
L822: negative_vectors = encoded[inverse[len(positives) :]].reshape(
L823: len(positives), negatives.shape[1], -1
L825: return positive_vectors, negative_vectors
L845: def corrected_negative_loss(
L851: total = config.uniform_negatives + config.inbatch_negatives
L853: if config.uniform_negatives:
L854: result += config.uniform_negatives * raw[
L855: :, : config.uniform_negatives
L857: if config.inbatch_negatives:
L858: selected = slice(config.uniform_negatives, total)
L860: result += config.inbatch_negatives * (
L880: raise RuntimeError("chronological target partition produced negative counts")
L1119: negatives: torch.Tensor,
L1120: negative_weights: torch.Tensor,
L1134: "negative_sum": 0.0,
L1162: positive_vectors, negative_vectors = candidate_vectors(
L1165: negatives[begin:end],
L1168: negative_logits = torch.einsum(
L1169: "bd,bkd->bk", query_vectors, negative_vectors
L1172: negative_loss = corrected_negative_loss(
L1173: negative_logits, negative_weights[begin:end], config
L1179: selected_weights * (positive_loss + negative_loss) / 2.0
L1190: statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
L1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
L1227: negative_count = config.uniform_negatives + config.inbatch_negatives
L1232: total_negative = 0.0
L1293: negatives, negative_weights = draw_training_negatives(
L1314: negatives,
L1315: negative_weights,
L1331: total_negative += statistics["negative_sum"]
L1353: "negative_logit": total_negative / max(total_targets * negative_count, 1),
L1355: / max(total_targets * negative_count, 1),
L1449: negatives, negative_weights = draw_training_negatives(
L1469: negatives,
L1470: negative_weights,
L1567: if config.negative_filter_scope == "full-training-history"
L1601: "negative_filter_scope", "last-100"
L1629: stored_filter_scope != config.negative_filter_scope
L1660: f"{stored_filter_scope}->{config.negative_filter_scope}, "
L1944: "--negative-filter-scope",
L1946: default=Config.negative_filter_scope,
L2034: negative_filter_scope=args.negative_filter_scope,
L2136: "negative_sampling": (
L2139: "false_negative_filter": config.negative_filter_scope,
---
## Step 34 · agent · 2026-09-29T01:32:17.323Z
### read_file · 2026-09-29T01:32:17.323Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 40,
"start_line": 1
}
### Tool output · 2026-09-29T01:32:17.329Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-40 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: 41.
--- 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)
## Step 35 · agent · 2026-09-29T01:33:19.027Z
### Recorded reasoning summary
Analyzing Model Architecture
I'm currently dissecting the model's architecture. It appears to be a straightforward two-tower retriever, notably omitting user IDs, output normalization, and employing raw dot-product retrieval. My focus is on understanding the implications of the sample-weighted binary cross-entropy and the specific negative sampling strategy.
### Recorded reasoning summary
Refining Negative Sampling
I'm now focusing on the limitations within the current negative-sampling approach. The code for `draw_full_history_filtered_negatives` is under scrutiny, with particular attention paid to how it handles known positives and the potential for improvement under the fixed CPU budget.
### Recorded reasoning summary
Investigating Negative Sampling Logic
I'm diving into the `draw_full_history_filtered_negatives` function, specifically examining how it generates uniform and in-batch negatives. I'm analyzing the use of `torch.randint` and the logic for selecting in-batch negatives, especially how it handles existing targets and prioritizes them.
### Recorded reasoning summary
Evaluating Negative Sampling Strategy
I'm currently examining the implementation of negative sampling, particularly the logic within `draw_full_history_filtered_negatives`. My focus is on the generation of both uniform and in-batch negatives, paying close attention to the use of `torch.randint` and the selection process for in-batch negatives, including how existing targets are handled.
### Recorded reasoning summary
Revising Uniform Negative Generation
I'm scrutinizing the generation of uniform negatives here, observing that replacements are drawn from `torch.randint` on the CPU. My current focus is on how these replacements are applied to the `negatives` array and how the associated `correction` factor is updated for these re-sampled items.
### Recorded reasoning summary
Sampling In-Batch Negatives
I'm observing that in-batch negatives are sampled by selecting 8 random items from the unique targets within the batch. This seems to be done for each target, which raises questions about the logic and potential biases in this approach.
### Recorded reasoning summary
Questioning In-Batch Sampling Logic
I'm questioning the uniform random sampling of in-batch negatives from `unique_targets`. This contradicts standard practice where negative selection should reflect data distribution popularity, not equal probability.
### Recorded reasoning summary
Challenging Negative Sampling
I'm questioning the sampling of in-batch negatives from `unique_targets`, as it appears to be uniformly random. This seems to contradict the subsequent application of propensity correction, which penalizes popular items as if they were sampled proportionally, creating a logical inconsistency in the overall strategy.
### Recorded reasoning summary
Reconsidering In-Batch Negatives
I'm now considering if in-batch items are sampled with replacement from targets, which would naturally align with popularity. I'm also questioning why only 8 in-batch negatives are used, and if they should simply be the other items in the batch, contrasting this with standard two-tower approaches.
### Recorded reasoning summary
Encoding All Items
I've realized that the process of encoding items is extremely inefficient. It appears I'm encoding all 30,000 catalog items in every single step because of the way negatives are sampled and unique items are identified, which is a significant bottleneck.
### Recorded reasoning summary
Revising Item Encoding
I've identified a critical inefficiency: all 30,000 catalog items are being encoded in *every* training step. This occurs because of how negative sampling and unique items are identified, leading to a massive computational bottleneck where the item tower MLP runs repeatedly for nearly all items, even though only a subset is used for dot products.
### Recorded reasoning summary
Optimizing Item Encoding
I'm addressing the substantial inefficiency of encoding all 30,000 catalog items in every training step. This repeated encoding, driven by negative sampling and unique item identification, significantly bottlenecks the item tower MLP. I will optimize this process to run only when necessary.
### Recorded reasoning summary
Refining Loss Calculation
I'm refining the negative loss calculation. Specifically, I'm analyzing how the `_negative_loss` function combines contributions from uniform and in-batch negatives, paying close attention to the `correction` weights and their impact on the final loss value, particularly within the `score_target_microbatches` function.
### Recorded reasoning summary
Adjusting Loss Weighting
I'm questioning the division of the combined positive and negative losses by two, as this implies an equal weighting between positives and negatives. Given that each negative sample only receives 1/32 of the gradient compared to a positive, this weighting seems incorrect and needs adjustment.
### Recorded reasoning summary
Improving Pair Accuracy
My focus has shifted to the unexpectedly low pair accuracy of 75.4%. This indicates that for a given positive, it only outperforms random negatives 75% of the time. Given the catalog size, this will severely hinder retrieval performance, meaning true positives will rarely rank highly. I'm investigating why this occurs, questioning the use of point-wise BCE with negative sampling and considering alternatives like Sampled Softmax, InfoNCE, or BPR.
### Recorded reasoning summary
Evaluating Loss Function Choice
I'm now considering whether the binary cross-entropy loss is indeed the correct choice, especially given the prompt's focus on negative example selection and the potential need for alternatives like InfoNCE. My current thought is to re-examine the prompt and the code for definitive guidance on the objective function.
### Recorded reasoning summary
Revisiting Objective Function
I'm re-examining the prompt's core directive to focus on how the training objective selects and uses negative examples. This brings me back to evaluating the suitability of binary cross-entropy versus alternatives like InfoNCE, especially concerning the specific negative sampling strategies being employed.
### Recorded reasoning summary
Revising Loss Components
I'm now re-evaluating how uniform and in-batch negatives contribute to the loss, paying close attention to the impact of the `correction` weights on the overall `_negative_loss` calculation, particularly as handled within `score_target_microbatches`.
**Adjusting Loss Weighting Scheme**
I am questioning the division of combined positive and negative losses by two, as this implies an equal weighting. Given that each negative sample receives only 1/32 of the gradient compared to a positive, this weighting appears incorrect and requires adjustment.
**Diagnosing Low Pair Accuracy**
The pair accuracy is unexpectedly low at 75.4%. This implies that for a given positive, it only outperforms random negatives 75% of the time, which will severely hinder retrieval. I am investigating why this occurs, considering alternatives to point-wise BCE with negative sampling.
**Evaluating Objective Function Suitability**
I am reconsidering the choice of binary cross-entropy loss, especially in light of the prompt's emphasis on negative example selection and the potential benefits of alternatives like InfoNCE for this specific task.
**Re-examining Training Objective**
My focus is back on the prompt's core requirement: how the training objective selects and utilizes negative examples. This leads me to re-evaluate binary cross-entropy against options like InfoNCE, considering the current negative sampling methods.
### Recorded reasoning summary
Analyzing Unique Target Identification
I'm examining the `unique_targets` variable, noticing it's derived from `targets`, which itself originates from `positive_items`. The length of `positive_items` appears to be `batch_size * timesteps`, creating a potentially large pool for `unique_targets` from which priorities are then drawn.
### Recorded reasoning summary
Optimizing In-Batch Negative Selection
I'm struck by the immense memory and computational cost of generating in-batch negatives. The current `priorities` matrix, potentially millions of floats, is processed with `torch.topk` at every step. This inefficient process needs immediate re-evaluation to reduce the resource burden.
### Recorded reasoning summary
Correcting In-Batch Negative Handling
I've identified an issue where invalid in-batch negatives are replaced by uniform draws with a correction factor of 1.0. This seems to override the intended propensity correction logic, potentially skewing the loss calculation and needs adjustment to ensure accurate weighting.
### Recorded reasoning summary
Revising Weighted Loss Calculation
I'm analyzing the loss computation, specifically the weighted average for uniform negatives and the independent calculation for in-batch ones. It appears the final negative loss combines these with a 75/25 split, which warrants further scrutiny in light of gradient contributions.
### Recorded reasoning summary
Adjusting Loss Gradient Contributions
I'm observing that the loss is calculated as the average of positive and negative losses. This implies gradients for the positive item are half the magnitude of standard BCE, and gradients for each negative item are half the magnitude of standard BCE, divided by the number of negatives. This requires adjustment to match the intended weighting and gradient contributions.
### Recorded reasoning summary
Correcting Loss Gradient Magnitudes
I'm realizing the gradient contribution to individual negatives is minuscule due to averaging, diminishing their impact. This contrasts with standard methods like SGNS where the sum, not average, dictates the loss, ensuring each negative contributes meaningfully.
### Recorded reasoning summary
Fixing Loss Averaging
I'm correcting an erroneous averaging operation in `corrected_negative_loss`. The intention was to sum negative losses for strong gradient signals, but it was instead averaging, significantly weakening each negative's impact. This fix aligns with typical contrastive learning objectives.
### Recorded reasoning summary
Revising Loss Normalization
I'm identifying that the calculation currently divides the sum of losses by the total, effectively returning a mean. This, combined with subsequent division by two, incorrectly implies equal weighting between positives and negatives, which needs correction.
### Recorded reasoning summary
Refining Training Objective Logic
I'm re-examining the training objective, specifically the interplay between the binary cross-entropy loss and the chosen negative sampling strategy. My current focus is on whether InfoNCE or sampled softmax would be more appropriate given the prompt's emphasis on negative example utilization and the observed low pair accuracy.
### Recorded reasoning summary
Revising Training Objective Logic
I'm re-examining the training objective, specifically the interplay between the binary cross-entropy loss and the chosen negative sampling strategy. My current focus is on whether InfoNCE or sampled softmax would be more appropriate given the prompt's emphasis on negative example utilization and the observed low pair accuracy.
### Recorded reasoning summary
Clarifying Objective Function
I am re-evaluating the core directive to focus on how the training objective selects and uses negative examples. This brings me back to considering InfoNCE or sampled softmax over binary cross-entropy, especially given the observed low pair accuracy and the prompt's emphasis on effective negative utilization.
### Recorded reasoning summary
Challenging Uniform In-Batch Sampling
I'm reviewing the sampling of in-batch negatives from `unique_targets`. The current uniform random selection appears problematic, especially when contrasted with propensity correction later applied. This approach seems inconsistent with how popularity should influence sampling.
### Recorded reasoning summary
Analyzing Negative Sampling Strategies
I'm currently questioning the uniform random sampling of in-batch negatives from `unique_targets`. This approach seems inconsistent, especially when paired with subsequent propensity correction, and I am investigating how item popularity should ideally influence negative selection in this two-tower setup.
### grep_search · 2026-09-29T01:33:19.027Z
{
"include_pattern": "train_current_model.py",
"dir_path": "/app/solution",
"pattern": "dislike"
}
### Tool output · 2026-09-29T01:33:19.042Z
Found 33 matches for pattern "dislike" in path "/app/solution" (filter: "train_current_model.py"):
---
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),
---
## Step 36 · agent · 2026-09-29T01:33:37.724Z
### Recorded reasoning summary
Analyzing Dislike Usage
My current focus is on understanding how dislikes are integrated. It appears they serve solely as an input feature for the user tower, never as explicit negative training examples. I'm exploring the implications and potential nuances of this approach.
### Recorded reasoning summary
Clarifying Candidate Exclusion
I've realized that my previous assumption about dislikes was slightly off. It turns out that any track a user has *encountered*, including through a dislike, is explicitly excluded from their candidate set during evaluation. This means the model will never be tested on content the user has already interacted with.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now digging into the specifics of how negative examples are sampled during training. It seems we're using two methods: uniform sampling from a large pool and in-batch negatives. I'm starting to question if this approach is truly optimal given the constraints and the prompt's emphasis on limitations.
### Recorded reasoning summary
Analyzing Training Time
I'm investigating why training takes so long, specifically around 211 seconds per epoch. It seems the time is consumed by GRU forward/backward passes, negative sampling with resampling iterations, and item encoding. I need to scrutinize `draw_full_history_filtered_negatives` closely.
### Recorded reasoning summary
Refining Negative Sampling
I'm examining the `invalid_full_history_negatives` function. It appears that rejected in-batch items are re-sampled uniformly. I'm also looking at how negative samples are generated, specifically the role of `draw_full_history_filtered_negatives`, to ensure optimal training given our limitations.
### run_shell_command · 2026-09-29T01:33:37.724Z
{
"command": "python -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog columns:', catalog.columns.tolist())\nprint(catalog.head())\nprint('Shape:', catalog.shape)\nprint('Pos listens summary:\\n', catalog['positive_listens'].describe())\nprint('Organic pos listens summary:\\n', catalog['organic_positive_listens'].describe())\n\"",
"description": "Inspect candidate catalog"
}
### Tool output · 2026-09-29T01:33:39.274Z
Output: Catalog columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
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
Shape: (30000, 4)
Pos listens summary:
count 30000.000000
mean 668.544300
std 1053.389553
min 163.000000
25% 228.000000
50% 354.000000
75% 669.250000
max 26615.000000
Name: positive_listens, dtype: float64
Organic pos listens summary:
count 30000.000000
mean 313.926433
std 636.039902
min 0.000000
25% 93.000000
50% 162.000000
75% 309.000000
max 20620.000000
Name: organic_positive_listens, dtype: float64
Process Group PGID: 1742
## Step 37 · agent · 2026-09-29T01:33:44.376Z
### Recorded reasoning summary
Analyzing Data Distribution
The extreme skew in listen counts, with a median of 354 and a maximum of 26,615, is striking. I'm now shifting to examine the baseline model's output to understand its performance against this reality.
### run_shell_command · 2026-09-29T01:33:44.376Z
{
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\nranking = pd.read_parquet('/app/solution/production_validation.parquet')\nprint('Ranking shape:', ranking.shape)\nprint('Number of unique items recommended:', ranking.item_id.nunique())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\nranking['pop_rank'] = ranking.item_id.map(item_pop)\nprint('Recommended items popularity rank summary:\\n', ranking['pop_rank'].describe())\ntop_rec = ranking.item_id.value_counts().head(20)\nprint('Most frequently recommended items:')\nfor item, count in top_rec.items():\n print(f'Item {item}: count={count} ({count/ranking.uid.nunique()*100:.1f}%), pop_rank={item_pop.get(item)}')\n\"",
"description": "Analyze baseline ranking predictions"
}
### Tool output · 2026-09-29T01:33:46.183Z
Output: Ranking shape: (845600, 3)
Number of unique items recommended: 2575
Recommended items popularity rank summary:
count 845600.000000
mean 2991.802935
std 5853.839534
min 1.000000
25% 102.000000
50% 413.000000
75% 2627.000000
max 29992.000000
Name: pop_rank, dtype: float64
Most frequently recommended items:
Item 6896222: count=5089 (60.2%), pop_rank=818
Item 7565403: count=4970 (58.8%), pop_rank=2062
Item 2055777: count=4647 (55.0%), pop_rank=1170
Item 8722394: count=4627 (54.7%), pop_rank=634
Item 5010168: count=4572 (54.1%), pop_rank=2947
Item 5134208: count=4397 (52.0%), pop_rank=306
Item 3968387: count=4160 (49.2%), pop_rank=318
Item 8823268: count=4157 (49.2%), pop_rank=76
Item 6414220: count=4155 (49.1%), pop_rank=3207
Item 8350060: count=4132 (48.9%), pop_rank=1529
Item 7227266: count=4091 (48.4%), pop_rank=919
Item 8729910: count=4036 (47.7%), pop_rank=564
Item 3375935: count=4003 (47.3%), pop_rank=282
Item 6625048: count=3992 (47.2%), pop_rank=128
Item 6006390: count=3988 (47.2%), pop_rank=75
Item 6813373: count=3960 (46.8%), pop_rank=126
Item 2408923: count=3865 (45.7%), pop_rank=111
Item 2705460: count=3815 (45.1%), pop_rank=3016
Item 1034521: count=3747 (44.3%), pop_rank=189
Item 781438: count=3710 (43.9%), pop_rank=86
Process Group PGID: 1759
## Step 38 · agent · 2026-09-29T01:33:50.373Z
### Recorded reasoning summary
Discovering Recommendation Patterns
I'm analyzing the recommendation data. It appears a very small fraction of our catalog, just over 8%, is ever recommended. Notably, a significant portion of users are receiving recommendations for the same few, moderately popular items, with some items reaching over 60% of the user base.
### run_shell_command · 2026-09-29T01:33:50.373Z
{
"description": "Analyze validation ground truth items and popularity",
"command": "python -c \"\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\nall_relevant_items = []\ngrades = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_relevant_items.append(item)\n grades.append(grade)\n\nrel_df = pd.DataFrame({'item_id': all_relevant_items, 'grade': grades})\nrel_df['pop_rank'] = rel_df.item_id.map(item_pop)\nprint(f'Scored users: {len(graded)}')\nprint(f'Total relevant user-item pairs: {len(rel_df)}')\nprint(f'Unique relevant items: {rel_df.item_id.nunique()}')\nprint('Grade distribution:\\n', rel_df.grade.value_counts())\nprint('Popularity rank summary of relevant items:\\n', rel_df.pop_rank.describe())\n\""
}
### Tool output · 2026-09-29T01:33:51.863Z
Output: Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1777
## Step 39 · agent · 2026-09-29T01:33:55.575Z
### run_shell_command · 2026-09-29T01:33:55.575Z
{
"command": "python -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\nall_relevant_items = []\ngrades = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_relevant_items.append(item)\n grades.append(grade)\n\nrel_df = pd.DataFrame({'item_id': all_relevant_items, 'grade': grades})\nrel_df['pop_rank'] = rel_df.item_id.map(item_pop)\nprint(f'Scored users: {len(graded)}')\nprint(f'Total relevant user-item pairs: {len(rel_df)}')\nprint(f'Unique relevant items: {rel_df.item_id.nunique()}')\nprint('Grade distribution:\\n', rel_df.grade.value_counts())\nprint('Popularity rank summary of relevant items:\\n', rel_df.pop_rank.describe())\n\"",
"description": "Analyze validation ground truth items and popularity"
}
### Tool output · 2026-09-29T01:33:57.677Z
Output: Scored users: 4263
Total relevant user-item pairs: 37769
Unique relevant items: 14124
Grade distribution:
grade
1 33352
3 4417
Name: count, dtype: int64
Popularity rank summary of relevant items:
count 37769.000000
mean 8463.087744
std 8117.953262
min 1.000000
25% 1733.000000
50% 5622.000000
75% 13374.000000
max 30000.000000
Name: pop_rank, dtype: float64
Process Group PGID: 1788
## Step 40 · agent · 2026-09-29T01:34:15.220Z
### Recorded reasoning summary
Analyzing Ground Truth Data
I've been dissecting the validation ground truth, noting 4,263 scored users and 37,769 relevant user-item pairs. The sheer volume of 14,124 unique relevant items is striking, with a median popularity rank of 5,6 indicating their relative commonality.
### Recorded reasoning summary
Investigating Popularity Bias
I'm digging into the baseline's predictions. It's heavily skewed towards the most popular items, predicting only a fraction of unique items compared to what users actually engage with. I'm now analyzing *why* this two-tower model exhibits such extreme popularity bias during training and negative sampling.
### Recorded reasoning summary
Understanding Negative Sampling
My recent focus has been on in-batch negative sampling. I've realized that with uniform sampling, popular items are rarely chosen as negatives, leading to skewed positive embeddings and a strong bias towards high-popularity items in predictions.
### Recorded reasoning summary
Revising Negative Sampling Strategy
I've realized my previous assumptions about in-batch negative sampling were inverted. Popular items are sampled as negatives far more frequently than I initially thought, which actually helps to counteract popularity bias. This insight is crucial for refining my approach.
### Recorded reasoning summary
Adjusting In-Batch Sampling
I've discovered my previous understanding of `unique_targets` was flawed; it actually evens out popular and rare items, effectively destroying the true negative distribution. Furthermore, the `item_correction` mechanism, when applied to popular items with small weights, significantly reduces their influence on the weighted average loss.
### Recorded reasoning summary
Refining Loss Calculations
I've discovered that popular items were paradoxically penalized less in the negative loss. The subsequent averaging mechanism compounded this. My current focus is on understanding how different loss functions, like pointwise BCE versus InfoNCE, directly impact ranking optimization.
### Recorded reasoning summary
Correcting Item Bias
I'm now exploring how to apply logQ correction to InfoNCE. The aim is to adjust the logits by subtracting the log probability of each item, effectively debiasing the training signal and ensuring raw dot products are used for final ranking, as per Yi et al. 2019.
### run_shell_command · 2026-09-29T01:34:15.220Z
{
"description": "Test training speed with prepared cache",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --output /app/solution/test_cache.parquet"
}
### Tool output · 2026-09-29T01:39:26.084Z
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=196.5s
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=209.5 validation_seconds=14.8
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/test_cache.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 306.476,
"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": 0.5398380473223814,
"positive_logit": 0.7427799578701499,
"negative_logit": -0.6222247038719716,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 209.497521838,
"validation_loss": 0.4966362164658338,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 14.751028146999943
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497010231018,
"item_norm_mean": 1.7570279836654663,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1808
## Step 41 · agent · 2026-09-29T01:39:29.530Z
### run_shell_command · 2026-09-29T01:39:29.530Z
{
"description": "Remove temporary test file",
"command": "rm -f /app/solution/test_cache.parquet"
}
### Tool output · 2026-09-29T01:39:29.546Z
Output: (empty)
Process Group PGID: 1833
## Step 42 · agent · 2026-09-29T01:39:33.225Z
### Recorded reasoning summary
Saving Baseline Submission
Before proceeding with testing any enhancements, I'm ensuring a valid baseline submission is securely stored. This is crucial for preserving our best result, especially as the deadline approaches, and I'm checking the `--stage submission` command's output.
### run_shell_command · 2026-09-29T01:39:33.225Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_sub.npz \\\n --output /app/solution/dummy.parquet \\\n --prepare-only",
"description": "Build submission data cache"
}
### Tool output · 2026-09-29T01:40:30.581Z
Output: loaded explicit events: rows=746,504 users=8,307
prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.2s
prepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.5s
prepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.9s
prepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.6s
prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.7s
prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.7s
prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.5s
prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=43.1s
prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=48.1s
target users without qualifying catalog sequence=6
prepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322
saved public-derived full-sequence cache: /app/solution/prepared_sub.npz
{
"stage": "submission",
"sequence_users": 9153,
"sequence_tokens": 20712971,
"next_item_targets": 20703818,
"selected_training_targets": 1067606,
"selected_validation_targets": 131997,
"discarded_old_targets": 19504215,
"target_users": 8456,
"runtime_seconds": 53.311,
"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: 1836
## Step 43 · agent · 2026-09-29T01:40:36.115Z
### Recorded reasoning summary
Finalizing Baseline Submission
My focus has shifted to generating a valid baseline submission using the now-prepared cache files. I'm setting up `/app/solution/submission` to ensure a complete, albeit basic, output.
### run_shell_command · 2026-09-29T01:40:36.115Z
{
"command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\n\nrows = []\nfor idx, uid in enumerate(eligibility.uids):\n mask = np.unpackbits(eligibility.packed[idx], count=len(eligibility.item_ids), bitorder='little').astype(bool)\n eligible_items = eligibility.item_ids[mask]\n # pick top 100 eligible items\n top100 = eligible_items[:100]\n for r, item in enumerate(top100, 1):\n rows.append((int(uid), int(item), r))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Generated fallback submission with shape:', df.shape)\n\n# Validate it\nvalidated = validate_ranking(df, targets)\nprint('Validated successfully! Rows:', len(validated))\n\"",
"description": "Create initial valid fallback submission.parquet and validate constraints"
}
### Tool output · 2026-09-29T01:40:37.614Z
Output: Traceback (most recent call last):
File "", line 4, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1860
## Step 44 · agent · 2026-09-29T01:40:41.767Z
### run_shell_command · 2026-09-29T01:40:41.767Z
{
"command": "PYTHONPATH=/task python -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\n\nrows = []\nfor idx, uid in enumerate(eligibility.uids):\n mask = np.unpackbits(eligibility.packed[idx], count=len(eligibility.item_ids), bitorder='little').astype(bool)\n eligible_items = eligibility.item_ids[mask]\n top100 = eligible_items[:100]\n for r, item in enumerate(top100, 1):\n rows.append((int(uid), int(item), r))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Generated fallback submission with shape:', df.shape)\n\nvalidated = validate_ranking(df, targets)\nprint('Validated successfully! Rows:', len(validated))\n\"",
"description": "Create initial valid fallback submission.parquet and validate constraints"
}
### Tool output · 2026-09-29T01:40:44.884Z
Output: Generated fallback submission with shape: (845600, 3)
Validated successfully! Rows: 845600
Process Group PGID: 1871
## Step 45 · agent · 2026-09-29T01:40:50.200Z
### Recorded reasoning summary
Establishing Baseline Submission
My current focus is on ensuring the integrity of the baseline submission file located at `/app/solution/submission.parquet`. I've confirmed it adheres to all invariant requirements, including the precise row count of 845,600, the presence of `uid`, `item_id`, and `rank` columns, and the inclusion of only eligible candidate items.
### update_topic · 2026-09-29T01:40:50.200Z
{
"strategic_intent": "Formulate diagnoses of the current negative-sampling and loss implementation, then run empirical backtests on public validation.",
"title": "Diagnosing and Improving Negative Sampling and Training Objective",
"summary": "We have successfully reproduced the baseline validation score (0.02128), cached both validation and submission preprocessed sequences to accelerate training by 50 seconds per run, and verified an initial fallback submission. We analyzed the baseline recommendations, discovering severe popularity bias (only 2,575 unique items recommended, median popularity rank 413) whereas true relevant discovery items have median popularity rank 5,622 across 14,124 unique items. We are now transitioning to systematically diagnosing and improving the negative-sampling strategy and loss objective."
}
### Tool output · 2026-09-29T01:40:50.204Z
Current topic: "Diagnosing and Improving Negative Sampling and Training Objective"
Topic summary: We have successfully reproduced the baseline validation score (0.02128), cached both validation and submission preprocessed sequences to accelerate training by 50 seconds per run, and verified an initial fallback submission. We analyzed the baseline recommendations, discovering severe popularity bias (only 2,575 unique items recommended, median popularity rank 413) whereas true relevant discovery items have median popularity rank 5,622 across 14,124 unique items. We are now transitioning to systematically diagnosing and improving the negative-sampling strategy and loss objective.
Strategic Intent: Formulate diagnoses of the current negative-sampling and loss implementation, then run empirical backtests on public validation.
## Step 46 · agent · 2026-09-29T01:41:12.423Z
### Recorded reasoning summary
Analyzing Baseline Model Details
I'm currently digging into the specifics of the baseline model, including its negative sampling strategy and how the loss function is configured. My focus is on understanding how these elements contribute to the overall performance and efficiency within the given latency constraints.
### Recorded reasoning summary
Refining Negative Sampling Tactics
My analysis is zeroing in on the trainer's negative sampling mechanism, specifically its mix of uniform and in-batch negatives and the correctional power applied. I'm probing how this impacts the objective's selection and usage of these examples to identify limitations and opportunities for improvement within the CPU budget.
### Recorded reasoning summary
Exploring Target Weighting
I'm investigating the impact of switching the target weighting strategy to "discovery-graded." This seems pertinent given the task's grading system, and I want to see how it influences the objective's selection and utilization of negatives compared to the current "organic-strong" setting.
### Recorded reasoning summary
Investigating Loss Calculation
I'm examining the `corrected_negative_loss` function and its interaction with `score_target_microbatches`. Specifically, I'm analyzing how the distribution of uniform and in-batch negatives, along with the `correction` tensor's weights, influences the calculation of the raw and final loss values.
### Recorded reasoning summary
Tracing Loss Calculation Path
I'm currently tracing the mathematical flow within the loss calculation. My focus is on how `negative_logits` are computed via einsum, and the subsequent softplus application to both positive and negative logits. I'm also examining the averaging and weighting steps before summing to the final loss.
### Recorded reasoning summary
Evaluating Pair Accuracy Needs
My current focus is on the implications of the observed 75% pair accuracy. It seems that achieving a top-100 ranking for the true positive at inference time necessitates a pair accuracy of 99.7%. I'm analyzing why the current loss function falls short of this, particularly how it doesn't directly enforce a sufficient margin between positive and negative scores and how the division by K in negative loss diminishes individual negative gradients.
### Recorded reasoning summary
Comparing Loss Functions
I'm comparing how my current loss function dilutes the impact of a strong negative compared to InfoNCE, which directly couples positive and negative logits. InfoNCE's softmax gradient automatically hard-negative weights and its temperature parameter sharpens the distribution, influencing gradients differently.
### Recorded reasoning summary
Considering Alternative Loss Functions
I'm evaluating other loss functions like BPR and Margin Ranking Loss, which directly optimize score differences. I'm also contemplating whether InfoNCE or a logQ-corrected InfoNCE might be more effective. I've noticed specific validation evaluation functions and early stopping criteria in the training script.
### Recorded reasoning summary
Optimizing Epoch Speed
I'm investigating the 209-second epoch duration. It seems the bottleneck lies within `run_training_epoch`, specifically the `draw_training_negatives` function. I'm analyzing its complex steps, including generating uniform negatives, finding unique targets, and the iterative rejection sampling process for invalid histories, to identify potential performance improvements.
### run_shell_command · 2026-09-29T01:41:12.423Z
{
"description": "Benchmark unique_items in candidate_vectors",
"command": "python -c \"\nimport time\nimport torch\nimport numpy as np\n\n# Simulate candidate_vectors with unique_items\nB = 5000\nK = 32\npositives = torch.randint(0, 30000, (B,))\nnegatives = torch.randint(0, 30000, (B, K))\n\nt0 = time.time()\nall_items = torch.cat([positives, negatives.reshape(-1)])\nunique_items, inverse = torch.unique(all_items, return_inverse=True)\nt1 = time.time()\nprint(f'Unique items: {len(unique_items)} out of {len(all_items)} in {(t1-t0)*1000:.2f}ms')\n\""
}
### Tool output · 2026-09-29T01:41:16.655Z
Output: Unique items: 29873 out of 165000 in 4.72ms
Process Group PGID: 1887
## Step 47 · agent · 2026-09-29T01:41:19.875Z
### Recorded reasoning summary
Observing Item Distribution
I'm noticing a consistent pattern where the `unique_items` count hovers around 29,873 per batch. This high figure, given the catalog size and the inclusion of uniform negatives, suggests we're effectively sampling nearly the entire item pool.
### run_shell_command · 2026-09-29T01:41:19.875Z
{
"description": "Benchmark encode_items for 30000 items",
"command": "python -c \"\nimport time\nimport torch\nimport sys\nsys.path.append('/app/solution')\nimport two_tower_model as base\n\ncatalog_feat = torch.randn(30000, 4)\nconfig = base.TowerConfig()\nmodel = base.SequentialTwoTower(catalog_feat.numpy(), config)\n\nitems = torch.arange(30000)\nfor _ in range(5):\n t0 = time.time()\n out = model.encode_items(items)\n t1 = time.time()\n print(f'encode_items 30000 items: {(t1-t0)*1000:.2f}ms')\n\""
}
### Tool output · 2026-09-29T01:41:25.521Z
Output: encode_items 30000 items: 320.97ms
encode_items 30000 items: 322.79ms
encode_items 30000 items: 316.61ms
encode_items 30000 items: 308.11ms
encode_items 30000 items: 296.00ms
Process Group PGID: 1900
## Step 48 · agent · 2026-09-29T01:41:29.933Z
### Recorded reasoning summary
Measuring Encoding Efficiency
My recent analysis confirms encoding 30,000 items requires approximately 310ms, with the backward pass adding another 400ms. This translates to roughly 186 seconds per epoch, considering 266 batches.
### run_shell_command · 2026-09-29T01:41:29.933Z
{
"command": "python -c \"\nimport numpy as np\n\n# In a typical batch of 5000 targets from listening data:\n# How many unique items?\n# Let's test with real data!\ndata = np.load('/app/solution/prepared_val.npz')\nseq = data['sequence_items'][:5000]\nprint('Unique targets in 5000 targets:', len(np.unique(seq)))\n\"",
"description": "Check unique targets in typical batch"
}
### Tool output · 2026-09-29T01:41:30.346Z
Output: Unique targets in 5000 targets: 1896
Process Group PGID: 1914
## Step 49 · agent · 2026-09-29T01:41:47.697Z
### Recorded reasoning summary
Optimizing Item Encoding
I've realized the significant performance bottleneck: encoding nearly 2,000 unique items across 5,000 targets. By optimizing this, encoding time has dropped from 320ms to approximately 20ms, a fifteen-fold improvement. I'm now re-evaluating the prompt's emphasis on request-time latency and item vector constraints.
### Recorded reasoning summary
Investigating Negative Sampling
My focus has shifted to the training objective and how it selects negative examples. I'm digging into the limitations of our current negative sampling strategy, aiming to improve it within the existing CPU constraints and validate these changes via the public backtest.
### Recorded reasoning summary
Refining Negative Sampling
I'm analyzing our current negative sampling, which draws uniformly from the entire catalog, leading to popular items being rarely chosen as negatives. I'm exploring strategies to incorporate hard negatives, like popular or similar items the user hasn't interacted with, to improve the training objective.
### Recorded reasoning summary
Evaluating Negative Sampling Strategy
I'm examining the trade-offs between in-batch and uniform negative sampling. In-batch negatives appear crucial for capturing interaction popularity and providing harder examples. The current loss function dilutes negative penalties and lacks competitive ranking between positive and negative items.
### Recorded reasoning summary
Adjusting Negative Sample Weights
I've identified an issue where popular items, when sampled as negatives, are actually downweighted in the loss function. This counterintuitively prevents the model from learning to penalize them effectively. I'm exploring how to invert this behavior.
### Recorded reasoning summary
Correcting Popularity Bias
I'm now applying the Yi et al. (2019) correction. Subtracting $\log(p_i)$ from the logit of popular items reduces their influence during training, forcing the model to learn genuine relevance over mere popularity. This should resolve the over-prediction of popular items.
### Recorded reasoning summary
Improving Relevance Estimation
I've implemented the Yi et al. correction to account for item popularity in my loss function. This adjustment debiases the training by subtracting item popularity probabilities, ensuring the model learns genuine user preference rather than just popular items. This should rectify the over-prediction of highly popular items during inference.
### run_shell_command · 2026-09-29T01:41:47.697Z
{
"description": "Evaluate baseline with discovery-graded target weighting",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --target-weighting discovery-graded \\\n --output /app/solution/val_discovery_graded.parquet\npython /task/evaluate_public.py /app/solution/val_discovery_graded.parquet"
}
### Tool output · 2026-09-29T01:47:07.787Z
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=195.4s
epoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.3 validation_seconds=15.2
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/val_discovery_graded.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 312.362,
"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": 0.5421154191580255,
"positive_logit": 0.7774311369950272,
"negative_logit": -0.6407561995894085,
"pair_accuracy": 0.7602795720701684,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 208.31431418599982,
"validation_loss": 0.5048305711216913,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.20520111399992
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7521852254867554,
"query_norm_std": 0.8638167977333069,
"item_norm_mean": 1.8543914556503296,
"item_norm_std": 0.8459439277648926
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_discovery_graded.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 1924
## Step 50 · agent · 2026-09-29T01:47:12.380Z
### run_shell_command · 2026-09-29T01:47:12.380Z
{
"command": "rm -f /app/solution/val_discovery_graded.parquet",
"description": "Remove temporary validation parquet"
}
### Tool output · 2026-09-29T01:47:12.395Z
Output: (empty)
Process Group PGID: 1973
## Step 51 · agent · 2026-09-29T01:47:32.746Z
### Recorded reasoning summary
Deepening Negative Sampling
I'm now delving into the intricacies of negative sampling and the loss function. My focus is on a detailed examination, ensuring alignment with the project's mature baseline and chronological validation requirements. This deep dive aims to refine our approach significantly.
### Recorded reasoning summary
Analyzing Negative Sampling Logic
I'm currently investigating how our training objective selects and utilizes negative examples. My focus is on diagnosing limitations within the current strategy and improving it while adhering to the CPU budget. I'll validate these changes with the public backtest to ensure we meet all constraints.
### Recorded reasoning summary
Refining Negative Sampling Methods
I'm currently implementing a more sophisticated negative sampling strategy. This involves drawing mixed negatives and ensuring all known public-training positives are rejected, aiming for a robust and efficient approach.
### Recorded reasoning summary
Optimizing Negative Sample Generation
I'm now refining the generation of negative samples, specifically focusing on how we select and correct them. My attention is on efficiently handling unique targets and incorporating both uniform and in-batch negatives, ensuring we meet performance targets within the CPU budget while addressing invalid samples.
### Recorded reasoning summary
Correcting Negative Sample Errors
I'm now addressing errors arising during negative sample generation. My current focus is on the correction mechanism for invalid samples, specifically how rejected in-batch items are handled as uniform draws. This ensures the integrity of the training data.
### Recorded reasoning summary
Investigating Resampling Logic
I'm closely examining the resampling process, specifically how invalid in-batch negatives are replaced with uniform draws. My focus is on quantifying these replacements and understanding the composition of in-batch negatives, particularly how unique targets might inadvertently include items the user has already interacted with.
### Recorded reasoning summary
Revising Negative Sampling Strategy
I'm reconsidering our negative sampling approach, moving away from fixed ratios of uniform and in-batch negatives. My focus is on exploring the benefits of increasing in-batch negatives, inspired by techniques like Mixed Negative Sampling, for improved efficiency and coverage of the data distribution.
### Recorded reasoning summary
Analyzing In-Batch Filtering
I'm now dissecting how in-batch negative items are filtered. My focus is on the process where an item already interacted with by a specific user is masked out, preventing it from being a valid negative for that particular user.
### run_shell_command · 2026-09-29T01:47:32.746Z
{
"description": "Benchmark matrix multiplication 6400x192 @ 192x6400",
"command": "python -c \"\nimport torch\nimport time\n\nQ = torch.randn(6400, 192)\nV = torch.randn(6400, 192)\n\nfor _ in range(5):\n t0 = time.time()\n S = Q @ V.T\n t1 = time.time()\n print(f'6400x192 @ 192x6400: {(t1-t0)*1000:.2f}ms')\n\""
}
### Tool output · 2026-09-29T01:47:36.135Z
Output: 6400x192 @ 192x6400: 73.15ms
6400x192 @ 192x6400: 87.33ms
6400x192 @ 192x6400: 87.84ms
6400x192 @ 192x6400: 83.32ms
6400x192 @ 192x6400: 82.71ms
Process Group PGID: 1976
## Step 52 · agent · 2026-09-29T01:47:47.892Z
### Recorded reasoning summary
Analyzing Model Configuration
I'm currently scrutinizing `train_current_model.py`, specifically questioning the values set for `Config.uniform_negatives` and `Config.inbatch_negatives`. The presence of command-line options in this script is also a point of investigation.
### grep_search · 2026-09-29T01:47:47.892Z
{
"pattern": "add_argument.*negative",
"dir_path": "/app/solution",
"include_pattern": "train_current_model.py"
}
### Tool output · 2026-09-29T01:47:47.909Z
No matches found for pattern "add_argument.*negative" in path "/app/solution" (filter: "train_current_model.py").
## Step 53 · agent · 2026-09-29T01:48:33.382Z
### Recorded reasoning summary
Clarifying Argument Parsing
I'm reviewing the argument parsing logic, specifically noting that `--uniform-negatives` and `--inbatch-negatives` aren't registered parameters. I'm focusing on the `add_argument` calls to confirm only `--negative-filter-scope` is present, and examining the `Config` instantiation in `main()` to understand how these configurations are handled.
### Recorded reasoning summary
Investigating Configuration Loading
I've identified that several configuration parameters, like `uniform_negatives` and `inbatch_negatives`, are defined within the `Config` class but are not exposed as command-line arguments. My current focus is on how these internal `Config` settings are populated and utilized, as they aren't directly mapped from `args`.
### Recorded reasoning summary
Analyzing Negative Sampling
I'm now delving into how negative examples are utilized within the `corrected_negative_loss` function of the current trainer. My aim is to pinpoint limitations in the existing negative-sampling strategy, hypothesize potential improvements, and then validate these changes against the public backtest, all while respecting the CPU budget and serving contracts.
### Recorded reasoning summary
Refining Negative Loss Calculation
I'm scrutinizing the `corrected_negative_loss` function and its interaction with `score_target_microbatches`. My focus is on how `uniform_negatives` and `inbatch_negatives` are weighted and applied, specifically examining the division by `total` and the `clamp_min` for stability, to ensure accurate loss computation.
### Recorded reasoning summary
Adjusting Loss Weights
I'm analyzing the impact of current loss calculations. The ratio of positive to negative item weighting is severely skewed, with positives having 32 times more influence. This imbalance is critically hindering effective retrieval training, prompting an urgent need to re-evaluate and rebalance these weights.
### Recorded reasoning summary
Revising Loss Strategy
I'm questioning the fundamental approach to negative loss in BCE. The tendency to simply amplify popular item embeddings is problematic as it doesn't effectively push down false positives. I'm considering alternative loss functions like InfoNCE for retrieval, or re-evaluating how negative loss is scaled within BCE.
### Recorded reasoning summary
Exploring InfoNCE Properties
I'm investigating the mathematical underpinnings of InfoNCE, particularly its gradient behavior with respect to hard negatives and how its structure inherently addresses the positive/negative loss balancing issue. I'm also considering the implications of the temperature parameter $\tau$.
### Recorded reasoning summary
Analyzing InfoNCE Gradient
I'm examining how InfoNCE's gradient behaves with hard negatives and its inherent balance of positive/negative loss, considering the impact of the temperature parameter $\tau$. My research suggests that InfoNCE naturally avoids popularity collapse by adjusting logits based on item popularity, leading to better retrieval.
### Recorded reasoning summary
Evaluating Embedding Normalization
I'm considering normalizing embeddings or using cosine similarity for better logit ranges, as current dot products are limited. However, the serving contract uses raw dot products in `rank_candidates`, creating a constraint. I need to reconcile this with the potential benefits of normalized representations.
### Recorded reasoning summary
Investigating Negative Selection
I'm now examining how negatives are drawn, specifically questioning the origin of `uniform_negatives` and `inbatch_negatives`. My focus is on understanding the selection process for these negatives, considering the implications for retrieval model training and potential biases.
### Recorded reasoning summary
Exploring Negative Sampling Strategy
I'm analyzing the impact of negative sampling methods. Uniform negatives, often tail tracks, provide trivial distinctions, leading to coarse popularity learning. In-batch negatives, however, force finer distinctions by contrasting listened songs, thus teaching nuanced preferences. I am now exploring alternative ratios and sampling pools for negatives.
### Recorded reasoning summary
Analyzing In-Batch Negative Sampling
I'm digging into how in-batch negatives were implemented, specifically questioning the use of `torch.unique` on targets. This seems to limit `unique_targets` to each song appearing only once per batch, which feels off for generating diverse in-batch negatives. I'm exploring the impact of this on the sampling process.
### Recorded reasoning summary
Correcting In-Batch Sampling
I've discovered the in-batch negative sampling implementation is fundamentally flawed. It uniformly samples from unique targets, negating the intended proportional sampling based on listen counts. This needs immediate correction to reflect the true definition and purpose of in-batch negatives.
### Recorded reasoning summary
Correcting Loss Weighting
I've identified a critical flaw where popular items were uniformly sampled as negatives and their loss contribution was significantly downweighted. This made them effectively immune to loss penalties, leading to the model's inability to properly learn from them. I'm now focused on rectifying this imbalance.
### Recorded reasoning summary
Revising Importance Sampling Logic
I'm realizing the baseline's attempt to estimate uniform negative loss using in-batch negatives was fundamentally flawed. The prior implementation's weighting scheme incorrectly inverted the importance sampling principle, aiming to treat naturally hard, popular in-batch negatives as if they were uniformly sampled. This needs complete correction.
### Recorded reasoning summary
Correcting Baseline Strategy
My realization is that the baseline attempted to use all negatives (24 uniform + 8 in-batch) to simulate uniform random sampling. This means 99% of these negatives were unlistened tail items, which leads models to only learn to recommend the globally most popular items, explaining the observed recommendation pattern.
### Recorded reasoning summary
Testing In-Batch Negatives
I'm now investigating the impact of not downweighting in-batch negatives by $1/p(x)$. My focus is on treating these in-batch negatives as hard negatives, considering the common practice in recommendation systems like YouTube and Spotify, where their real-world listenership is key.
### Recorded reasoning summary
Testing In-Batch Negatives
I'm investigating how not downweighting in-batch negatives by $1/p(x)$ impacts performance. My focus is on treating these as hard negatives, aligning with practices in systems like YouTube and Spotify where real-world listenership is crucial for effective contrastive learning.
### Recorded reasoning summary
Revising Validation Logic
I'm realizing the validation metric must align with the training loss for effective checkpoint selection. If I train with InfoNCE or a modified BCE, the `evaluate_validation_bce` function needs to reflect that, otherwise, early stopping decisions will be flawed and my model performance will suffer.
### run_shell_command · 2026-09-29T01:48:33.382Z
{
"description": "Backup original train_current_model.py",
"command": "cp /app/solution/train_current_model.py /app/solution/train_current_model_baseline.py"
}
### Tool output · 2026-09-29T01:48:33.401Z
Output: (empty)
Process Group PGID: 1991
## Step 54 · agent · 2026-09-29T01:50:12.887Z
### Recorded reasoning summary
Refining Negative Sampling
I'm currently diving into `train_current_model.py` to explore modifications in negative sampling, specifically focusing on the `draw_full_history_filtered_negatives` function. My next step is to test different approaches here to see how they impact the model's performance.
### Recorded reasoning summary
Adjusting Negative Sampling Ratios
I'm experimenting with flipping the ratios of uniform vs. in-batch negatives. Additionally, I'm considering sampling in-batch negatives directly from `targets_cpu` for a frequency-based approach, and evaluating the impact of removing the `item_correction` downweighting on in-batch negatives within the `corrected_negative_loss` function.
### Recorded reasoning summary
Revising Loss Calculation
I'm reassessing how popular items are handled in negative sampling. By removing the downweighting on popular items when sampling in-batch negatives, I'm aiming to ensure they're penalized more effectively. I'm also exploring if InfoNCE or cross-entropy loss would be a more suitable framework.
### Recorded reasoning summary
Evaluating InfoNCE Implementation
I've realized the elegance of InfoNCE directly using `F.cross_entropy`. This method naturally handles positive and negative logits without arbitrary separation, ensuring that a single strong negative can significantly influence the gradient. The simplicity is striking.
### Recorded reasoning summary
Exploring Temperature Scaling
I'm now focused on the temperature parameter $\tau$ in the InfoNCE loss. It appears to inherently perform hard negative mining. I'm investigating optimal values, considering the norms of our embeddings and common practices in related retrieval models like CLIP, potentially ranging from 0.05 to 0.2.
### Recorded reasoning summary
Testing LogQ Correction
I'm now considering logQ correction within the InfoNCE framework, inspired by sampling-bias correction. This involves subtracting the log of an item's sampling probability from its logit. I'm eager to test how this impacts personalized ranking at inference time, moving beyond simple popularity.
### Recorded reasoning summary
Adding Configuration Options
I'm planning to integrate CLI arguments and configuration options to manage loss types, temperatures, negative sampling strategies, and target weighting. My immediate task is to update `train_current_model.py` to accommodate these parameters for streamlined experimentation and robust public NDCG measurement.
### Recorded reasoning summary
Adding Configuration Options
I'm planning to integrate CLI arguments and configuration options to manage loss types, temperatures, negative sampling strategies, and target weighting. My immediate task is to update `train_current_model.py` to accommodate these parameters for streamlined experimentation and robust public NDCG measurement.
**Implementing Configuration Parameters**
I've started integrating new CLI arguments into `main()` for loss type, temperature, uniform and in-batch negative counts, and in-batch sampling frequency. This will allow for more flexible and systematic testing of different configurations, making it easier to manage hyperparameters.
**Analyzing Score Target Microbatches**
I am now scrutinizing the `score_target_microbatches` function to understand its loss and gradient computation. My goal is to ensure these newly added configuration parameters are correctly integrated into the existing loss calculation logic, ensuring accurate and efficient training.
### Recorded reasoning summary
Validating Gradient Flow
I am now examining the `score_target_microbatches` function, specifically how loss and gradients are computed within its microbatches. My focus is on confirming the seamless integration of the new configuration parameters into the existing loss calculation logic to ensure precision and efficiency.
### Recorded reasoning summary
Validating Microbatch Logic
I'm currently stepping through `score_target_microbatches` to verify how the loss and gradients are calculated within these segments. My primary concern is confirming that the new configuration parameters integrate correctly into the existing loss computation pipeline, ensuring everything flows as expected for precise training.
### Recorded reasoning summary
Verifying Microbatch Gradient Flow
I'm meticulously reviewing `score_target_microbatches`, tracing the loss and gradient computations within each microbatch. My immediate focus is confirming the correct integration of the new configuration parameters into the existing loss calculation pipeline to ensure accurate and efficient training.
### read_file · 2026-09-29T01:50:12.887Z
{
"end_line": 1485,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1364
}
### Tool output · 2026-09-29T01:50:12.895Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1364-1485 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: 1486.
--- FILE CONTENT (truncated) ---
def evaluate_validation_bce(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
np.argsort(totals[users_with_validation], kind="stable")
]
groups = [
ordered[start : start + config.user_batch_size]
for start in range(0, len(ordered), config.user_batch_size)
]
generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
total_loss = 0.0
total_targets = 0
total_weight = 0.0
started = time.monotonic()
for group_number, users in enumerate(groups, start=1):
group_totals = totals[users]
group_starts = training_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_totals.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_totals,
group_starts,
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0)
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
statistics, _ = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=False,
)
total_loss += statistics["loss_sum"]
total_targets += len(positive_items)
total_weight += statistics["weight_sum"]
if group_number % 25 == 0:
release_accelerator_cache(device)
return {
"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,
}
## Step 55 · agent · 2026-09-29T01:50:37.855Z
### Recorded reasoning summary
Confirming Validation Loss Calculation
I've confirmed that the `evaluate_validation_bce` function directly calculates the validation loss by calling `score_target_microbatches` with `backward=False`. This means the validation loss is being accurately determined during the evaluation phase. My next step is to examine the `item_correction` and logging mechanisms.
### Recorded reasoning summary
Investigating Item Correction Logic
I'm now analyzing the `build_item_correction` function. My current focus is on how it estimates in-batch propensities. I'm tracing the calculation of counts and probabilities to understand its contribution to the model's behavior.
### Recorded reasoning summary
Analyzing In-batch Correction
I'm scrutinizing the in-batch correction logic. It appears the `probabilities` used are derived from the overall target item probabilities across the training corpus. I'm comparing this approach to Google's Sampled Softmax with LogQ correction, specifically examining how positive and candidate item logits are adjusted based on these probabilities.
### Recorded reasoning summary
Exploring Loss Function Variations
I'm now considering switching to InfoNCE for evaluation to better understand its behavior without the complexities of logQ correction. I'm also reviewing the configuration parameters that will be needed to support these different loss functions, including temperature, negative sampling strategies, and weighting schemes, to ensure flexibility and comparability.
### Recorded reasoning summary
Refining Target Sampling
I'm re-examining how in-batch targets are sampled. The current approach draws randomly from all unique targets. I'm now considering an alternative where `inbatch` is sampled directly from the `targets_cpu` for each row, potentially improving efficiency and relevance.
### Recorded reasoning summary
Simplifying Target Sampling
I've identified a more efficient method for sampling in-batch targets. By directly sampling from `targets_cpu`, items with higher frequency within the batch naturally become more probable to be selected. This also eliminates significant tensor allocation and processing steps, streamlining the entire pipeline considerably.
### Recorded reasoning summary
Refining Negative Sampling
I'm now focusing on the logic for handling rejected negative items. It seems rejected items are replaced with random catalog items uniformly. I'm considering making the number of in-batch versus uniform negatives configurable via CLI arguments to further tune the sampling strategy.
### Recorded reasoning summary
Configuring Negative Sampling
I'm now looking at how negative items are handled. It appears rejected items are being replaced with random catalog items uniformly. I plan to make the ratio of in-batch to uniform negatives a configurable parameter via CLI arguments to better control this aspect.
### Recorded reasoning summary
Implementing Loss Function Logic
I'm now integrating the logic for the InfoNCE loss function. This involves correctly calculating scaled logits and applying cross-entropy. I'm also preparing to expose new CLI arguments for loss type, temperature, and negative sampling configurations to provide greater control over the evaluation process.
### Recorded reasoning summary
Integrating InfoNCE and LogQ
I'm now integrating the logic for the InfoNCE loss function, specifically focusing on how `logQ` correction is computed for both positive and negative items. My observation is that while `loss_sum` is calculated using InfoNCE cross-entropy, the `positive_sum`, `negative_sum`, `pair_correct`, and `weight_sum` statistics remain identical across different loss calculations, which I need to verify.
### Recorded reasoning summary
Considering BPR Loss
I'm now exploring the BPR loss function as an alternative to InfoNCE. It appears to directly penalize when negative logits exceed positive logits, offering a different approach to ranking optimization. I'm evaluating how its formulation and resulting loss calculation might compare to current methods.
### read_file · 2026-09-29T01:50:37.855Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 40,
"end_line": 75
}
### Tool output · 2026-09-29T01:50:37.864Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 40-75 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 76.
--- FILE CONTENT (truncated) ---
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
## Step 56 · agent · 2026-09-29T01:50:41.583Z
### Recorded reasoning summary
Refining Configuration Options
I'm considering adding `loss_type`, `temperature`, and `inbatch_frequency_sample` to our `Config`. I'm also exploring the potential impact of introducing an `inbatch_correction_power` parameter.
### read_file · 2026-09-29T01:50:41.583Z
{
"end_line": 1990,
"start_line": 1925,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:50:41.591Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1925-1990 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) ---
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
## Step 57 · agent · 2026-09-29T01:50:53.220Z
### Recorded reasoning summary
Considering Loss Function Options
I'm currently exploring the inclusion of different loss function types in `main()`, specifically evaluating options like "bce", "infonce", "infonce-logq", and "bpr". I'm also looking at integrating a configurable temperature parameter.
### Recorded reasoning summary
Implementing In-Batch Negatives
I'm now focusing on the practical implementation of in-batch negative sampling, particularly the `draw_full_history_filtered_negatives` function. I'm examining the conditional logic when `config.inbatch_frequency_sample` is enabled, and how this interacts with the calculation of `corrected_count` and subsequent priority sampling.
### Recorded reasoning summary
Refining In-Batch Sampling
I've successfully refactored the in-batch negative sampling logic. When `inbatch_frequency_sample` is active, I'm now directly sampling from `targets_cpu`, which significantly boosts performance. This approach bypasses the previous, slower `priorities.masked_fill_` and `torch.topk` operations.
### Recorded reasoning summary
Analyzing Score Calculation
I'm currently analyzing the `score_target_microbatches` function, specifically how it computes `positive_logits` and `negative_logits`. I'm investigating the conditional logic for "infonce" and "bpr" loss types and their integration with temperature scaling and cross-entropy calculations.
### Recorded reasoning summary
Evaluating Infonce Loss
I am currently dissecting the `infonce` loss calculation, specifically scrutinizing how `all_logits` is constructed and what constitutes `negative_logits`. My focus is on the implications of `all_logits` concatenating positive logits with `negative_logits` of shape `(B, K)`.
### Recorded reasoning summary
Adjusting infonce-logq Logic
I've noticed an oversight in my previous analysis of `infonce-logq` loss. I was not accounting for the `negative_weights` correctly, which are already being passed and applied to the score calculation based on the `correction` logic. I need to re-evaluate the direct impact of these pre-computed weights.
### read_file · 2026-09-29T01:50:53.220Z
{
"start_line": 150,
"file_path": "/app/solution/train_current_model.py",
"end_line": 170
}
### Tool output · 2026-09-29T01:50:53.229Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 150-170 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: 171.
--- FILE CONTENT (truncated) ---
def model_config(config: Config) -> base.TowerConfig:
"""Map the dense trainer configuration onto the unchanged tower."""
return base.TowerConfig(
sequence_length=config.bptt_steps,
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim,
user_hidden_1=config.user_hidden_1,
user_hidden_2=config.user_hidden_2,
item_hidden_1=config.item_hidden_1,
item_hidden_2=config.item_hidden_2,
dropout=config.dropout,
epochs=config.epochs,
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
gradient_clip=config.gradient_clip,
uniform_negatives=config.uniform_negatives,
inbatch_negatives=config.inbatch_negatives,
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
seed=config.seed,
## Step 58 · agent · 2026-09-29T01:50:59.064Z
### Recorded reasoning summary
Exploring Model Configuration
I'm currently examining how `model_config` instantiates `base.TowerConfig`, specifically noting the parameters like `uniform_negatives` and `inbatch_negatives` being passed. This detail seems crucial for understanding the underlying configuration mechanics.
### Recorded reasoning summary
Implementing Loss Variants
I'm now focused on integrating new loss functionalities. This involves defining `loss_type` and `temperature` parameters, and expanding `score_target_microbatches` to support InfoNCE, BPR, and BCE. Additionally, I'll be addressing the `inbatch_frequency_sample` flag and its impact.
### read_file · 2026-09-29T01:50:59.064Z
{
"end_line": 80,
"start_line": 40,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:50:59.073Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 40-80 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 81.
--- FILE CONTENT (truncated) ---
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
item_ids: np.ndarray
packed: np.ndarray
## Step 59 · agent · 2026-09-29T01:51:01.531Z
### read_file · 2026-09-29T01:51:01.531Z
{
"end_line": 685,
"file_path": "/app/solution/train_current_model.py",
"start_line": 645
}
### Tool output · 2026-09-29T01:51:01.539Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 645-685 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: 686.
--- FILE CONTENT (truncated) ---
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
## Step 60 · agent · 2026-09-29T01:51:04.472Z
### read_file · 2026-09-29T01:51:04.472Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1160,
"end_line": 1195
}
### Tool output · 2026-09-29T01:51:04.479Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1160-1195 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1196.
--- FILE CONTENT (truncated) ---
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
## Step 61 · agent · 2026-09-29T01:51:07.282Z
### read_file · 2026-09-29T01:51:07.282Z
{
"end_line": 2050,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1935
}
### Tool output · 2026-09-29T01:51:10.366Z
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 62 · agent · 2026-09-29T01:51:21.081Z
### Recorded reasoning summary
Exploring Configuration Options
I'm currently examining the `Config` and `args` definitions. Specifically, I'm focusing on how the `loss_type` parameter is handled, noting its default value and available choices as provided in the arguments. This helps me understand the flexibility and current settings for this critical component.
### Recorded reasoning summary
Investigating Negative Sampling
My focus has shifted to the `draw_full_history_filtered_negatives` function. I'm analyzing its handling of in-batch negatives, specifically how `config.inbatch_negatives` and `config.inbatch_frequency_sample` influence the generation of negative samples.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm deep into the `draw_full_history_filtered_negatives` function, specifically the part that calculates `corrected_count`. I'm looking at how it uses `config.inbatch_negatives` and the number of unique targets to determine the actual number of in-batch negatives, ensuring it doesn't exceed available unique targets. I'm also tracing how it then generates those `inbatch` samples and handles any remaining `filler` negatives.
### Recorded reasoning summary
Analyzing Loss Calculation
I'm now scrutinizing the `score_target_microbatches` function, particularly how different `config.loss_type` values like "infonce" and "bpr" impact logit calculations and cross-entropy losses. I'm tracing how positive and negative logits are combined and scaled with temperature before loss computation.
### replace · 2026-09-29T01:51:21.081Z
{
"old_string": " early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29",
"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 = 0.1\n inbatch_frequency_sample: bool = False",
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add loss_type, temperature, and inbatch_frequency_sample to Config"
}
### Tool error · 2026-09-29T01:51:21.091Z
EACCES: permission denied, open '/app/solution/train_current_model.py'
## Step 63 · agent · 2026-09-29T01:51:24.634Z
### run_shell_command · 2026-09-29T01:51:24.634Z
{
"command": "ls -la /app/solution",
"description": "Check file permissions in /app/solution"
}
### Tool output · 2026-09-29T01:51:24.653Z
Output: total 1064568
drwxr-xr-x 1 agent agent 4096 Sep 29 01:48 .
drwxr-xr-x 1 agent agent 4096 Sep 29 01:18 ..
-r--r--r-- 1 agent agent 5774 Sep 29 01:22 evaluate_public.py
-rw-r--r-- 1 agent agent 552686232 Sep 29 01:40 prepared_sub.npz
-rw-r--r-- 1 agent agent 535225808 Sep 29 01:30 prepared_val.npz
-rw-r--r-- 1 agent agent 1244226 Sep 29 01:28 production_validation.parquet
-r--r--r-- 1 agent agent 8265 Sep 29 01:22 ranking_evaluation.py
-rw-r--r-- 1 agent agent 735840 Sep 29 01:40 submission.parquet
-r--r--r-- 1 agent agent 80058 Sep 29 01:22 train_current_model.py
-r--r--r-- 1 agent agent 80058 Sep 29 01:48 train_current_model_baseline.py
-r--r--r-- 1 agent agent 15985 Sep 29 01:22 two_tower_model.py
Process Group PGID: 1994
## Step 64 · agent · 2026-09-29T01:51:27.115Z
### run_shell_command · 2026-09-29T01:51:27.115Z
{
"description": "Make files in /app/solution writable",
"command": "chmod -R u+w /app/solution"
}
### Tool output · 2026-09-29T01:51:27.133Z
Output: (empty)
Process Group PGID: 1997
## Step 65 · agent · 2026-09-29T01:51:31.873Z
### replace · 2026-09-29T01:51:31.873Z
{
"instruction": "Add loss_type, temperature, and inbatch_frequency_sample 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 = 0.1\n inbatch_frequency_sample: bool = False",
"old_string": " early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29"
}
### Tool output · 2026-09-29T01:51:31.899Z
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 = 0.1
inbatch_frequency_sample: bool = False
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
...
## Step 66 · agent · 2026-09-29T01:51:37.846Z
### read_file · 2026-09-29T01:51:37.846Z
{
"start_line": 650,
"end_line": 685,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:51:37.853Z
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 67 · agent · 2026-09-29T01:51:43.678Z
### replace · 2026-09-29T01:51:43.678Z
{
"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)",
"instruction": "Support inbatch_frequency_sample in draw_full_history_filtered_negatives",
"new_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n if config.inbatch_frequency_sample:\n rand_indices = torch.randint(\n len(targets_cpu),\n (batch_size, config.inbatch_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.from_numpy(targets_cpu)[rand_indices]\n corrected_count = config.inbatch_negatives\n else:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:51:43.703Z
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_frequency_sample:
rand_indices = torch.randint(
len(targets_cpu),
(batch_size, config.inbatch_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.from_numpy(targets_cpu)[rand_indices]
corrected_count = config.inbatch_negatives
else:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
...
## Step 68 · agent · 2026-09-29T01:51:48.154Z
### read_file · 2026-09-29T01:51:48.154Z
{
"start_line": 1170,
"end_line": 1205,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:51:48.162Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1170-1205 of 2178 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1206.
--- FILE CONTENT (truncated) ---
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()
## Step 69 · agent · 2026-09-29T01:51:53.134Z
### replace · 2026-09-29T01:51:53.134Z
{
"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 all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n scaled = all_logits / config.temperature\n target_indices = torch.zeros(len(scaled), dtype=torch.long, device=device)\n sample_loss = F.cross_entropy(scaled, target_indices, reduction=\"none\")\n loss_sum = (selected_weights * sample_loss).sum()\n elif config.loss_type == \"bpr\":\n diff = negative_logits - positive_logits.unsqueeze(1)\n sample_loss = F.softplus(diff / config.temperature).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()",
"instruction": "Add infonce and bpr loss support to score_target_microbatches",
"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()",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:51:53.154Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
if config.loss_type == "infonce":
all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)
scaled = all_logits / config.temperature
target_indices = torch.zeros(len(scaled), dtype=torch.long, device=device)
sample_loss = F.cross_entropy(scaled, target_indices, reduction="none")
loss_sum = (selected_weights * sample_loss).sum()
elif config.loss_type == "bpr":
diff = negative_logits - positive_logits.unsqueeze(1)
sample_loss = F.softplus(diff / config.temperature).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 70 · agent · 2026-09-29T01:51:55.523Z
### read_file · 2026-09-29T01:51:55.523Z
{
"start_line": 1940,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2070
}
### Tool output · 2026-09-29T01:51:55.531Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1940-2070 of 2189 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) ---
elif output.suffix == ".csv":
frame.to_csv(output, index=False)
else:
raise ValueError("--output must end in .parquet or .csv")
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
## Step 71 · agent · 2026-09-29T01:52:00.387Z
### replace · 2026-09-29T01:52:00.387Z
{
"file_path": "/app/solution/train_current_model.py",
"old_string": " parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()",
"instruction": "Add CLI arguments and pass them to Config in main()",
"new_string": " parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce\", \"infonce\", \"bpr\"),\n default=Config.loss_type,\n )\n parser.add_argument(\"--temperature\", type=float, default=Config.temperature)\n parser.add_argument(\n \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n )\n parser.add_argument(\n \"--inbatch-negatives\", type=int, 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(\"--inbatch-frequency-sample\", action=\"store_true\")\n args = parser.parse_args()"
}
### Tool output · 2026-09-29T01:52:00.406Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
parser.add_argument(
"--loss-type",
choices=("bce", "infonce", "bpr"),
default=Config.loss_type,
)
parser.add_argument("--temperature", type=float, default=Config.temperature)
parser.add_argument(
"--uniform-negatives", type=int, default=Config.uniform_negatives
)
parser.add_argument(
"--inbatch-negatives", type=int, default=Config.inbatch_negatives
)
parser.add_argument(
"--inbatch-correction-power",
type=float,
default=Config.inbatch_correction_power,
)
parser.add_argument("--inbatch-frequency-sample", 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 (
...
## Step 72 · agent · 2026-09-29T01:52:02.966Z
### read_file · 2026-09-29T01:52:02.966Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 2055,
"end_line": 2090
}
### Tool output · 2026-09-29T01:52:02.973Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2055-2090 of 2207 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: 2091.
--- FILE CONTENT (truncated) ---
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 73 · agent · 2026-09-29T01:52:06.925Z
### replace · 2026-09-29T01:52:06.925Z
{
"old_string": " early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )",
"instruction": "Pass new CLI options to Config in main()",
"file_path": "/app/solution/train_current_model.py",
"new_string": " 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 uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n inbatch_correction_power=args.inbatch_correction_power,\n inbatch_frequency_sample=args.inbatch_frequency_sample,\n )"
}
### Tool output · 2026-09-29T01:52:06.944Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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,
uniform_negatives=args.uniform_negatives,
inbatch_negatives=args.inbatch_negatives,
inbatch_correction_power=args.inbatch_correction_power,
inbatch_frequency_sample=args.inbatch_frequency_sample,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
...
## Step 74 · agent · 2026-09-29T01:52:10.280Z
### read_file · 2026-09-29T01:52:10.280Z
{
"start_line": 2150,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2175
}
### Tool output · 2026-09-29T01:52:10.287Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2150-2175 of 2213 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: 2176.
--- FILE CONTENT (truncated) ---
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",
## Step 75 · agent · 2026-09-29T01:52:13.395Z
### Recorded reasoning summary
Exploring Architecture Details
I'm currently examining the `architecture` block, specifically lines 2170-2190, to understand the scoring mechanism, objective function, and negative sampling strategy. This section is crucial for grasping the model's core learning process.
### read_file · 2026-09-29T01:52:13.395Z
{
"start_line": 2170,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2200
}
### Tool output · 2026-09-29T01:52:13.402Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2170-2200 of 2213 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: 2201.
--- FILE CONTENT (truncated) ---
"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,
## Step 76 · agent · 2026-09-29T01:52:22.915Z
### Recorded reasoning summary
Testing Configurations
I'm currently exploring various configurations. My immediate focus is on verifying default execution and identifying any potential crashes. Following that, I plan to test specific loss types like BPR and InfoNCE, and will also investigate the optimal temperature setting for InfoNCE.
### run_shell_command · 2026-09-29T01:52:22.915Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --target-weighting discovery-graded \\\n --loss-type infonce \\\n --temperature 0.1 \\\n --output /app/solution/val_infonce_01.parquet\npython /task/evaluate_public.py /app/solution/val_infonce_01.parquet",
"description": "Train and evaluate InfoNCE with temp=0.1 on validation backtest"
}
### Tool output · 2026-09-29T01:57:59.629Z
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=2.882234 running_bce=2.888013 elapsed=202.9s
epoch=1/1 train_loss=2.87533 validation_loss=2.66998 pair_accuracy=0.775 train_targets=1,052,953 validation_targets=130,103 train_seconds=216.3 validation_seconds=16.0
restored best validation-BCE checkpoint: epoch=1 loss=2.669981
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/val_infonce_01.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 327.088,
"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": 0.1,
"inbatch_frequency_sample": false
},
"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": 2.8753293548020498,
"positive_logit": 0.01788053704554617,
"negative_logit": -0.12286158901484713,
"pair_accuracy": 0.7748257460209526,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 216.32477699699984,
"validation_loss": 2.669981013659102,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 16.029928746999758
}
],
"embedding_diagnostics": {
"query_norm_mean": 0.6981537938117981,
"query_norm_std": 0.19272516667842865,
"item_norm_mean": 0.7907130122184753,
"item_norm_std": 0.17432135343551636
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_infonce_01.parquet",
"scores": {
"quality_ndcg@10": 0.018216,
"quality_users": 4263
}
}
Process Group PGID: 2000
## Step 77 · agent · 2026-09-29T01:58:05.225Z
### Recorded reasoning summary
Observing Parameter Shift
I'm seeing a significant reduction in `query_norm_mean` and `item_norm_mean`, which dropped from 1.75 and 1.85 respectively. The `positive_logit` also decreased considerably from 0.77 to 0.0178, alongside a dip in `quality_ndcg@10` to 0.018216.
### run_shell_command · 2026-09-29T01:58:05.225Z
{
"command": "rm -f /app/solution/val_infonce_01.parquet",
"description": "Remove temporary validation parquet"
}
### Tool output · 2026-09-29T01:58:05.243Z
Output: (empty)
Process Group PGID: 2049
## Step 78 · agent · 2026-09-29T01:58:09.844Z
### Recorded reasoning summary
Analyzing BCE Baseline
I'm currently dissecting the baseline BCE loss, specifically the 0.023522 achieved by `discovery-graded`. My focus is on investigating `inbatch_correction_power`'s role, recalling its earlier tendency to downweight popular items.
### run_shell_command · 2026-09-29T01:58:09.844Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --target-weighting discovery-graded \\\n --inbatch-correction-power 0.0 \\\n --output /app/solution/val_corr_power_0.parquet\npython /task/evaluate_public.py /app/solution/val_corr_power_0.parquet",
"description": "Test inbatch_correction_power = 0.0 with discovery-graded weighting"
}
### Tool output · 2026-09-29T02:03:46.255Z
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=202.2s
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=215.8 validation_seconds=16.1
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/val_corr_power_0.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 328.131,
"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": 0.1,
"inbatch_frequency_sample": false
},
"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": 0.5610289441109853,
"positive_logit": 0.6264491075626895,
"negative_logit": -0.6609028982207017,
"pair_accuracy": 0.7632028388256645,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 215.77226310900005,
"validation_loss": 0.5201467735449403,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 16.066914179000378
}
],
"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/val_corr_power_0.parquet",
"scores": {
"quality_ndcg@10": 0.015049,
"quality_users": 4263
}
}
Process Group PGID: 2052
## Step 79 · agent · 2026-09-29T02:04:10.559Z
### Recorded reasoning summary
Analyzing Correction Impact
I'm investigating a significant NDCG drop to 0.0150 when `inbatch_correction_power` was set to 0.0, down from 0.0235 at 0.5. This suggests the correction mechanism, even at a low setting, is crucial. I'm now examining the `build_item_correction` function to understand the underlying calculation.
### run_shell_command · 2026-09-29T02:04:10.559Z
{
"description": "Remove temporary validation parquet",
"command": "rm -f /app/solution/val_corr_power_0.parquet"
}
### Tool output · 2026-09-29T02:04:10.577Z
Output: (empty)
Process Group PGID: 2101
## Step 80 · agent · 2026-09-29T02:04:13.795Z
### run_shell_command · 2026-09-29T02:04:13.795Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --target-weighting discovery-graded \\\n --inbatch-correction-power 1.0 \\\n --output /app/solution/val_corr_power_1.parquet\npython /task/evaluate_public.py /app/solution/val_corr_power_1.parquet",
"description": "Test inbatch_correction_power = 1.0 with discovery-graded weighting"
}
### Tool output · 2026-09-29T02:09:47.687Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=0.971 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=0.540607 running_bce=0.533212 elapsed=201.8s
epoch=1/1 train_loss=0.53122 validation_loss=0.49435 pair_accuracy=0.759 train_targets=1,052,953 validation_targets=130,103 train_seconds=215.4 validation_seconds=15.9
restored best validation-BCE checkpoint: epoch=1 loss=0.494347
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/val_corr_power_1.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 325.686,
"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": 1.0,
"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": 0.1,
"inbatch_frequency_sample": false
},
"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": 0.531223179207967,
"positive_logit": 0.8379884010350294,
"negative_logit": -0.6214525562306966,
"pair_accuracy": 0.7585659390780025,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 215.40360276399997,
"validation_loss": 0.49434683065951257,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.922902670999974
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.4753897190093994,
"query_norm_std": 0.6710734367370605,
"item_norm_mean": 1.976240634918213,
"item_norm_std": 0.8238486051559448
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_corr_power_1.parquet",
"scores": {
"quality_ndcg@10": 0.023,
"quality_users": 4263
}
}
Process Group PGID: 2104
## Step 81 · agent · 2026-09-29T02:09:52.445Z
### run_shell_command · 2026-09-29T02:09:52.445Z
{
"command": "rm -f /app/solution/val_corr_power_1.parquet",
"description": "Remove temporary validation parquet"
}
### Tool output · 2026-09-29T02:09:52.462Z
Output: (empty)
Process Group PGID: 2153
## Step 82 · agent · 2026-09-29T02:09:56.180Z
### Recorded reasoning summary
Adjusting Negative Sampling
I'm considering tweaking the balance between uniform and in-batch negative samples. My current hypothesis is to shift towards an equal distribution, specifically testing a 16:16 ratio from the current 24:8. I'll proceed with this exploration to see how it impacts performance.
### run_shell_command · 2026-09-29T02:09:56.180Z
{
"description": "Test uniform_negatives=16, inbatch_negatives=16",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 16 \\\n --inbatch-negatives 16 \\\n --output /app/solution/val_u16_i16.parquet\npython /task/evaluate_public.py /app/solution/val_u16_i16.parquet"
}
### Tool output · 2026-09-29T02:15:22.351Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.556066 running_bce=0.556343 elapsed=194.5s
epoch=1/1 train_loss=0.55431 validation_loss=0.52006 pair_accuracy=0.726 train_targets=1,052,953 validation_targets=130,103 train_seconds=207.5 validation_seconds=14.6
restored best validation-BCE checkpoint: epoch=1 loss=0.520058
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/val_u16_i16.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 317.967,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 16,
"inbatch_negatives": 16,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"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": 0.1,
"inbatch_frequency_sample": false
},
"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": 0.5543083903753837,
"positive_logit": 0.6960524614353784,
"negative_logit": -0.5012892156770395,
"pair_accuracy": 0.7255772871628648,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 207.45100623500002,
"validation_loss": 0.5200578083106654,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 14.572256708000168
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],
"embedding_diagnostics": {
"query_norm_mean": 1.7176122665405273,
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"device": "cpu"
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{
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"scores": {
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"quality_users": 4263
}
}
Process Group PGID: 2156
## Step 83 · agent · 2026-09-29T02:15:26.989Z
### run_shell_command · 2026-09-29T02:15:26.989Z
{
"description": "Remove temporary validation parquet",
"command": "rm -f /app/solution/val_u16_i16.parquet"
}
### Tool output · 2026-09-29T02:15:27.010Z
Output: (empty)
Process Group PGID: 2207
## Step 84 · agent · 2026-09-29T02:15:34.882Z
### Recorded reasoning summary
Investigating In-Batch Negative Impact
I'm currently probing a peculiar performance dip. Introducing more in-batch negatives unexpectedly lowered pair accuracy and NDCG scores. I'm analyzing the underlying mechanisms to understand this counter-intuitive effect.
### Recorded reasoning summary
Examining Candidate Sets
I'm diving into how validation and test candidates are defined, noting they exclude training and validation encounters respectively. The relevance criteria for these candidates, based on user organic engagement, seems key. I'm also scrutinizing the high popularity of relevant items in our dataset.
### Recorded reasoning summary
Exploring Negative Sampling Strategy
I'm re-evaluating our negative sampling. The `full-training-history` scope seems correct, but I'm questioning the impact of uniform negatives versus in-batch ones, especially their interaction and relative quantities, to explain the recent performance anomalies.
### run_shell_command · 2026-09-29T02:15:34.882Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 32 \\\n --inbatch-negatives 0 \\\n --output /app/solution/val_u32_i0.parquet\npython /task/evaluate_public.py /app/solution/val_u32_i0.parquet",
"description": "Test uniform_negatives=32, inbatch_negatives=0"
}
### Tool output · 2026-09-29T02:20:53.534Z
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=186.7s
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=199.1 validation_seconds=15.3
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
{
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"stage": "validation",
"output": "/app/solution/val_u32_i0.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 310.452,
"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": 0.1,
"inbatch_frequency_sample": false
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
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"loss": 0.5254377354844952,
"positive_logit": 0.9194933287193273,
"negative_logit": -0.8096884086535646,
"pair_accuracy": 0.7977172592223964,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 199.0963418270003,
"validation_loss": 0.4844796381698273,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.340325407999899
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],
"embedding_diagnostics": {
"query_norm_mean": 1.4759684801101685,
"query_norm_std": 0.6978867053985596,
"item_norm_mean": 1.9009125232696533,
"item_norm_std": 0.8285666108131409
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"torch_version": "2.8.0+cpu",
"device": "cpu"
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{
"model": "/app/solution/val_u32_i0.parquet",
"scores": {
"quality_ndcg@10": 0.022328,
"quality_users": 4263
}
}
Process Group PGID: 2210
## Step 85 · agent · 2026-09-29T02:20:59.387Z
### Recorded reasoning summary
Exploring Negative Sampling Strategies
I'm evaluating the impact of negative sampling. While increasing uniform negatives boosted pair accuracy significantly, it appears to have slightly reduced NDCG. This suggests a need for a balanced approach, potentially incorporating some in-batch negatives for optimal retrieval performance.
### run_shell_command · 2026-09-29T02:20:59.387Z
{
"command": "rm -f /app/solution/val_u32_i0.parquet",
"description": "Remove temporary validation parquet"
}
### Tool output · 2026-09-29T02:20:59.404Z
Output: (empty)
Process Group PGID: 2259
## Step 86 · agent · 2026-09-29T02:21:08.023Z
### Recorded reasoning summary
Exploring Negative Sample Sizes
I'm currently investigating the impact of a higher total number of negative samples, specifically exploring a configuration with 48 uniform and 16 in-batch negatives (totaling 64). My next step is to assess how runtime scales under these increased negative sampling conditions.
## Recorded session usage
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