yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-r01-b9049e57
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T01:22:39.350Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
## Step 2 · user · 2026-09-29T01:22:39.391Z
# Improve a production music-discovery two-tower
You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team's next launch gate.
Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.
The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.
## Production Context
The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.
The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.
## What You Have
All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` window. For the final submission, that
validation window becomes observed history, and another 30-minute gap precedes
the hidden seven-day outcome window.
The package includes:
- `/task/current_model/train_current_model.py`: the production training and
ranking script. It can reproduce the public validation ranking and produce a
hidden-test submission after refitting.
- `/task/current_model/two_tower_model.py`: model primitives used by the
production script.
- `/task/evaluate_public.py`: the public validation evaluator for development.
- `/task/data/validation_eligible_candidates.npz` and
`/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for
the validation and hidden-test cutoffs.
- `/task/data/validation_preference_state.parquet` and
`/task/data/test_preference_state.parquet`: active explicit preference state
at the corresponding cutoffs.
- `/task/requirements.txt`: the exact offline Python distribution inventory.
You can reproduce the production public backtest with:
```sh
python /task/current_model/train_current_model.py \
--stage validation \
--public-dir /task \
--output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet
```
## Data
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action came
through a recommendation surface. A listen is a conventional positive at 50%
completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. For model development and selection, train only from `train_*` and score the
subsequent `val_*` outcomes.
2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
Validation candidates exclude all `train_*` encounters. Test candidates exclude
all `train_*` and `val_*` encounters. Future test interactions are hidden and
used only by the separate Harbor verifier.
## Discovery Objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.
## Deliverable
Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.
The ranking must:
- include the columns `uid`, `item_id`, and `rank`; only these columns are
evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
candidate set.
Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model-provider
connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
the deadline.
## Step 3 · agent · 2026-09-29T01:22:55.960Z
### Recorded reasoning summary
Analyzing Current Problem
I'm focusing on a thorough examination of the problem space and all stipulated requirements. My aim is to build a robust understanding before proceeding with any implementation details for the two-tower recommendation model.
### Recorded reasoning summary
Investigating Production Model
I'm diving deep into the production model's negative sampling strategy. My current focus is on diagnosing its limitations and formulating improvements within the given CPU constraints, aiming to boost retrieval quality.
### update_topic · 2026-09-29T01:22:55.960Z
{
"summary": "Beginning investigation of the two-tower recommendation model. Inspecting the codebase in /task/current_model, evaluation logic in /task/evaluate_public.py, and checking available data and environment.",
"title": "Initial Research & Baseline Exploration",
"strategic_intent": "Inspect existing production model code, evaluation script, and dataset in /task."
}
### Tool output · 2026-09-29T01:22:55.970Z
Current topic: "Initial Research & Baseline Exploration"
Topic summary: Beginning investigation of the two-tower recommendation model. Inspecting the codebase in /task/current_model, evaluation logic in /task/evaluate_public.py, and checking available data and environment.
Strategic Intent: Inspect existing production model code, evaluation script, and dataset in /task.
### list_directory · 2026-09-29T01:22:55.960Z
{
"dir_path": "/task"
}
### Tool error · 2026-09-29T01:22:55.970Z
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 4 · agent · 2026-09-29T01:23:01.747Z
### run_shell_command · 2026-09-29T01:23:01.747Z
{
"command": "ls -la /task && ls -la /task/current_model && ls -la /task/data",
"description": "List files in /task and subdirectories"
}
### Tool output · 2026-09-29T01:23:01.778Z
Output: total 40
dr-xr-xr-x 1 root root 4096 Sep 29 01:19 .
drwxr-xr-x 1 root root 4096 Sep 29 01:22 ..
-r--r--r-- 1 root root 0 Sep 29 01:17 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 data
-r--r--r-- 1 root root 5774 Sep 29 01:17 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:17 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:17 requirements.txt
total 104
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:19 ..
-r--r--r-- 1 root root 80058 Sep 29 01:17 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 01:17 two_tower_model.py
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:19 ..
-r--r--r-- 1 root root 349352 Sep 29 01:17 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:17 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:17 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:18 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:17 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:17 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:17 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:17 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:17 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:17 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:17 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:17 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:17 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:17 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:17 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:18 validation_preference_state.parquet
Process Group PGID: 1612
## Step 5 · agent · 2026-09-29T01:23:03.824Z
### run_shell_command · 2026-09-29T01:23:03.824Z
{
"command": "nproc && free -h && cat /task/requirements.txt",
"description": "Check available CPU cores, RAM, and requirements.txt"
}
### Tool output · 2026-09-29T01:23:03.840Z
Output: 8
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1617
## Step 6 · agent · 2026-09-29T01:23:05.566Z
### run_shell_command · 2026-09-29T01:23:05.566Z
{
"command": "cat /proc/meminfo | head -n 5 && cat /task/requirements.txt",
"description": "Check memory and requirements.txt"
}
### Tool output · 2026-09-29T01:23:05.582Z
Output: MemTotal: 15988972 kB
MemFree: 1393156 kB
MemAvailable: 14456344 kB
Buffers: 248280 kB
Cached: 12490076 kB
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
Process Group PGID: 1621
## Step 7 · agent · 2026-09-29T01:23:07.339Z
### run_shell_command · 2026-09-29T01:23:07.339Z
{
"description": "Read evaluate_public.py",
"command": "cat /task/evaluate_public.py"
}
### Tool output · 2026-09-29T01:23:07.352Z
Output: #!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py "
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1626
## Step 8 · agent · 2026-09-29T01:23:09.793Z
### Recorded reasoning summary
Analyzing Model Code
I'm currently reviewing the code for the two-tower model and its training script. My immediate focus is on understanding the architecture and training loop defined within these files to identify potential areas for optimization and improvement.
### run_shell_command · 2026-09-29T01:23:09.793Z
{
"command": "wc -l /task/current_model/*.py",
"description": "Count lines in current_model python files"
}
### Tool output · 2026-09-29T01:23:09.807Z
Output: 2164 /task/current_model/train_current_model.py
433 /task/current_model/two_tower_model.py
2597 total
Process Group PGID: 1629
## Step 9 · agent · 2026-09-29T01:23:11.906Z
### run_shell_command · 2026-09-29T01:23:11.906Z
{
"description": "Read first half of two_tower_model.py",
"command": "head -n 250 /task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T01:23:11.937Z
Output: """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()
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
Process Group PGID: 1632
## Step 10 · agent · 2026-09-29T01:23:13.623Z
### run_shell_command · 2026-09-29T01:23:13.623Z
{
"command": "sed -n '251,450p' /task/current_model/two_tower_model.py",
"description": "Read second half of two_tower_model.py"
}
### Tool output · 2026-09-29T01:23:13.640Z
Output: 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
Process Group PGID: 1635
## Step 11 · agent · 2026-09-29T01:23:16.396Z
### Recorded reasoning summary
Exploring Model Implementation
I'm examining `two_tower_model.py` to understand the `draw_filtered_mixed_negatives` function. My next step is to review `train_current_model.py` to grasp the training objective, data processing, and loss function implementation.
### run_shell_command · 2026-09-29T01:23:16.396Z
{
"command": "grep -n \"^def \\|^class \" /task/current_model/train_current_model.py",
"description": "List classes and functions in train_current_model.py"
}
### Tool output · 2026-09-29T01:23:16.411Z
Output: 41:class Config:
76:class CandidateSet:
99:class SequenceData:
127:class TargetPartition:
140:def log(message: str) -> None:
144:def seed_everything(seed: int) -> None:
150:def model_config(config: Config) -> base.TowerConfig:
174:def append_state_snapshot(
190:def causal_activity_features(
247:def build_sequence_parts(
314:def final_user_features(
341:def prepare_data(
471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
481:def save_cache(
493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
511:def load_or_prepare(
534:def build_item_correction(
578:def build_known_positive_filter(
609:def invalid_full_history_negatives(
630:def draw_full_history_filtered_negatives(
724:def draw_training_negatives(
763:def event_sequence(
774:def pool_explicit_states(
798:def user_queries(
813:def candidate_vectors(
828:def history_windows(
845:def corrected_negative_loss(
867:def retained_target_window_starts(
888:def chronological_target_partition(
914:def load_organic_like_outcomes(
955:def build_target_weights(
1035:def build_dense_block(
1111:def score_target_microbatches(
1198:def release_accelerator_cache(device: torch.device) -> None:
1203:def run_training_epoch(
1364:def evaluate_validation_bce(
1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
1495:def save_epoch_checkpoint(
1521:def train_model(
1782:def inference_queries(
1861:def rank_candidates(
1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
1922:def main() -> int:
Process Group PGID: 1638
## Step 12 · agent · 2026-09-29T01:23:18.561Z
### run_shell_command · 2026-09-29T01:23:18.561Z
{
"description": "Read Config class in train_current_model.py",
"command": "sed -n '40,140p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:18.575Z
Output: @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:
Process Group PGID: 1641
## Step 13 · agent · 2026-09-29T01:23:21.009Z
### run_shell_command · 2026-09-29T01:23:21.009Z
{
"description": "Read main function of train_current_model.py",
"command": "sed -n '1920,2050p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:21.023Z
Output: def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
Process Group PGID: 1644
## Step 14 · agent · 2026-09-29T01:23:22.821Z
### run_shell_command · 2026-09-29T01:23:22.821Z
{
"command": "sed -n '2051,2170p' /task/current_model/train_current_model.py",
"description": "Read end of train_current_model.py"
}
### Tool output · 2026-09-29T01:23:22.835Z
Output: if device.type == "mps" and not torch.backends.mps.is_available():
parser.error("MPS was requested but is unavailable")
started = time.monotonic()
prepared, eligibility = load_or_prepare(
public,
args.stage,
config,
args.prepared_cache.resolve() if args.prepared_cache else None,
)
target_partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
if args.prepare_only:
print(
json.dumps(
{
"stage": args.stage,
"sequence_users": len(prepared.user_ids),
"sequence_tokens": len(prepared.sequence_items),
"next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(
target_partition.window_starts.sum()
),
"target_users": len(eligibility.uids),
"runtime_seconds": round(time.monotonic() - started, 3),
"config": asdict(config),
},
indent=2,
)
)
return 0
model, training_history = train_model(
public,
args.stage,
prepared,
config,
device,
args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
)
ranking, embedding_diagnostics = rank_candidates(
model,
prepared,
eligibility,
config,
device,
args.ranking_batch_size,
)
write_ranking(ranking, args.output.resolve())
print(
json.dumps(
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": args.stage,
"output": str(args.output.resolve()),
"rows": len(ranking),
"users": int(ranking.uid.nunique()),
"runtime_seconds": round(time.monotonic() - started, 3),
"public_inputs_only": True,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": (
"every-adjacent-target-within-recent-chronological-window"
),
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": False,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": (
"24-uniform-plus-8-propensity-corrected-inbatch"
),
"false_negative_filter": config.negative_filter_scope,
"target_weighting": config.target_weighting,
},
"config": asdict(config),
"sequence_tokens": len(prepared.sequence_items),
"all_next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(target_partition.window_starts.sum()),
"training_history": training_history,
"embedding_diagnostics": embedding_diagnostics,
"torch_version": torch.__version__,
"device": str(device),
},
indent=2,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
Process Group PGID: 1647
## Step 15 · agent · 2026-09-29T01:23:25.949Z
### Recorded reasoning summary
Reviewing Core Directives
I'm currently dissecting the core instructions, specifically examining how the existing trainer, a mature baseline with chronological internal validation, utilizes negative samples within its training objective.
### run_shell_command · 2026-09-29T01:23:25.949Z
{
"description": "Read negative handling lines 530-650 in train_current_model.py",
"command": "sed -n '530,650p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:25.962Z
Output: 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",
)
Process Group PGID: 1650
## Step 16 · agent · 2026-09-29T01:23:34.796Z
### run_shell_command · 2026-09-29T01:23:34.796Z
{
"command": "sed -n '651,770p' /task/current_model/train_current_model.py",
"description": "Read draw_training_negatives in train_current_model.py"
}
### Tool output · 2026-09-29T01:23:34.809Z
Output: 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)
Process Group PGID: 1653
## Step 17 · agent · 2026-09-29T01:23:36.678Z
### run_shell_command · 2026-09-29T01:23:36.678Z
{
"command": "sed -n '840,960p' /task/current_model/train_current_model.py",
"description": "Read loss and weighting in train_current_model.py"
}
### Tool output · 2026-09-29T01:23:36.691Z
Output: windows = np.lib.stride_tricks.sliding_window_view(padded, width)
offset = start - base_start
return windows[offset : offset + count].copy()
def corrected_negative_loss(
logits: torch.Tensor,
correction: torch.Tensor,
config: Config,
) -> torch.Tensor:
raw = F.softplus(logits)
total = config.uniform_negatives + config.inbatch_negatives
result = torch.zeros(len(logits), device=logits.device)
if config.uniform_negatives:
result += config.uniform_negatives * raw[
:, : config.uniform_negatives
].mean(dim=1)
if config.inbatch_negatives:
selected = slice(config.uniform_negatives, total)
weights = correction[:, selected]
result += config.inbatch_negatives * (
(raw[:, selected] * weights).sum(dim=1)
/ weights.sum(dim=1).clamp_min(1.0e-6)
)
return result / total
def retained_target_window_starts(
total_targets: np.ndarray,
validation_counts: np.ndarray,
training_counts: np.ndarray,
) -> np.ndarray:
"""Return the discarded prefix before the retained chronological suffix."""
training_region_ends = total_targets - validation_counts
window_starts = training_region_ends - training_counts
if (
np.any(window_starts < 0)
or np.any(training_counts < 0)
or np.any(validation_counts < 0)
):
raise RuntimeError("chronological target partition produced negative counts")
if not np.array_equal(
window_starts + training_counts + validation_counts, total_targets
):
raise RuntimeError("chronological target window is not a final contiguous suffix")
return window_starts
def chronological_target_partition(
prepared: SequenceData,
validation_fraction: float,
max_train_targets_per_user: int,
max_validation_targets_per_user: int,
) -> TargetPartition:
"""Select a recent contiguous window with a final out-of-time BCE suffix."""
totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
validation = np.floor(totals * validation_fraction).astype(np.int64)
validation[(totals >= 2) & (validation == 0)] = 1
if max_validation_targets_per_user:
validation = np.minimum(validation, max_validation_targets_per_user)
history_training = totals - validation
training = history_training.copy()
if max_train_targets_per_user:
training = np.minimum(training, max_train_targets_per_user)
window_starts = retained_target_window_starts(totals, validation, training)
return TargetPartition(
window_starts=window_starts,
training_counts=training,
validation_counts=validation,
history_training_counts=history_training,
)
def load_organic_like_outcomes(
public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
"""Load mapped organic-like timestamps from public fitting history."""
prefixes = ("train",) if stage == "validation" else ("train", "val")
frames = [
pd.read_parquet(
public / "data" / f"{prefix}_likes.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
for prefix in prefixes
]
likes = pd.concat(frames, ignore_index=True)
likes = likes[likes.is_organic == 1].copy()
mapped = pd.Index(item_ids).get_indexer(
likes.item_id.to_numpy(dtype=np.int64)
)
likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
nested: dict[int, dict[int, list[int]]] = {}
for raw_uid, raw_timestamp, raw_item in likes.itertuples(
index=False, name=None
):
nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
int(raw_timestamp)
)
result = {
uid: {
item: np.asarray(timestamps, dtype=np.int64)
for item, timestamps in items.items()
}
for uid, items in nested.items()
}
log(
f"loaded organic-like outcomes: rows={len(likes):,} "
f"users={len(result):,}"
)
return result
def build_target_weights(
public: Path,
stage: str,
prepared: SequenceData,
training_counts: np.ndarray,
config: Config,
Process Group PGID: 1656
## Step 18 · agent · 2026-09-29T01:23:38.749Z
### run_shell_command · 2026-09-29T01:23:38.749Z
{
"description": "Read dense block and scoring in train_current_model.py",
"command": "sed -n '1035,1160p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:38.762Z
Output: def build_dense_block(
prepared: SequenceData,
target_weights: np.ndarray,
users: np.ndarray,
window_starts: np.ndarray,
process_counts: np.ndarray,
score_starts: np.ndarray,
start: int,
steps: int,
config: Config,
) -> tuple[np.ndarray, ...]:
"""Build one causal sequence block and mark the target rows to score."""
batch_size = len(users)
padding_item = len(prepared.item_ids)
input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
targets = np.zeros((batch_size, steps), dtype=np.uint16)
weights = np.ones((batch_size, steps), dtype=np.float32)
scored = np.zeros((batch_size, steps), dtype=bool)
histories = np.full(
(batch_size, steps, config.bptt_steps),
padding_item,
dtype=np.uint16,
)
processed = np.zeros(batch_size, dtype=np.int32)
lengths = prepared.lengths
for row, user_row in enumerate(users):
count = min(steps, max(int(process_counts[row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[user_row])
window_start = int(window_starts[row])
absolute_start = window_start + start
positions = slice(
offset + absolute_start,
offset + absolute_start + count,
)
target_positions = slice(
offset + absolute_start + 1,
offset + absolute_start + count + 1,
)
input_items[row, :count] = prepared.sequence_items[positions]
event_features[row, :count] = prepared.event_features[positions]
activity_features[row, :count] = prepared.activity_features[positions]
state_ids[row, :count] = prepared.state_ids[positions]
targets[row, :count] = prepared.sequence_items[target_positions]
weights[row, :count] = target_weights[target_positions]
score_from = min(count, max(int(score_starts[row]) - start, 0))
scored[row, score_from:count] = True
processed[row] = count
user_sequence = prepared.sequence_items[
offset : offset + int(lengths[user_row])
]
histories[row, :count] = history_windows(
user_sequence,
absolute_start,
count,
config.bptt_steps,
padding_item,
)
return (
input_items,
event_features,
activity_features,
state_ids,
targets,
scored,
histories,
processed,
weights,
)
def score_target_microbatches(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
sequence_vectors: torch.Tensor,
flat_states: np.ndarray,
flat_activity: np.ndarray,
positive_items: torch.Tensor,
negatives: torch.Tensor,
negative_weights: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
"""Compute one logical block's exact BCE in bounded-memory slices."""
target_count = len(positive_items)
total_weight = float(sample_weights.sum())
if total_weight <= 0:
raise RuntimeError("target block has no positive sample weight")
sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
statistics = {
"loss_sum": 0.0,
"positive_sum": 0.0,
"negative_sum": 0.0,
"pair_correct": 0.0,
"weight_sum": 0.0,
}
for begin in range(0, target_count, config.loss_microbatch_size):
end = min(begin + config.loss_microbatch_size, target_count)
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
Process Group PGID: 1659
## Step 19 · agent · 2026-09-29T01:23:41.259Z
### run_shell_command · 2026-09-29T01:23:41.259Z
{
"description": "Read rest of score_target_microbatches",
"command": "sed -n '1161,1260p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:41.274Z
Output: )
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),
Process Group PGID: 1662
## Step 20 · agent · 2026-09-29T01:23:43.039Z
### run_shell_command · 2026-09-29T01:23:43.039Z
{
"command": "sed -n '1261,1380p' /task/current_model/train_current_model.py",
"description": "Read rest of run_training_epoch and validation"
}
### Tool output · 2026-09-29T01:23:43.054Z
Output: hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0).detach()
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
"loss": total_loss / max(total_weight, 1.0e-12),
"positive_logit": total_positive / max(total_targets, 1),
"negative_logit": total_negative / max(total_targets * negative_count, 1),
"pair_accuracy": total_pair_correct
/ max(total_targets * negative_count, 1),
"targets": float(total_targets),
"target_weight_sum": total_weight,
"batches": float(batches),
"seconds": time.monotonic() - started,
}
@torch.no_grad()
def evaluate_validation_bce(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
Process Group PGID: 1665
## Step 21 · agent · 2026-09-29T01:23:46.600Z
### run_shell_command · 2026-09-29T01:23:46.600Z
{
"description": "Read inference queries and candidate ranking",
"command": "sed -n '1780,1920p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:46.618Z
Output: @torch.inference_mode()
def inference_queries(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
device: torch.device,
) -> torch.Tensor:
sequence_rows = prepared.inference_user_rows
lengths = np.where(
sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
)
ordered = np.argsort(lengths, kind="stable")
result = torch.zeros(
len(sequence_rows), config.embedding_dim, dtype=torch.float32
)
for group_start in range(0, len(ordered), config.user_batch_size):
output_rows = ordered[group_start : group_start + config.user_batch_size]
rows = sequence_rows[output_rows]
group_lengths = lengths[output_rows]
hidden = torch.zeros(
1, len(output_rows), config.embedding_dim, device=device
)
for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
steps = min(
config.bptt_steps,
int(group_lengths.max(initial=0)) - start,
)
items = np.full(
(len(output_rows), steps),
len(prepared.item_ids),
dtype=np.uint16,
)
features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
counts = np.zeros(len(output_rows), dtype=np.int32)
for local_row, sequence_row in enumerate(rows):
if sequence_row < 0:
continue
count = min(steps, max(int(group_lengths[local_row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[sequence_row])
selected = slice(offset + start, offset + start + count)
items[local_row, :count] = prepared.sequence_items[selected]
features[local_row, :count] = prepared.event_features[selected]
counts[local_row] = count
outputs, _ = event_sequence(
model,
torch.from_numpy(items.astype(np.int64)).to(device),
torch.from_numpy(features.astype(np.float32)).to(device),
hidden,
)
next_rows: list[torch.Tensor] = []
for local_row, count in enumerate(counts):
next_rows.append(
outputs[local_row, count - 1]
if count
else hidden[0, local_row]
)
hidden = torch.stack(next_rows).unsqueeze(0)
liked_items = torch.from_numpy(
prepared.inference_liked_items[output_rows].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.inference_disliked_items[output_rows].astype(np.int64)
).to(device)
queries = user_queries(
model,
hidden.squeeze(0),
model._mean_context_channel(liked_items),
model._mean_context_channel(disliked_items),
torch.from_numpy(
prepared.inference_user_features[output_rows].astype(np.float32)
).to(device),
)
result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
return result
@torch.inference_mode()
def rank_candidates(
model: base.SequentialTwoTower,
prepared: SequenceData,
eligibility: CandidateSet,
config: Config,
device: torch.device,
ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
model.eval()
queries = inference_queries(model, prepared, config, device)
item_parts: list[torch.Tensor] = []
for start in range(0, len(prepared.item_ids), 2_048):
rows = torch.arange(
start,
min(start + 2_048, len(prepared.item_ids)),
dtype=torch.long,
device=device,
)
item_parts.append(model.encode_items(rows).cpu())
items = torch.cat(item_parts)
rows: list[tuple[int, int, int]] = []
for start in range(0, len(eligibility.uids), ranking_batch_size):
end = min(start + ranking_batch_size, len(eligibility.uids))
scores = queries[start:end] @ items.T
eligible = np.unpackbits(
eligibility.packed[start:end],
axis=1,
count=len(prepared.item_ids),
bitorder="little",
).astype(bool, copy=False)
scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
for local_row, columns in enumerate(selected):
uid = int(eligibility.uids[start + local_row])
rows.extend(
(uid, int(prepared.item_ids[column]), rank)
for rank, column in enumerate(columns, start=1)
)
if end == len(eligibility.uids) or end % 1_024 == 0:
log(f"ranked users={end:,}/{len(eligibility.uids):,}")
query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
diagnostics = {
"query_norm_mean": float(query_norm.mean()),
"query_norm_std": float(query_norm.std()),
"item_norm_mean": float(item_norm.mean()),
"item_norm_std": float(item_norm.std()),
}
return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
def write_ranking(frame: pd.DataFrame, output: Path) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
if output.suffix == ".parquet":
frame.to_parquet(output, index=False, compression="zstd")
elif output.suffix == ".csv":
frame.to_csv(output, index=False)
else:
raise ValueError("--output must end in .parquet or .csv")
Process Group PGID: 1668
## Step 22 · agent · 2026-09-29T01:23:48.573Z
### run_shell_command · 2026-09-29T01:23:48.573Z
{
"description": "Read prepare_data in train_current_model.py",
"command": "sed -n '340,430p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:23:48.588Z
Output: def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
if n_items >= np.iinfo(np.uint16).max:
raise RuntimeError("uint16 item storage is too small for this catalog")
if stage == "validation":
prefixes = ("train",)
history_end = base.TRAIN_END
candidate_name = "validation_eligible_candidates.npz"
state_name = "validation_preference_state.parquet"
else:
prefixes = ("train", "val")
history_end = base.VALIDATION_END
candidate_name = "test_eligible_candidates.npz"
state_name = "test_preference_state.parquet"
eligibility = CandidateSet.read(data / candidate_name)
if not np.array_equal(eligibility.item_ids, item_ids):
raise RuntimeError("catalog and candidate artifact item order differ")
explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
inference_likes, inference_dislikes, like_counts, dislike_counts = (
base.load_inference_state(
data / state_name,
item_index,
eligibility.uids,
n_items,
)
)
inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
# Snapshot zero is the shared empty explicit state.
liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
disliked_states = [
np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
]
state_counts: list[tuple[int, int]] = [(0, 0)]
user_ids: list[int] = []
offsets = [0]
timestamp_parts: list[np.ndarray] = []
item_parts: list[np.ndarray] = []
event_parts: list[np.ndarray] = []
activity_parts: list[np.ndarray] = []
state_parts: list[np.ndarray] = []
listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
started = time.monotonic()
for seen_users, (uid, listens) in enumerate(
base.merge_listen_streams(listen_paths, item_index), start=1
):
parts = build_sequence_parts(
listens,
explicit_by_user.get(uid, base.empty_explicit_events()),
n_items,
liked_states,
disliked_states,
state_counts,
)
if parts is None:
continue
sequence_row = len(user_ids)
user_ids.append(uid)
timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
item_parts.append(parts[1])
event_parts.append(parts[2])
activity_parts.append(parts[3])
state_parts.append(parts[4])
offsets.append(offsets[-1] + len(parts[1]))
inference_row = inference_rows.get(uid)
if inference_row is not None:
inference_sequence_rows[inference_row] = sequence_row
inference_user_features[inference_row] = final_user_features(
parts[0],
parts[1],
parts[2],
history_end,
int(like_counts[inference_row]),
int(dislike_counts[inference_row]),
)
if seen_users % 1_000 == 0:
log(
Process Group PGID: 1671
## Step 23 · agent · 2026-09-29T01:23:50.655Z
### run_shell_command · 2026-09-29T01:23:50.655Z
{
"command": "find /app /task -name \"*cache*\"",
"description": "Find cache files"
}
### Tool output · 2026-09-29T01:23:50.670Z
Output: (empty)
Process Group PGID: 1674
## Step 24 · agent · 2026-09-29T01:23:54.108Z
### Recorded reasoning summary
Testing Validation Stage
I'm currently executing the `train_current_model.py` script with the `--stage validation` argument to observe its behavior and test the reproduction command provided. This step is crucial for understanding the model's performance during the validation phase.
### run_shell_command · 2026-09-29T01:23:54.108Z
{
"description": "Check data preparation runtime with --prepare-only",
"command": "python3 /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepare-only \\\n --output /app/solution/temp.parquet"
}
### Tool output · 2026-09-29T01:24:47.770Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.7s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.8s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.1s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=24.8s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.5s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=34.9s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.1s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=44.7s
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
{
"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": 49.639,
"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: 1677
## Step 25 · agent · 2026-09-29T01:24:50.153Z
### Recorded reasoning summary
Saving Prepared Data
I'm exploring how to leverage the `--prepared-cache` flag. My current focus is on understanding the `save_cache` and `load_cache` functions within `train_current_model.py` to efficiently store and retrieve preprocessed data, potentially speeding up future runs.
### run_shell_command · 2026-09-29T01:24:50.153Z
{
"description": "Check cache implementation in train_current_model.py",
"command": "sed -n '470,535p' /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T01:24:50.168Z
Output: 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,
Process Group PGID: 1703
## Step 26 · agent · 2026-09-29T01:24:58.984Z
### run_shell_command · 2026-09-29T01:24:58.984Z
{
"description": "Check disk space in /app",
"command": "df -h /app"
}
### Tool output · 2026-09-29T01:24:58.999Z
Output: Filesystem Size Used Avail Use% Mounted on
overlay 96G 14G 83G 15% /
Process Group PGID: 1706
## Step 27 · agent · 2026-09-29T01:25:02.067Z
### Recorded reasoning summary
Copying Code for Reproduction
I'm prioritizing the relocation of code from `/task/current_model/` to `/app/solution/`. This ensures I have a dedicated, working copy of the production model's source for subsequent tasks. I'll also confirm this aligns with prompt requirements.
### run_shell_command · 2026-09-29T01:25:02.067Z
{
"command": "cp /task/current_model/train_current_model.py /app/solution/ && cp /task/current_model/two_tower_model.py /app/solution/ && ls -la /app/solution/",
"description": "Copy baseline code to /app/solution/"
}
### Tool output · 2026-09-29T01:25:02.087Z
Output: total 108
drwxr-xr-x 1 agent agent 4096 Sep 29 01:25 .
drwxr-xr-x 1 agent agent 4096 Sep 29 01:19 ..
-r--r--r-- 1 agent agent 80058 Sep 29 01:25 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 01:25 two_tower_model.py
Process Group PGID: 1709
## Step 28 · agent · 2026-09-29T01:25:03.912Z
### run_shell_command · 2026-09-29T01:25:03.912Z
{
"command": "chmod +w /app/solution/*.py",
"description": "Make solution files writable"
}
### Tool output · 2026-09-29T01:25:03.928Z
Output: (empty)
Process Group PGID: 1714
## Step 29 · agent · 2026-09-29T01:25:07.951Z
### Recorded reasoning summary
Establishing Baseline Performance
I'm currently initiating the baseline model validation to set our benchmark. My immediate focus is on quantifying the training duration for a single epoch and understanding the scale of `selected_training_targets` (1,052,953). Next, I'll determine the number of user batches required for this process.
### run_shell_command · 2026-09-29T01:25:07.951Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --output /app/solution/production_validation.parquet",
"description": "Run baseline validation training and generate ranking"
}
### Tool output · 2026-09-29T01:31:12.099Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.7s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.7s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.7s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.0s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=24.7s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.3s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=34.7s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=39.9s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=44.5s
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/val_cache.npz
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=198.0s
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": 359.814,
"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": 0.5398380453021118,
"positive_logit": 0.7427799631232914,
"negative_logit": -0.6222247012934038,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 211.00342043400008,
"validation_loss": 0.4966362195892997,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.520146490000002
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497606277466,
"item_norm_mean": 1.7570281028747559,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1717
## Step 30 · agent · 2026-09-29T01:31:16.861Z
### run_shell_command · 2026-09-29T01:31:16.861Z
{
"description": "Evaluate baseline validation ranking",
"command": "python3 /task/evaluate_public.py /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T01:31:20.239Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1751
## Step 31 · agent · 2026-09-29T01:31:23.057Z
### Recorded reasoning summary
Analyzing Initial Results
I've completed the first run, observing a `quality_ndcg@10` of 0.02128 with 4263 users. The total runtime was 359 seconds, with significant portions dedicated to data prep and training. The cache is now saved, which should expedite future data preparation steps.
### run_shell_command · 2026-09-29T01:31:23.057Z
{
"description": "Test cache load time",
"command": "python3 -c '\nimport time, numpy as np\nt0 = time.time()\nwith np.load(\"/app/solution/val_cache.npz\") as a:\n print(a.files)\nprint(f\"Loaded in {time.time()-t0:.2f}s\")\n'"
}
### Tool output · 2026-09-29T01:31:25.642Z
Output: ['metadata', 'item_ids', 'item_features', 'user_ids', 'offsets', 'sequence_timestamps', 'sequence_items', 'event_features', 'activity_features', 'state_ids', 'liked_states', 'disliked_states', 'state_counts', 'inference_user_rows', 'inference_liked_items', 'inference_disliked_items', 'inference_user_features']
Loaded in 0.06s
Process Group PGID: 1771
## Step 32 · agent · 2026-09-29T01:31:28.685Z
### Recorded reasoning summary
Generating Initial Submission
I'm focusing on creating that initial valid submission file `/app/solution/submission.parquet` as instructed. My next step is to confirm if a baseline submission can be generated at this stage, or if I need to wait for further prompt information before proceeding.
### run_shell_command · 2026-09-29T01:31:28.685Z
{
"description": "Read ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
}
### Tool output · 2026-09-29T01:31:28.702Z
Output: """Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
Process Group PGID: 1781
## Step 33 · agent · 2026-09-29T01:31:32.371Z
### Recorded reasoning summary
Testing Submission Stage
I'm focusing on refining the `--stage submission` functionality in `train_current_model.py` to generate the test ranking. My immediate goal is to explore the `--prepare-only` flag to optimize test data caching and ensure the best valid submission is preserved throughout the process.
### run_shell_command · 2026-09-29T01:31:32.371Z
{
"description": "Prepare and cache submission stage data",
"command": "python3 /app/solution/train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepare-only \\\n --prepared-cache /app/solution/test_cache.npz \\\n --output /app/solution/temp.parquet"
}
### Tool output · 2026-09-29T01:32:28.503Z
Output: loaded explicit events: rows=746,504 users=8,307
prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.0s
prepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.2s
prepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.4s
prepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.0s
prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=25.9s
prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=30.8s
prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=36.4s
prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=41.9s
prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=46.7s
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/test_cache.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": 52.056,
"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: 1784
## Step 34 · agent · 2026-09-29T01:32:31.550Z
### Recorded reasoning summary
Building and Saving Caches
I've successfully built and saved both `val_cache.npz` and `test_cache.npz` in `/app/solution/`. My next step is to generate a baseline submission, which will serve as a fallback `submission.parquet`. I'll be running the submission stage using the existing baseline script with the `--prepared-cache` argument.
### run_shell_command · 2026-09-29T01:32:31.550Z
{
"command": "python3 /app/solution/train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepared-cache /app/solution/test_cache.npz \\\n --output /app/solution/submission.parquet",
"description": "Generate initial baseline submission.parquet"
}
### Tool output · 2026-09-29T01:37:51.836Z
Output: loaded full-sequence cache: tokens=20,712,971 targets=20,703,818
chronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.576 max=1.000
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=198.2s
epoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=213.1 validation_seconds=16.2
restored best validation-BCE checkpoint: epoch=1 loss=0.497949
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": "submission",
"output": "/app/solution/submission.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 315.923,
"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": 20712971,
"all_next_item_targets": 20703818,
"selected_training_targets": 1067606,
"selected_validation_targets": 131997,
"discarded_old_targets": 19504215,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5383531209256304,
"positive_logit": 0.7500170028812898,
"negative_logit": -0.6300986778730688,
"pair_accuracy": 0.7552423658634365,
"targets": 1067606.0,
"target_weight_sum": 641383.25,
"batches": 270.0,
"seconds": 213.07477701900007,
"validation_loss": 0.4979487119638799,
"validation_targets": 131997.0,
"validation_target_weight_sum": 80328.75,
"validation_seconds": 16.163106934000098
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.6518515348434448,
"query_norm_std": 0.8145266771316528,
"item_norm_mean": 1.9033515453338623,
"item_norm_std": 0.9598206877708435
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1808
## Step 35 · agent · 2026-09-29T01:37:57.601Z
### run_shell_command · 2026-09-29T01:37:57.601Z
{
"command": "python3 -c '\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(\"/task/data/test_eligible_candidates.npz\")\n\nranking = pd.read_parquet(\"/app/solution/submission.parquet\")\nprint(\"Validating ranking structure...\")\nvalidated = validate_ranking(ranking, targets)\nprint(f\"Validated rows: {len(validated)}\")\n\nprint(\"Checking candidate eligibility...\")\nall_eligible = all(\n eligibility.is_eligible(uid, item)\n for uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n)\nprint(f\"All candidates eligible: {all_eligible}\")\n'",
"description": "Verify submission.parquet format and candidate eligibility"
}
### Tool output · 2026-09-29T01:37:59.097Z
Output: Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1833
## Step 36 · agent · 2026-09-29T01:38:03.948Z
### run_shell_command · 2026-09-29T01:38:03.948Z
{
"description": "Verify submission.parquet format with PYTHONPATH=/task",
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(\"/task/data/test_eligible_candidates.npz\")\n\nranking = pd.read_parquet(\"/app/solution/submission.parquet\")\nprint(\"Validating ranking structure...\")\nvalidated = validate_ranking(ranking, targets)\nprint(f\"Validated rows: {len(validated)}\")\n\nprint(\"Checking candidate eligibility...\")\nall_eligible = all(\n eligibility.is_eligible(uid, item)\n for uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n)\nprint(f\"All candidates eligible: {all_eligible}\")\n'"
}
### Tool output · 2026-09-29T01:38:06.575Z
Output: Validating ranking structure...
Validated rows: 845600
Checking candidate eligibility...
All candidates eligible: True
Process Group PGID: 1844
## Step 37 · agent · 2026-09-29T01:38:10.213Z
### Recorded reasoning summary
Confirming Submission Status
My current thought process confirms we've successfully passed all necessary checks for submission. Furthermore, the caching files are prepared, and our initial baseline NDCG@10 score is at 0.0.
### update_topic · 2026-09-29T01:38:10.213Z
{
"title": "Diagnosis of Negative Sampling & Objective",
"strategic_intent": "Diagnose negative sampling, loss function, and training objective limitations.",
"summary": "Successfully ran baseline training and evaluation. Baseline validation NDCG@10 is 0.02128. Generated a verified baseline submission.parquet and prepared cache files val_cache.npz and test_cache.npz. Now initiating detailed technical investigation into negative sampling, negative weights, loss function, and training objective under the two-tower serving constraints."
}
### Tool output · 2026-09-29T01:38:10.217Z
Current topic: "Diagnosis of Negative Sampling & Objective"
Topic summary: Successfully ran baseline training and evaluation. Baseline validation NDCG@10 is 0.02128. Generated a verified baseline submission.parquet and prepared cache files val_cache.npz and test_cache.npz. Now initiating detailed technical investigation into negative sampling, negative weights, loss function, and training objective under the two-tower serving constraints.
Strategic Intent: Diagnose negative sampling, loss function, and training objective limitations.
## Step 38 · agent · 2026-09-29T01:38:18.874Z
### Recorded reasoning summary
Investigating Objective Function
I'm currently scrutinizing how the trainer, a mature baseline with chronological internal validation, selects and utilizes negative examples within its training objective. My focus is on pinpointing any inherent limitations in this process.
### Recorded reasoning summary
Refining Negative Sampling
I'm diving deep into the current negative sampling methodology, particularly examining `draw_training_negatives` and its interaction with `draw_full_history_filtered_negatives`. My goal is to pinpoint inefficiencies and explore improvements within our CPU constraints. I'm also scrutinizing in-batch negative selection and the computation of negative weights.
### Recorded reasoning summary
Analyzing Loss Components
I'm currently dissecting the divergence between positive and negative loss calculations. Specifically, I'm comparing the implications of `F.softplus(-positive_logits)` versus the outcome of `corrected_negative_loss` using raw logits, to understand their impact.
### Recorded reasoning summary
Questioning Loss Calculation
I'm re-examining the loss function's structure. It appears to be a hybrid, possibly a sampled softmax or InfoNCE, rather than standard binary cross-entropy. I need to clarify how the positive and negative components are combined.
### run_shell_command · 2026-09-29T01:38:18.874Z
{
"description": "Read draw_full_history_filtered_negatives",
"command": "sed -n '630,725p' /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:38:18.892Z
Output: 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,
Process Group PGID: 1864
## Step 39 · agent · 2026-09-29T01:38:24.946Z
### Recorded reasoning summary
Analyzing Training Dynamics
I'm currently dissecting the `draw_training_negatives` function to understand its role within the `run_training_epoch` loop. My focus is on how it's invoked and its specific contribution to the epoch's progression, particularly concerning `bptt_steps`.
### Recorded reasoning summary
Investigating Target Sources
I'm tracing the `targets` variable within `draw_full_history_filtered_negatives`, and it appears to originate from `positive_items` in the preceding microbatch processing. I'm now examining the size of these `positive_items`.
### run_shell_command · 2026-09-29T01:38:24.946Z
{
"command": "sed -n '810,840p' /app/solution/train_current_model.py",
"description": "Read candidate_vectors in train_current_model.py"
}
### Tool output · 2026-09-29T01:38:24.963Z
Output: )
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)
Process Group PGID: 1867
## Step 40 · agent · 2026-09-29T01:38:38.585Z
### Recorded reasoning summary
Analyzing Core Components
I am now meticulously dissecting the loss function, prompt, and problem description. My current focus is on re-evaluating the prompt, particularly the stringent request-time latency budget and the pre-computed nature of item vectors.
### Recorded reasoning summary
Investigating Negative Sampling
I'm digging into how the training objective handles negative examples. My current task is to pinpoint weaknesses in the existing negative sampling approach and then devise improvements, all while adhering to the CPU constraints and validating with the public backtest.
### Recorded reasoning summary
Examining Negative Sampling Details
I'm deep-diving into the specifics of negative selection. Uniform sampling appears to over-represent long-tail items due to the power-law distribution of real listening habits, while in-batch negatives are sampled based on their frequency within the current batch's targets.
### Recorded reasoning summary
Refining Sampling Strategies
I'm refining the negative sampling approach based on the observed power-law distribution. The goal is to create a more effective strategy than uniform or in-batch sampling by considering actual listening habits while remaining within CPU constraints.
### Recorded reasoning summary
Adjusting Correction Logic
I've discovered that the current implementation incorrectly reduces the `correction` factor for popular items. This is counterintuitive, and I need to understand why this logic is applied and how it impacts training.
### Recorded reasoning summary
Revising Correction Logic
I'm re-examining the weighted average calculation for in-batch negatives. It appears the `correction` factor, intended for propensity, is being used as importance weights, which contradicts the goal of simulating uniform sampling of in-batch items.
### Recorded reasoning summary
Correcting Negative Sampling Weights
I'm identifying that the `correction` factor is unexpectedly downweighting popular items among negatives, akin to treating them as uniform negatives. This logic needs revision as it contradicts the purpose of in-batch negatives, which is to distinguish between popular items.
### run_shell_command · 2026-09-29T01:38:38.585Z
{
"command": "grep -n \"dislike\" /app/solution/two_tower_model.py /app/solution/train_current_model.py",
"description": "Search for dislike usage in the codebase"
}
### Tool output · 2026-09-29T01:38:38.602Z
Output: /app/solution/two_tower_model.py:184: ("dislikes", 1, 1, 0),
/app/solution/two_tower_model.py:185: ("undislikes", 1, 0, 1),
/app/solution/two_tower_model.py:265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
/app/solution/two_tower_model.py:267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
/app/solution/two_tower_model.py:270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
/app/solution/two_tower_model.py:281: return liked, disliked, like_counts, dislike_counts
/app/solution/train_current_model.py:110: disliked_states: np.ndarray
/app/solution/train_current_model.py:114: inference_disliked_items: np.ndarray
/app/solution/train_current_model.py:176: active_dislikes: dict[int, int],
/app/solution/train_current_model.py:179: disliked_states: list[np.ndarray],
/app/solution/train_current_model.py:183: disliked_states.append(
/app/solution/train_current_model.py:184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
/app/solution/train_current_model.py:186: state_counts.append((len(active_likes), len(active_dislikes)))
/app/solution/train_current_model.py:252: disliked_states: list[np.ndarray],
/app/solution/train_current_model.py:279: active_dislikes: dict[int, int] = {}
/app/solution/train_current_model.py:293: else active_dislikes
/app/solution/train_current_model.py:304: active_dislikes,
/app/solution/train_current_model.py:307: disliked_states,
/app/solution/train_current_model.py:320: dislike_count: int,
/app/solution/train_current_model.py:337: result[5] = np.log1p(dislike_count) / 4.0
/app/solution/train_current_model.py:369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
/app/solution/train_current_model.py:383: disliked_states = [
/app/solution/train_current_model.py:405: disliked_states,
/app/solution/train_current_model.py:427: int(dislike_counts[inference_row]),
/app/solution/train_current_model.py:440: np.log1p(dislike_counts[missing]) / 4.0
/app/solution/train_current_model.py:455: disliked_states=np.stack(disliked_states),
/app/solution/train_current_model.py:459: inference_disliked_items=inference_dislikes,
/app/solution/train_current_model.py:784: disliked_items = torch.from_numpy(
/app/solution/train_current_model.py:785: prepared.disliked_states[unique_states].astype(np.int64)
/app/solution/train_current_model.py:788: disliked_vectors = model._mean_context_channel(disliked_items)
/app/solution/train_current_model.py:793: disliked_vectors[inverse_rows],
/app/solution/train_current_model.py:802: disliked_vectors: torch.Tensor,
/app/solution/train_current_model.py:807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
/app/solution/train_current_model.py:1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
/app/solution/train_current_model.py:1159: disliked_vectors,
/app/solution/train_current_model.py:1844: disliked_items = torch.from_numpy(
/app/solution/train_current_model.py:1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
/app/solution/train_current_model.py:1851: model._mean_context_channel(disliked_items),
Process Group PGID: 1870
## Step 41 · agent · 2026-09-29T01:39:17.706Z
### Recorded reasoning summary
Clarifying Dislike Usage
I'm currently reviewing how dislikes are incorporated. My understanding is they solely function as an input feature for the user tower, specifically as `disliked_vectors`, and not as negative targets. I'm now investigating the `train_listens.parquet` file to understand the implications of listens with low play ratios or skipped tracks on this.
### run_shell_command · 2026-09-29T01:39:17.706Z
{
"command": "sed -n '200,315p' /app/solution/train_current_model.py",
"description": "Read build_sequence_parts"
}
### Tool output · 2026-09-29T01:39:17.721Z
Output: return result
left_rows = np.searchsorted(
timestamps,
timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
rows = np.arange(length, dtype=np.int64)
counts = rows - left_rows + 1
organic_sum = np.cumsum(organic.astype(np.float64))
completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
before_organic = np.where(
left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
)
before_completion = np.where(
left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
)
unique_counts = np.empty(length, dtype=np.int32)
frequencies: dict[int, int] = {}
left = 0
for right, raw_item in enumerate(items):
item = int(raw_item)
frequencies[item] = frequencies.get(item, 0) + 1
required_left = int(left_rows[right])
while left < required_left:
old_item = int(items[left])
remaining = frequencies[old_item] - 1
if remaining:
frequencies[old_item] = remaining
else:
del frequencies[old_item]
left += 1
unique_counts[right] = len(frequencies)
result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
result[:, 2] = (
(organic_sum - before_organic) / np.maximum(counts, 1)
).astype(np.float16)
result[:, 3] = (
(completion_sum - before_completion)
/ np.maximum(counts, 1)
/ 100.0
).astype(np.float16)
return result
def build_sequence_parts(
listens: base.ListenEvents,
explicit: base.ExplicitEvents,
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
items = listens.items[qualifying].astype(np.uint16, copy=False)
organic = listens.organic[qualifying].astype(np.float32, copy=False)
completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
if not len(items):
return None
event_features = np.zeros((len(items), 3), dtype=np.float16)
event_features[:, 0] = organic.astype(np.float16)
event_features[:, 1] = (
np.minimum(completion, 100.0) / 100.0
).astype(np.float16)
if len(items) > 1:
gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
event_features[1:, 2] = np.exp(
-gaps.astype(np.float64) / (60.0 * base.DAY)
).astype(np.float16)
activity = causal_activity_features(
timestamps, items, organic, completion
)
states = np.zeros(len(items), dtype=np.uint32)
active_likes: dict[int, int] = {}
active_dislikes: dict[int, int] = {}
explicit_position = 0
current_state = 0
for row, timestamp in enumerate(timestamps):
changed = False
while (
explicit_position < len(explicit.timestamps)
and int(explicit.timestamps[explicit_position]) < int(timestamp)
):
item = int(explicit.items[explicit_position])
event_timestamp = int(explicit.timestamps[explicit_position])
destination = (
active_likes
if int(explicit.channel[explicit_position]) == 0
else active_dislikes
)
if int(explicit.active[explicit_position]) == 1:
destination[item] = event_timestamp
else:
destination.pop(item, None)
explicit_position += 1
changed = True
if changed:
current_state = append_state_snapshot(
active_likes,
active_dislikes,
n_items,
liked_states,
disliked_states,
state_counts,
)
states[row] = np.uint32(current_state)
return timestamps, items.copy(), event_features, activity, states
def final_user_features(
timestamps: np.ndarray,
Process Group PGID: 1873
## Step 42 · agent · 2026-09-29T01:39:42.914Z
### Recorded reasoning summary
Refining Sequence Logic
I'm currently examining the `qualifying` condition, specifically how it ties into the construction of sequences from positive listens. My focus is on ensuring this aligns precisely with the stated problem description's requirements.
### Recorded reasoning summary
Optimizing Negative Sampling
I'm now concentrating on the training objective, particularly how it handles negative examples. My aim is to diagnose and enhance the current negative-sampling strategy to improve performance within the existing CPU constraints, while ensuring the backtest validation remains robust.
### Recorded reasoning summary
Analyzing Negative Sampling Strategy
I'm now delving into how the training objective utilizes negative examples. My immediate focus is on diagnosing limitations within the current negative-sampling approach and devising improvements, all while operating within the established CPU budget.
### Recorded reasoning summary
Investigating Loss Function
I'm scrutinizing the loss summation component, specifically the averaging of positive and negative losses. This has raised questions about its equivalence to a standard Binary Cross-Entropy formulation, prompting a deeper mathematical analysis of its implications.
### Recorded reasoning summary
Evaluating Loss Function Choice
I'm questioning the use of BCE instead of InfoNCE or sampled softmax for this recommendation retrieval task. I'm analyzing how these different loss functions fundamentally optimize the ranking of positive items against negatives, particularly in the context of sampled softmax with logQ correction.
### Recorded reasoning summary
Challenging Loss Function
I'm realizing Binary Cross-Entropy doesn't inherently rank items against each other, unlike InfoNCE or sampled softmax. This seems crucial for retrieval. I'm also noting the absence of normalization or temperature scaling in the current setup.
### Recorded reasoning summary
Revising Objective Function
I'm now reassessing the core objective function. The current implementation uses a sample-weighted BCE, which doesn't inherently encourage ranking. I'm exploring alternatives like InfoNCE or sampled softmax to better align with the retrieval task's ranking requirements, and considering the implications of normalization and temperature scaling.
### Recorded reasoning summary
Sampling Negatives Unfairly
I'm realizing the uniform negative sampling is heavily biased towards unpopular tracks. This is because the `candidate_catalog` is ordered by popularity, and drawing uniformly from the entire range means very few popular tracks are ever chosen as negatives.
### Recorded reasoning summary
Identifying Trivial Negatives
I'm now seeing that the negatives being sampled are too easy. I'm investigating the in-batch negative sampling mechanism to understand how it's drawing examples and whether this contributes to the issue.
### Recorded reasoning summary
Questioning Sampling Logic
I'm realizing the negative sampling, as implemented, treats all unique items in the batch equally, regardless of their actual frequency. This means rare items have the same chance of being sampled as extremely popular ones, which seems problematic.
### Recorded reasoning summary
Correcting Item Sampling Weights
I'm now revising the in-batch negative sampling correction. It appears the current weighting was based on an incorrect assumption about how items are sampled, specifically assuming a uniform draw from target frequencies which isn't the case for in-batch negatives.
### Recorded reasoning summary
Adjusting Sampling Probabilities
I'm realizing the in-batch negative sampling is fundamentally flawed. Instead of sampling according to item popularity, it samples uniformly from unique targets. This, coupled with a weight correction that inversely favors popular items, means the model rarely receives informative negative signals on those crucial tracks.
### Recorded reasoning summary
Inspecting Top Predictions
I'm now investigating the model's actual output to validate my hypothesis. My focus is on the top 10 predicted items in the `production_validation.parquet` file. I'm setting up a Python snippet to examine these predictions and confirm if popular items are indeed being over-ranked due to the flawed negative sampling.
### run_shell_command · 2026-09-29T01:39:42.914Z
{
"command": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/app/solution/production_validation.parquet\")\nprint(\"Top 10 ranked items overall (by frequency in top 10 ranks):\")\ntop10 = df[df[\"rank\"] <= 10]\nitem_counts = top10[\"item_id\"].value_counts()\nprint(item_counts.head(20))\nprint(\"Total unique items appearing in top 10:\", top10[\"item_id\"].nunique())\nprint(\"Total unique items appearing in all 100 ranks:\", df[\"item_id\"].nunique())\n'",
"description": "Analyze top ranked items in baseline validation ranking"
}
### Tool output · 2026-09-29T01:39:44.498Z
Output: Top 10 ranked items overall (by frequency in top 10 ranks):
item_id
6006390 2917
8213481 2800
6896222 2712
8743390 2653
5436843 2587
2651314 2580
8722394 2542
3542184 2427
5862961 2292
906358 2066
5927996 1872
9378983 1537
2185191 1522
6901374 1463
3033749 1333
5252571 1289
2408923 1215
4423556 1148
8351511 1121
3375935 1107
Name: count, dtype: int64
Total unique items appearing in top 10: 592
Total unique items appearing in all 100 ranks: 2575
Process Group PGID: 1876
## Step 43 · agent · 2026-09-29T01:39:48.324Z
### Recorded reasoning summary
Discovering High-Performing Items
I'm thrilled to see item `6006390` making it into the top 10 for a significant portion of users, over 34%. Item `8213481` is also performing exceptionally well, appearing in the top 10 for 2,800 users. This suggests strong user engagement with these items.
### run_shell_command · 2026-09-29T01:39:48.324Z
{
"command": "python3 -c '\nimport pandas as pd\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(cat[cat[\"item_id\"].isin([6006390, 8213481, 6896222, 8743390, 5436843])][[\"item_id\", \"popularity_rank\", \"positive_listens\", \"organic_positive_listens\"]])\n'",
"description": "Check catalog statistics for top ranked items"
}
### Tool output · 2026-09-29T01:39:49.884Z
Output: item_id popularity_rank positive_listens organic_positive_listens
6 8213481 7 22505 17071
13 5436843 14 14897 12127
21 8743390 22 13518 8480
74 6006390 75 8970 6813
817 6896222 818 3020 2017
Process Group PGID: 1894
## Step 44 · agent · 2026-09-29T01:39:53.424Z
### Recorded reasoning summary
Validating Baseline Rankings
I'm now digging into the actual positive labels within `val_listens.parquet` and `val_likes.parquet`. My focus is on understanding the ground truth for validation to accurately assess the baseline model's performance, which currently seems to be prioritizing globally popular items.
### run_shell_command · 2026-09-29T01:39:53.424Z
{
"description": "Analyze validation ground truth popularity distribution",
"command": "python3 -c '\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(\"/task/data/validation_eligible_candidates.npz\")\nlistens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nlikes = pd.read_parquet(\"/task/data/val_likes.parquet\")\nstrong = listens[listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)][[\"uid\", \"item_id\"]].drop_duplicates()\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nstrong[\"pop_rank\"] = strong[\"item_id\"].map(cat[\"popularity_rank\"])\nprint(\"Pop rank stats of validation strong listens:\")\nprint(strong[\"pop_rank\"].describe())\nprint(\"\\nUnique items in validation strong listens:\", strong[\"item_id\"].nunique())\n'"
}
### Tool output · 2026-09-29T01:39:54.925Z
Output: Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1911
## Step 45 · agent · 2026-09-29T01:39:57.931Z
### run_shell_command · 2026-09-29T01:39:57.931Z
{
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(\"/task/data/validation_eligible_candidates.npz\")\nlistens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nlikes = pd.read_parquet(\"/task/data/val_likes.parquet\")\nstrong = listens[listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)][[\"uid\", \"item_id\"]].drop_duplicates()\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nstrong[\"pop_rank\"] = strong[\"item_id\"].map(cat[\"popularity_rank\"])\nprint(\"Pop rank stats of validation strong listens:\")\nprint(strong[\"pop_rank\"].describe())\nprint(\"\\nUnique items in validation strong listens:\", strong[\"item_id\"].nunique())\n'",
"description": "Analyze validation ground truth popularity distribution"
}
### Tool output · 2026-09-29T01:39:59.788Z
Output: Pop rank stats of validation strong listens:
count 170791.000000
mean 7204.430860
std 7799.615827
min 1.000000
25% 996.000000
50% 4079.000000
75% 11258.000000
max 30000.000000
Name: pop_rank, dtype: float64
Unique items in validation strong listens: 86668
Process Group PGID: 1922
## Step 46 · agent · 2026-09-29T01:40:10.100Z
### Recorded reasoning summary
Analyzing Popularity Data
I'm currently diving into the median popularity rank of relevant validation items, which stands at a notable 4,079. Seventy-five percent of these items fall within the top 11,258 ranks, and I've observed an impressive 86,668 items being listened to. My next step is to examine the top 100 items from the catalog.
### Recorded reasoning summary
Investigating Negatives
I'm now focusing on the crucial aspect of how negative examples are selected and utilized by the training objective. My next steps involve diagnosing current limitations within the negative-sampling strategy and exploring potential improvements.
### Recorded reasoning summary
Refining Negative Sampling
I'm examining the specifics of how uniform and in-batch negative examples are currently selected within the training objective. My focus is on the uniform sampling mechanism and its implementation, which I'll then compare to the in-batch logic.
### Recorded reasoning summary
Evaluating Negative Sampling Logic
I'm currently scrutinizing the PyTorch code responsible for generating negative samples, specifically how `invalid_full_history_negatives` functions when it encounters an invalid entry. My attention is also drawn to the distribution of `uniform_negatives` versus `inbatch_negatives`.
### Recorded reasoning summary
Clarifying Negative Sample Rejection
I'm examining how rejected in-batch negatives are replaced with uniform random draws and how `invalid_full_history_negatives` filters items from the user's interaction history, especially considering the `negative_filter_scope` configuration.
### run_shell_command · 2026-09-29T01:40:10.100Z
{
"command": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/task/data/train_dislikes.parquet\")\nprint(f\"Total dislikes: {len(df)}\")\nprint(f\"Users with dislikes: {df.uid.nunique()}\")\nprint(f\"Dislikes per user mean: {len(df)/df.uid.nunique():.1f}\")\n'",
"description": "Analyze train_dislikes.parquet statistics"
}
### Tool output · 2026-09-29T01:40:13.728Z
Output: Total dislikes: 101075
Users with dislikes: 5732
Dislikes per user mean: 17.6
Process Group PGID: 1942
## Step 47 · agent · 2026-09-29T01:41:12.701Z
### Recorded reasoning summary
Analyzing Listen Completions
I'm currently investigating listen completion rates. My initial analysis of dislikes shows a significant number of events, but I'm shifting focus to understand how many listens are actually completed, specifically looking at those with a `played_ratio_pct` under 50% in the `train_listens.parquet` dataset.
### Recorded reasoning summary
Investigating Negative Selection
I'm refining my understanding of the training objective's negative example handling. My current focus is on precisely how these negatives are chosen—whether through uniform, in-batch, or popularity-based methods—and then how the objective leverages them in training. I am also exploring the nuances of sampling strategies like replacement versus unique targets.
### Recorded reasoning summary
Examining Loss Functions
I'm now delving into the specifics of the loss function and how negative examples are incorporated. My current focus is on the `corrected_negative_loss` in `train_current_model.py`, specifically analyzing the implementation of Softplus and the weighting strategies for uniform and in-batch negatives.
### Recorded reasoning summary
Analyzing Loss Calculations
I've been scrutinizing the loss calculations, specifically questioning the division by K (number of negatives) in the `corrected_negative_loss` function. The expected BCE loss for one positive and K negatives should be a sum, not a mean. I'm re-evaluating if this division is intended or an oversight.
### Recorded reasoning summary
Revising Loss Calculation
I'm re-evaluating the loss function, specifically the division by K. My current assessment is that the unbiased estimate requires scaling by N/K, not 1/K, to properly account for the number of sampled negatives relative to the total pool. This likely impacts how the model learns from positive and negative pairs.
### Recorded reasoning summary
Questioning Model Performance
I'm critically examining the model's actual performance, realizing the low `pair_accuracy` of 0.755 implies it struggles to even rank a positive item above a single random negative. This explains the extremely poor NDCG@10 score of 0.02.
### Recorded reasoning summary
Investigating Low Pair Accuracy
I'm exploring why `pair_accuracy` is so low. It appears Binary Cross-Entropy treats each negative independently. Even when a negative item scores higher than a positive, its minimal impact on the loss, due to division by the number of negatives (K), means the model isn't sufficiently penalized or incentivized to rank positives higher.
### Recorded reasoning summary
Analyzing Softmax Formulation
I'm revisiting the Softmax cross-entropy loss formulation. It seems the key is how negative scores relative to the positive score are exponentiated and summed, directly impacting the probability assigned to the positive item. My previous thoughts about simple averaging were likely incomplete.
### Recorded reasoning summary
Refining Softmax Temperature
I'm now re-examining the Softmax formulation, specifically the impact of temperature ($\tau$). I realize low temperatures amplify score differences, pushing the positive's score significantly higher than all negatives, which is critical for sharp gradients and high confidence in ranking. I'm checking if inference uses raw dot products.
### Recorded reasoning summary
Adjusting Softmax Temperature
I'm now focused on how the Softmax temperature ($\tau$) influences the inference stage. My analysis confirms that dividing scores by $\tau$ during training and using raw dot products at inference is functionally equivalent for ranking, as the rank order remains unchanged under monotonic transformations.
### Recorded reasoning summary
Investigating Negative Sampling
I'm focusing on the specifics of how negative examples are sampled. My current understanding is that we're using a combination of uniform and in-batch negatives, with a total of 32 negatives per positive example. My next step is to delve into how these uniform negatives are drawn from the full dataset.
### Recorded reasoning summary
Revising In-Batch Sampling
I'm re-examining the in-batch negative sampling. It appears `unique_targets` are sampled uniformly rather than by frequency. Additionally, I need to understand the role of `item_correction` and how it relates to propensities derived from training targets.
### Recorded reasoning summary
Investigating Item Correction
I'm tracking the origin of `item_correction` and have identified it in the Yi et al. (2019) paper on sampling-bias-corrected recommendations. This work details using Softmax cross-entropy with $s_j^c = s(u, j) - \log p_j$, suggesting the sampling probability $p_j$ is crucial.
### Recorded reasoning summary
Correcting BCE Bias
I'm realizing the original author's BCE implementation used a weighting scheme ($w_j = (1/N / p_j)^{0.5}$) instead of the Softmax formulation that directly subtracts $\log p_j$. This weighting, when combined with division by the sum of weights, attempts to correct for popularity bias but appears to be a different approach than the cited paper.
### Recorded reasoning summary
Testing Loss Weighting
I'm now questioning the impact of popularity weighting in the loss function, noting rare items receive disproportionately high weight. I plan to systematically test alternative loss formulations like InfoNCE and Sampled Softmax, and to evaluate their effectiveness on validation data through rapid experimentation.
### Recorded reasoning summary
Analyzing Ranking Latency
I'm investigating the 80-second runtime for ranking generation. My current focus is on the `rank_candidates` function, specifically the item encoding loop which uses `torch.cat` and the subsequent batch matrix multiplication between queries and item embeddings. I need to understand if these operations are bottlenecks.
### Recorded reasoning summary
Exploring Ranking Performance
I'm digging into the ranking generation's runtime, specifically the `rank_candidates` function and its loop over users. I plan to test larger `ranking_batch_size` values to see if that speeds things up, and I'll also investigate the time spent per batch in `run_training_epoch` to identify bottlenecks. I believe we have enough time for several experiments.
### Recorded reasoning summary
Analyzing Negative Sampling Ratios
I'm re-evaluating the current negative sampling strategy. The 24 uniform negatives are proving too easy to distinguish from positives due to the catalog's long-tail distribution. I'm considering experiments with increased in-batch negatives, popularity-biased sampling, or entirely shared in-batch negatives to improve learning.
### Recorded reasoning summary
Analyzing Item Encoding Bottlenecks
I'm focused on the significant CPU time spent encoding items. With each microbatch processing roughly 15,000-25,000 unique items, predominantly from the long tail, the item tower's MLP is a clear bottleneck. I need to explore strategies to reduce the number of items encoded per batch.
### Recorded reasoning summary
Correcting In-Batch Propensities
I'm now dissecting the `corrected_negative_loss` function, specifically how the `item_correction` term, derived from sampling probabilities ($p_j^{0.5}$), is applied. The code shows it's used to weight and normalize the loss contribution from in-batch negatives, aiming to mitigate popularity bias.
### Recorded reasoning summary
Adjusting Loss Weights
I'm identifying a critical issue where popular items are receiving an extremely suppressed loss weight (0.25), while rare items get a disproportionately high weight (4.0). This prevents the model from being penalized for false positives on popular items. I will test setting `inbatch_correction_power` to 0.0 to apply a full negative gradient to popular items.
### Recorded reasoning summary
Testing In-Batch Sampling Strategy
I'm realizing the current in-batch negative sampling method draws uniformly from unique target items, not proportionally to their frequency. I'm now investigating if sampling directly from the full `targets` list, which reflects item popularity, will provide more valuable negative signals for the model.
### Recorded reasoning summary
Analyzing Loss Gradient Coupling
I'm now deeply analyzing the gradient dynamics of BCE versus InfoNCE/Softmax. My key insight is that BCE's independent gradients for positives and negatives create a critical flaw: even large negative scores relative to positives have their impact heavily diluted. This lack of coupling is precisely why the model struggles with accurate ranking.
### Recorded reasoning summary
Analyzing Gradient Amplification
My latest thoughts confirm that Softmax-style losses, with their inherent coupling and temperature scaling, provide significantly stronger gradients than BCE. This amplification is crucial for effectively pushing positive scores above negatives, which is precisely where our current model falters. I am now verifying that validation checkpoint selection doesn't inadvertently penalize these more aggressive gradient signals.
### Recorded reasoning summary
Revising Negative Sampling Ratios
I'm re-evaluating the current negative sampling strategy. The 24 uniform negatives are proving too easy to distinguish from positives due to the catalog's long-tail distribution. I'm considering experiments with increased in-batch negatives, popularity-biased sampling, or entirely shared in-batch negatives to improve learning.
### Recorded reasoning summary
Testing Loss Weighting Scheme
I'm now focusing on the impact of the `inbatch_correction_power` parameter. My current assessment is that setting it to 0.0 will effectively negate the popularity-based weighting for in-batch negatives, making them all equally weighted. I'm proposing this as a specific experiment to isolate its effect on model learning and performance.
### run_shell_command · 2026-09-29T01:41:12.701Z
{
"description": "Check if correction args are in train_current_model.py",
"command": "grep \"correction\" /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:41:12.718Z
Output: inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
def build_item_correction(
config.inbatch_correction_power,
correction = np.clip(
config.inbatch_correction_min,
config.inbatch_correction_max,
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
return torch.from_numpy(correction)
item_correction: torch.Tensor,
correction = np.ones_like(negatives, dtype=np.float32)
correction[
] = item_correction[inbatch[:, :corrected_count]].numpy()
torch.from_numpy(correction).to(device),
correction[invalid] = 1.0
torch.from_numpy(correction).to(device),
item_correction: torch.Tensor,
item_correction,
item_correction,
correction: torch.Tensor,
weights = correction[:, selected]
item_correction: torch.Tensor,
item_correction,
item_correction: torch.Tensor,
item_correction,
item_correction = build_item_correction(
item_correction,
item_correction,
item_correction,
Process Group PGID: 1959
## Step 48 · agent · 2026-09-29T01:41:16.147Z
### Recorded reasoning summary
Adding CLI Arguments
I'm focusing on integrating `inbatch_correction_power` as a CLI argument. My next step is to examine `train_current_model.py` to determine the best approach, whether it's adding a new argument or modifying the existing `Config`.
### run_shell_command · 2026-09-29T01:41:16.147Z
{
"command": "sed -n '955,1030p' /app/solution/train_current_model.py",
"description": "Read build_target_weights in train_current_model.py"
}
### Tool output · 2026-09-29T01:41:16.163Z
Output: 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}"
Process Group PGID: 1962
## Step 49 · agent · 2026-09-29T01:41:36.277Z
### Recorded reasoning summary
Analyzing Discovery Graded Weights
I'm currently examining the `build_target_weights` function, specifically the logic for `discovery-graded` target weighting. My focus is on how first-encounter novel items with organic likes are assigned a weight of 3.0.
### Recorded reasoning summary
Investigating Negative Sampling
I've shifted focus to negative sampling. The `uniform_negatives` and `inbatch_negatives` parameters in `train_current_model.py` are key. I'm exploring how `draw_full_history_filtered_negatives` selects and applies these, considering its impact on the training objective.
### Recorded reasoning summary
Refining Negative Sampling Strategies
I'm now concentrating on the `draw_full_history_filtered_negatives` function. I'm specifically analyzing how it generates uniform negatives and its logic for creating in-batch negatives, particularly when `corrected_count` is determined by `unique_targets`.
### Recorded reasoning summary
Evaluating In-Batch Negatives
I'm critically examining the `inbatch` negative generation within `draw_full_history_filtered_negatives`. I've identified a significant limitation: these negatives are currently drawn uniformly from `unique_targets`, not influenced by item frequency. This needs further investigation.
### Recorded reasoning summary
Correcting In-Batch Negative Sampling
I've discovered a critical flaw: in-batch negatives are sampled uniformly, ignoring target frequency. This leads to incorrect propensity corrections in the loss function, disproportionately de-weighting popular items and requiring immediate attention.
### Recorded reasoning summary
Adjusting Negative Sampling Mix
I'm now evaluating the impact of the uniform versus in-batch negative ratio. Given the catalog size and the nature of uniform negatives, I'm exploring increasing the proportion of in-batch or popularity-biased negatives to provide more effective gradient signals.
### Recorded reasoning summary
Revising Loss Function
I'm now focused on the InfoNCE loss function. My analysis shows it lacks competitive pressure between candidate items, unlike softmax cross-entropy which directly optimizes ranking by penalizing negatives with higher scores than positives.
### Recorded reasoning summary
Testing Negative Sampling Strategies
I'm now focused on a quick experimental setup. I've verified that I can run and evaluate candidate strategies efficiently using a modified `train_current_model.py` script and the `evaluate_public.py` utility. This will allow for rapid iteration on ideas like using disliked items as true negatives and sampling popular but un-interacted items.
### Recorded reasoning summary
Optimizing Ranking Performance
I'm now investigating potential speedups within the `rank_candidates` function. By increasing `ranking_batch_size` to 512 from its default of 64, I aim to reduce the number of iterations and significantly decrease the overall ranking time.
### run_shell_command · 2026-09-29T01:41:36.277Z
{
"command": "python3 -c '\nimport time, torch, numpy as np\n\nuids_count = 8456\nitems_count = 30000\ndim = 192\n\nqueries = torch.randn(uids_count, dim)\nitems = torch.randn(items_count, dim)\npacked = np.random.randint(0, 256, (uids_count, (items_count + 7) // 8), dtype=np.uint8)\n\nfor bs in [64, 256, 512]:\n t0 = time.time()\n for start in range(0, uids_count, bs):\n end = min(start + bs, uids_count)\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n packed[start:end],\n axis=1,\n count=items_count,\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=100, dim=1).indices\n print(f\"Batch size {bs}: {time.time() - t0:.2f}s\")\n'",
"description": "Benchmark ranking batch size"
}
### Tool output · 2026-09-29T01:41:42.440Z
Output: Batch size 64: 1.21s
Batch size 256: 0.91s
Batch size 512: 1.03s
Process Group PGID: 1965
## Step 50 · agent · 2026-09-29T01:42:35.936Z
### Recorded reasoning summary
Investigating Performance Bottlenecks
I've identified that the matrix multiplication and top-k operations are incredibly fast, completing in just 1.2 seconds. This leads me to investigate the remaining 80 seconds spent in `rank_candidates`. My initial checks point to the `queries = inference_queries(model, prepared, config, device)` function as the primary area consuming this time.
### Recorded reasoning summary
Analyzing Inference Latency
I've pinpointed the GRU forward pass within `inference_queries` as the 80-second bottleneck. Given `run_training_epoch` also takes significant time, I'm now exploring potential improvements in the negative sampling strategy and its associated loss function parameters.
### Recorded reasoning summary
Revising Loss Calculations
I've been re-examining the loss function implementation. My current focus is on exploring the potential benefits of switching from the existing BCE loss to an InfoNCE-style loss, and I'm analyzing its feasibility and impact on performance and accuracy.
### Recorded reasoning summary
Exploring InfoNCE Implementation
I'm currently digging into the specifics of the InfoNCE loss. My focus is on understanding how to correctly implement it, especially concerning temperature scaling and the potential application of propensity correction for in-batch negatives. I'm also re-evaluating the negative sampling strategy itself.
### Recorded reasoning summary
Refining Negative Sampling
I'm scrutinizing the `history_filtered_negatives` function, specifically the logic for selecting in-batch negatives. My attention is drawn to how unique targets are identified and how the number of in-batch negatives is corrected based on them, aiming to understand the original intent and potential optimizations.
### Recorded reasoning summary
Investigating Sampling Ratios
I'm questioning the current ratio of in-batch to uniform negatives and considering alternative configurations. My current hypothesis is that sampling negatives proportional to item popularity, possibly using a bias like $(\text{positive\_listens} + 1)^{0.5}$, could yield more informative training signals.
### Recorded reasoning summary
Evaluating Target Weighting
I'm now prioritizing an evaluation of Hypothesis E, specifically comparing the current `organic-strong` target weighting against the previously used `discovery-graded` approach. I'm reviewing the `build_target_weights` function to understand the precise mechanics of `discovery-graded` and its potential impact.
### Recorded reasoning summary
Comparing Target Weighting Approaches
I'm focused on evaluating the 'discovery-graded' target weighting method. My current task involves thoroughly examining its implementation within `build_target_weights` to understand how it assigns weights, particularly in relation to item novelty and temporal aspects of user interactions. This comparison is key to assessing its performance against the 'organic-strong' approach.
### Recorded reasoning summary
Revisiting Default Settings
I've realized the `discovery-graded` target weighting was already implemented and available via a CLI flag, not the default. I'm now questioning why it wasn't the default and am re-evaluating the prompt's emphasis on negative examples within the existing CPU budget.
### Recorded reasoning summary
Adjusting Negative Sampling Logic
I'm now concentrating on how the training objective selects and utilizes negative examples, particularly within the `corrected_negative_loss` function. My focus is on understanding the `inbatch_correction_power` parameter and how it influences the model's approach to negative sampling under the existing CPU budget.
### Recorded reasoning summary
Re-evaluating Correction Math
I'm examining the `corrected_negative_loss` calculation. Specifically, I'm scrutinizing how the `weights` variable, derived from `build_item_correction`'s `raw` output and `correction` term, is applied. The proportionality of negatives based on `inbatch_correction_power` is now under review.
### Recorded reasoning summary
Adjusting Negative Weighting
I've discovered that `inbatch_correction_power` effectively clips the weights applied to in-batch negatives. Popular items receive disproportionately small weights (around 2.6%), while tail items get excessively large ones (4.0). This suggests the current correction math is not yielding the intended informative signal.
### Recorded reasoning summary
Adjusting Negative Weighting
I've discovered that `inbatch_correction_power` effectively clips the weights applied to in-batch negatives. Popular items receive disproportionately small weights (around 2.6%), while tail items get excessively large ones (4.0). This suggests the current correction math is not yielding the intended informative signal.
### Recorded reasoning summary
Revising Weighting Strategies
I'm re-evaluating the impact of weighting strategies on popular versus tail items. My current focus is on the effect of clipping weights and considering if uniform weighting for in-batch negatives, or the InfoNCE loss, might offer a more informative training signal.
### Recorded reasoning summary
Testing InfoNCE Implementation
I'm now directly testing the InfoNCE loss by implementing it in `train_current_model.py`. I'm examining how the loss is computed and backpropagated within `score_target_microbatches`, particularly focusing on the impact of the negative logit on the model's behavior.
### Recorded reasoning summary
Implementing InfoNCE Testing
I'm currently implementing and testing the InfoNCE loss directly within `train_current_model.py`. My focus is on observing its behavior and impact on training dynamics as computed and backpropagated within `score_target_microbatches`.
### run_shell_command · 2026-09-29T01:42:35.936Z
{
"command": "sed -n '1364,1485p' /app/solution/train_current_model.py",
"description": "Read evaluate_validation_bce in train_current_model.py"
}
### Tool output · 2026-09-29T01:42:35.952Z
Output: 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,
}
Process Group PGID: 1978
## Step 51 · agent · 2026-09-29T01:42:42.219Z
### Recorded reasoning summary
Analyzing Validation Logic
I'm tracing how the validation loss is computed. It appears `evaluate_validation_bce` simply aggregates the output of `score_target_microbatches` when `backward=False`. My next step is to examine `train_model`'s utilization of this accumulated `validation_loss`.
### run_shell_command · 2026-09-29T01:42:42.219Z
{
"command": "sed -n '1520,1650p' /app/solution/train_current_model.py",
"description": "Read train_model in train_current_model.py"
}
### Tool output · 2026-09-29T01:42:42.236Z
Output: def train_model(
public: Path,
stage: str,
prepared: SequenceData,
config: Config,
device: torch.device,
checkpoint_dir: Path | None,
resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
training_counts = partition.training_counts
validation_counts = partition.validation_counts
log(
f"chronological BCE window: train={int(training_counts.sum()):,} "
f"validation={int(validation_counts.sum()):,} "
f"selected={partition.selected_count:,} "
f"discarded_old_targets={int(partition.window_starts.sum()):,} "
f"all={prepared.target_count:,}"
)
model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
device
)
optimizer = torch.optim.AdamW(
model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
)
generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
item_correction = build_item_correction(
prepared,
config,
partition.window_starts,
training_counts,
)
target_weights = build_target_weights(
public,
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
"negative_filter_scope", "last-100"
)
stored_target_weighting = stored_config.get(
"target_weighting", "uniform"
)
stored_weight_values = (
stored_config.get("ordinary_target_weight", 1.0),
stored_config.get("organic_strong_target_weight", 1.0),
stored_config.get("organic_like_target_weight", 3.0),
stored_config.get("discovery_horizon_days", 7),
)
current_weight_values = (
config.ordinary_target_weight,
config.organic_strong_target_weight,
config.organic_like_target_weight,
config.discovery_horizon_days,
)
stored_window_values = (
stored_config.get("validation_fraction", 0.10),
stored_config.get("max_train_targets_per_user", 0),
stored_config.get("max_validation_targets_per_user", 0),
)
current_window_values = (
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
objective_changed = (
stored_filter_scope != config.negative_filter_scope
or stored_target_weighting != config.target_weighting
or stored_weight_values != current_weight_values
or stored_window_values != current_window_values
)
model.load_state_dict(payload["model_state"])
if "optimizer_state" in payload and not objective_changed:
optimizer.load_state_dict(payload["optimizer_state"])
for group in optimizer.param_groups:
group["lr"] = config.learning_rate
group["weight_decay"] = config.weight_decay
log(f"restored optimizer state from {resume_checkpoint}")
else:
log(
f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
)
if "generator_state" in payload and not objective_changed:
generator.set_state(payload["generator_state"])
start_epoch = int(payload["epoch"])
if start_epoch >= config.epochs:
raise RuntimeError(
f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
Process Group PGID: 1981
## Step 52 · agent · 2026-09-29T01:42:47.050Z
### run_shell_command · 2026-09-29T01:42:47.050Z
{
"command": "sed -n '1651,1750p' /app/solution/train_current_model.py",
"description": "Read train_model loop in train_current_model.py"
}
### Tool output · 2026-09-29T01:42:47.067Z
Output: )
prior = {
str(name): float(value)
for name, value in payload.get("diagnostics", {}).items()
}
if objective_changed:
prior.pop("validation_loss", None)
log(
"BCE objective changed: filter "
f"{stored_filter_scope}->{config.negative_filter_scope}, "
"target weighting "
f"{stored_target_weighting}->{config.target_weighting}, "
f"target window {stored_window_values}->{current_window_values}; "
"recomputing the resume checkpoint's validation BCE"
)
if "validation_loss" not in prior:
prior.update(
evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
)
history.append(prior)
best_loss = float(prior["validation_loss"])
best_epoch = start_epoch
best_state = snapshot_model(model)
log(
f"resumed validation-BCE training: epoch={start_epoch} "
f"loss={best_loss:.6f}"
)
for epoch in range(start_epoch + 1, config.epochs + 1):
diagnostics = run_training_epoch(
model,
optimizer,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
item_correction,
known_positive_packed,
generator,
device,
epoch,
)
validation = evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
diagnostics.update(validation)
history.append(diagnostics)
current_state = snapshot_model(model)
if checkpoint_dir is not None:
save_epoch_checkpoint(
checkpoint_dir,
epoch,
current_state,
config,
diagnostics,
optimizer,
generator,
)
improved = (
diagnostics["validation_loss"]
< best_loss - config.early_stopping_min_delta
)
if improved:
best_loss = diagnostics["validation_loss"]
best_epoch = epoch
best_state = current_state
epochs_without_improvement = 0
if checkpoint_dir is not None:
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": current_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
checkpoint_dir / "best.pt",
)
Process Group PGID: 1984
## Step 53 · agent · 2026-09-29T01:43:00.071Z
### Recorded reasoning summary
Refining Model Performance
I'm focusing on how the model is performing. The latest logic checks if validation loss has improved enough to warrant saving the current state. If it has, the `best_state` variable is updated with the model's weights, ensuring we retain the best configuration found so far.
### Recorded reasoning summary
Evaluating Loss Functions
I'm now considering switching from BCE to softmax cross-entropy, specifically InfoNCE, to see if it improves performance. I need to meticulously trace the mathematical implications, especially regarding the inclusion of in-batch negatives and potential propensity corrections.
### Recorded reasoning summary
Investigating Loss Corrections
I'm delving into propensity corrections and the impact of temperature scaling within InfoNCE. I'm comparing raw InfoNCE against logQ corrected versions, scrutinizing how different temperature values amplify or mute the effects of dot product differences in two-tower models.
### Recorded reasoning summary
Adjusting Negative Sampling
I'm re-examining the `draw_full_history_filtered_negatives` function and questioning the current split of uniform vs. in-batch negatives. I'm exploring various combinations, like increasing in-batch negatives and decreasing uniform ones, or even solely relying on in-batch. This also brings to mind a previous sampling limitation that needs revisiting.
### Recorded reasoning summary
Designing New Experiments
I'm formulating hypotheses to rigorously test the impact of sampling strategies versus loss functions like InfoNCE versus BCE. The goal is to create a structured experiment plan, ensuring each run is distinct and logs results clearly, allowing us to compare the effectiveness of different approaches within our limited timeframe.
### Recorded reasoning summary
Testing Sampling Power
I'm initiating an experiment to evaluate the impact of setting `inbatch_correction_power` to 0.0, effectively disabling the downweighting of popular in-batch negatives. Concurrently, I'm planning to introduce CLI parameters for granular control over various hyperparameters, enabling efficient testing of different sampling and loss configurations.
### run_shell_command · 2026-09-29T01:43:00.071Z
{
"command": "sed -n '40,75p' /app/solution/train_current_model.py",
"description": "Read Config definition in train_current_model.py"
}
### Tool output · 2026-09-29T01:43:00.087Z
Output: @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)
Process Group PGID: 1987
## Step 54 · agent · 2026-09-29T01:43:11.851Z
### Recorded reasoning summary
Refining Configuration Options
I'm currently exploring the addition of several new configuration parameters related to in-batch correction and loss functions. These include `inbatch_correction_power`, `inbatch_correction_min`, `inbatch_correction_max`, `loss_function`, and `temperature`, aiming to provide more granular control over these aspects of the model.
### Recorded reasoning summary
Validating Cache Integrity
I've been digging into the caching mechanism. It seems changes to certain `Config` fields, like the new in-batch correction and loss parameters, won't actually invalidate the existing cache files. I'm verifying this now to ensure data consistency.
### Recorded reasoning summary
Testing Sample Modes
I'm investigating the `inbatch_sample_mode` functionality within `/app/solution/train_current_model.py`, specifically examining its behavior with `_full_history_filtered_negatives`. My focus is on how the sampling strategy adjusts when presented with different batch compositions and negative sampling configurations.
### Recorded reasoning summary
Adjusting Batch Sampling
I'm reviewing the logic for selecting in-batch negative samples. My current focus is on how the `corrected_count` influences the number of samples drawn from `unique_targets` versus randomly generated `filler` samples. I'm also examining how the `score_target_microbatches` function computes positive logits.
### Recorded reasoning summary
Implementing Loss Functions
I'm actively integrating the binary cross-entropy (BCE) and infoNCE loss functions. My current work involves processing `positive_logits` and `negative_logits` with `F.softplus` for BCE and `F.cross_entropy` for infoNCE, accounting for temperature scaling in the latter.
### Recorded reasoning summary
Analyzing InfoNCE Logic
I'm scrutinizing the InfoNCE implementation, specifically how `pair_correct` relates to positive versus negative logit comparisons. My focus is on understanding what the calculated `loss_sum` actually represents in this context, considering the total number of candidates.
### run_shell_command · 2026-09-29T01:43:11.851Z
{
"command": "sed -n '1940,2020p' /app/solution/train_current_model.py",
"description": "Read argparse in train_current_model.py"
}
### Tool output · 2026-09-29T01:43:11.867Z
Output: 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
Process Group PGID: 1990
## Step 55 · agent · 2026-09-29T01:43:20.208Z
### Recorded reasoning summary
Exploring Argument Integration
I'm currently investigating the implications of integrating these novel arguments into both `argparse` and the `Config` dataclass. My focus is on how these additions affect our existing configurations, particularly regarding `bptt_steps` and `user_batch_size`, to ensure seamless compatibility and functionality.
### Recorded reasoning summary
Refining Hyperparameter Ranges
I'm now zeroing in on sensible default ranges and constraints for several key hyperparameters, including `embedding_dim`, `hidden_dim`, and various loss-related parameters. The goal is to ensure these settings promote effective training and model convergence.
### Recorded reasoning summary
Verifying Instantiation Logic
I'm cross-referencing how the `Config` object is instantiated within `main()` against the arguments passed. This step ensures that all necessary parameters, like `bptt_steps` and `user_batch_size`, are correctly mapped from `args` to the `Config` dataclass, confirming our setup.
### Recorded reasoning summary
Validating Loss Computation
I'm currently scrutinizing the `score_target_microbatches` function, specifically its implementation of the InfoNCE loss. My focus is on ensuring the logits and temperature scaling are correctly applied before the cross-entropy calculation.
### run_shell_command · 2026-09-29T01:43:20.208Z
{
"description": "Read model_config function in train_current_model.py",
"command": "sed -n '150,175p' /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:43:20.224Z
Output: 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,
)
def append_state_snapshot(
active_likes: dict[int, int],
Process Group PGID: 1993
## Step 56 · agent · 2026-09-29T01:43:25.898Z
### Recorded reasoning summary
Refining Negative Sampling
I'm currently exploring the nuances of negative sampling strategies, specifically how `uniform_negatives`, `inbatch_negatives`, and their associated correction parameters like `inbatch_correction_power` and `inbatch_correction_min` influence model performance. My focus is on how these parameters interact and optimize the generation process by carefully considering these different negative sampling approaches.
### run_shell_command · 2026-09-29T01:43:25.898Z
{
"command": "sed -n '50,75p' /app/solution/train_current_model.py",
"description": "Read Config fields"
}
### Tool output · 2026-09-29T01:43:25.915Z
Output: 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)
Process Group PGID: 1996
## Step 57 · agent · 2026-09-29T01:43:28.400Z
### run_shell_command · 2026-09-29T01:43:28.400Z
{
"command": "sed -n '1170,1205p' /app/solution/train_current_model.py",
"description": "Read score_target_microbatches loss lines"
}
### Tool output · 2026-09-29T01:43:28.419Z
Output: )
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,
Process Group PGID: 1999
## Step 58 · agent · 2026-09-29T01:43:31.185Z
### run_shell_command · 2026-09-29T01:43:31.185Z
{
"command": "sed -n '1935,2050p' /app/solution/train_current_model.py",
"description": "Read argparse arguments lines"
}
### Tool output · 2026-09-29T01:43:31.204Z
Output: )
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)
Process Group PGID: 2002
## Step 59 · agent · 2026-09-29T01:43:35.426Z
### Recorded reasoning summary
Adding New Parameters
I'm currently integrating new parameters into `Config` and `argparse` to enhance negative sampling strategies. These include `--uniform-negatives`, `--inbatch-negatives`, `--inbatch-correction-power`, and `--inbatch-correction-min`, with their respective default values.
### replace · 2026-09-29T01:43:35.426Z
{
"allow_multiple": false,
"old_string": " early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29",
"file_path": "/app/solution/train_current_model.py",
"new_string": " early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n loss_function: str = \"bce\"\n temperature: float = 0.1\n seed: int = 29",
"instruction": "Add loss_function and temperature configuration options to Config dataclass"
}
### Tool output · 2026-09-29T01:43:35.450Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
loss_function: str = "bce"
temperature: float = 0.1
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
...
## Step 60 · agent · 2026-09-29T01:43:37.938Z
### Recorded reasoning summary
Updating Score Target Logic
I'm currently refining the `score_target_microbatches` function to accommodate both InfoNCE and BCE loss calculations. My immediate next step involves a close examination of lines 1170-1210 within `train_current_model.py` to ensure precise implementation.
### run_shell_command · 2026-09-29T01:43:37.938Z
{
"description": "Check score_target_microbatches lines in train_current_model.py",
"command": "sed -n '1170,1210p' /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:43:37.954Z
Output: 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,
Process Group PGID: 2005
## Step 61 · agent · 2026-09-29T01:43:45.241Z
### Recorded reasoning summary
Exploring InfoNCE Loss
I'm currently working on integrating the InfoNCE loss function, aiming to offer it as an alternative to BCE within `score_target_microbatches`. My immediate focus is on precisely defining and implementing the computation of the InfoNCE loss itself, referencing the provided code snippet for guidance.
### Recorded reasoning summary
Implementing InfoNCE Calculation
I'm refining the calculation of the InfoNCE loss, specifically handling both cases for positive and negative logits. My focus is on correctly applying the temperature scaling and cross-entropy loss, while ensuring the weighted sum of losses aligns with the expected formula.
### replace · 2026-09-29T01:43:45.241Z
{
"allow_multiple": false,
"new_string": " 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_function == \"infonce\":\n all_logits = torch.cat(\n [positive_logits.unsqueeze(1), negative_logits], dim=1\n ) / config.temperature\n loss_per_item = F.cross_entropy(\n all_logits,\n torch.zeros(len(positive_logits), dtype=torch.long, device=device),\n reduction=\"none\",\n )\n loss_sum = (selected_weights * loss_per_item).sum()\n else:\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"old_string": " negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"instruction": "Support infonce loss in score_target_microbatches",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:43:47.704Z
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_function == "infonce":
all_logits = torch.cat(
[positive_logits.unsqueeze(1), negative_logits], dim=1
) / config.temperature
loss_per_item = F.cross_entropy(
all_logits,
torch.zeros(len(positive_logits), dtype=torch.long, device=device),
reduction="none",
)
loss_sum = (selected_weights * loss_per_item).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 62 · agent · 2026-09-29T01:43:50.038Z
### run_shell_command · 2026-09-29T01:43:50.038Z
{
"command": "sed -n '1940,2060p' /app/solution/train_current_model.py",
"description": "Check argparse lines in train_current_model.py"
}
### Tool output · 2026-09-29T01:43:50.054Z
Output: parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
Process Group PGID: 2008
## Step 63 · agent · 2026-09-29T01:43:53.747Z
### replace · 2026-09-29T01:43:53.747Z
{
"allow_multiple": false,
"instruction": "Add CLI arguments for uniform/inbatch negatives, inbatch correction power, loss function, and temperature",
"new_string": " 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(\n \"--loss-function\",\n choices=(\"bce\", \"infonce\"),\n default=Config.loss_function,\n )\n parser.add_argument(\n \"--temperature\", type=float, default=Config.temperature\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )",
"old_string": " parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:43:53.767Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--uniform-negatives", type=int, default=Config.uniform_negatives
)
parser.add_argument(
"--inbatch-negatives", type=int, default=Config.inbatch_negatives
)
parser.add_argument(
"--inbatch-correction-power",
type=float,
default=Config.inbatch_correction_power,
)
parser.add_argument(
"--loss-function",
choices=("bce", "infonce"),
default=Config.loss_function,
)
parser.add_argument(
"--temperature", type=float, default=Config.temperature
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
...
## Step 64 · agent · 2026-09-29T01:43:59.531Z
### run_shell_command · 2026-09-29T01:43:59.531Z
{
"description": "Check config instantiation in main",
"command": "sed -n '2055,2095p' /app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:43:59.549Z
Output: 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,
Process Group PGID: 2011
## Step 65 · agent · 2026-09-29T01:44:04.593Z
### replace · 2026-09-29T01:44:04.593Z
{
"instruction": "Pass newly added CLI arguments to Config instantiation in main()",
"old_string": " config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )",
"new_string": " config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n inbatch_correction_power=args.inbatch_correction_power,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n loss_function=args.loss_function,\n temperature=args.temperature,\n seed=args.seed,\n )",
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T01:44:04.611Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
uniform_negatives=args.uniform_negatives,
inbatch_negatives=args.inbatch_negatives,
inbatch_correction_power=args.inbatch_correction_power,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
...
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
loss_function=args.loss_function,
temperature=args.temperature,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
...
## Step 66 · agent · 2026-09-29T01:44:08.178Z
### run_shell_command · 2026-09-29T01:44:08.178Z
{
"command": "python3 /app/solution/train_current_model.py --help",
"description": "Verify train_current_model.py syntax and help output"
}
### Tool output · 2026-09-29T01:44:12.121Z
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]
[--uniform-negatives UNIFORM_NEGATIVES]
[--inbatch-negatives INBATCH_NEGATIVES]
[--inbatch-correction-power INBATCH_CORRECTION_POWER]
[--loss-function {bce,infonce}]
[--temperature TEMPERATURE]
[--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 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.
options:
-h, --help show this help message and exit
--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
--uniform-negatives UNIFORM_NEGATIVES
--inbatch-negatives INBATCH_NEGATIVES
--inbatch-correction-power INBATCH_CORRECTION_POWER
--loss-function {bce,infonce}
--temperature TEMPERATURE
--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
retain this many recent pre-validation targets per
user; 0 is uncapped
--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER
retain this many final validation targets per user; 0
is uncapped
--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
Process Group PGID: 2014
## Step 67 · agent · 2026-09-29T01:44:15.645Z
### Recorded reasoning summary
Testing new settings
I'm updating the `update_topic` parameter and initiating our initial experimental run. This iteration builds upon a baseline employing BCE loss, an `inbatch_correction_power` of 0.5, and a combination of uniform and in-batch negatives. The `target_weighting` is set to 'organic-strong', with a validation NDCG@10 of 0.02128.
### update_topic · 2026-09-29T01:44:15.645Z
{
"summary": "We diagnosed that inbatch_correction_power=0.5 severely downweights popular items by 16x when they appear as negatives, preventing the model from penalizing popular tracks and causing it to blindly rank popular items for over 35% of users. In Experiment 1, we test inbatch_correction_power=0.0 to verify if removing this distortion improves retrieval quality.",
"title": "Experiment 1: In-batch Correction Power 0.0",
"strategic_intent": "Run Experiment 1: Test effect of setting inbatch_correction_power=0.0 on validation NDCG@10."
}
### Tool output · 2026-09-29T01:44:15.649Z
Current topic: "Experiment 1: In-batch Correction Power 0.0"
Topic summary: We diagnosed that inbatch_correction_power=0.5 severely downweights popular items by 16x when they appear as negatives, preventing the model from penalizing popular tracks and causing it to blindly rank popular items for over 35% of users. In Experiment 1, we test inbatch_correction_power=0.0 to verify if removing this distortion improves retrieval quality.
Strategic Intent: Run Experiment 1: Test effect of setting inbatch_correction_power=0.0 on validation NDCG@10.
## Step 68 · agent · 2026-09-29T01:44:18.431Z
### run_shell_command · 2026-09-29T01:44:18.431Z
{
"description": "Run Experiment 1: inbatch_correction_power=0.0",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --inbatch-correction-power 0.0 \\\n --output /app/solution/val_exp1.parquet && \\\nPYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_exp1.parquet"
}
### Tool output · 2026-09-29T01:49:34.747Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.562498 running_bce=0.562160 elapsed=196.6s
epoch=1/1 train_loss=0.56012 validation_loss=0.51430 pair_accuracy=0.757 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.8 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.514304
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_exp1.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 308.618,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "organic-strong"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.0,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"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,
"loss_function": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5601169318967811,
"positive_logit": 0.5843293790172945,
"negative_logit": -0.6334363930126783,
"pair_accuracy": 0.7566698133724867,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 209.77656594899986,
"validation_loss": 0.5143042050851984,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.377762591000192
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.6756763458251953,
"query_norm_std": 0.8129032254219055,
"item_norm_mean": 1.8248878717422485,
"item_norm_std": 0.8556307554244995
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_exp1.parquet",
"scores": {
"quality_ndcg@10": 0.018522,
"quality_users": 4263
}
}
Process Group PGID: 2025
## Step 69 · agent · 2026-09-29T01:49:51.015Z
### Recorded reasoning summary
Analyzing In-Batch Correction
I'm currently dissecting why setting `inbatch-correction-power` to 0.0 caused a dip in NDCG@10. My hypothesis is that without correction, the equal weighting of in-batch negatives with uniform ones leads to higher logits for the in-batch samples, negatively impacting performance.
### update_topic · 2026-09-29T01:49:51.015Z
{
"summary": "In Experiment 1, setting inbatch correction power to 0.0 under BCE lowered positive logits without improving ranking. Now in Experiment 2, we test InfoNCE (softmax cross-entropy over positive and negatives with temperature=0.1), which directly optimizes competitive ranking between positive items and negative items.",
"title": "Experiment 2: InfoNCE Loss",
"strategic_intent": "Run Experiment 2: Test InfoNCE loss with temperature=0.1 on validation NDCG@10."
}
### Tool output · 2026-09-29T01:49:51.020Z
Current topic: "Experiment 2: InfoNCE Loss"
Topic summary: In Experiment 1, setting inbatch correction power to 0.0 under BCE lowered positive logits without improving ranking. Now in Experiment 2, we test InfoNCE (softmax cross-entropy over positive and negatives with temperature=0.1), which directly optimizes competitive ranking between positive items and negative items.
Strategic Intent: Run Experiment 2: Test InfoNCE loss with temperature=0.1 on validation NDCG@10.
## Step 70 · agent · 2026-09-29T01:49:53.838Z
### run_shell_command · 2026-09-29T01:49:53.838Z
{
"description": "Run Experiment 2: InfoNCE loss with temperature 0.1",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --loss-function infonce \\\n --temperature 0.1 \\\n --output /app/solution/val_exp2.parquet && \\\nPYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_exp2.parquet"
}
### Tool output · 2026-09-29T01:55:09.433Z
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=2.783409 running_bce=2.821244 elapsed=196.9s
epoch=1/1 train_loss=2.80820 validation_loss=2.56629 pair_accuracy=0.778 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.9 validation_seconds=15.6
restored best validation-BCE checkpoint: epoch=1 loss=2.566293
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_exp2.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 307.855,
"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,
"loss_function": "infonce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 2.808204360976243,
"positive_logit": 0.04665410141588994,
"negative_logit": -0.09931234342102883,
"pair_accuracy": 0.777529748478802,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 209.91781183800003,
"validation_loss": 2.5662927785774188,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.577067247000286
}
],
"embedding_diagnostics": {
"query_norm_mean": 0.7343561053276062,
"query_norm_std": 0.19629673659801483,
"item_norm_mean": 0.7892193794250488,
"item_norm_std": 0.17928875982761383
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_exp2.parquet",
"scores": {
"quality_ndcg@10": 0.016805,
"quality_users": 4263
}
}
Process Group PGID: 2068
## Step 71 · agent · 2026-09-29T01:55:16.883Z
### Recorded reasoning summary
Analyzing Embedding Divergence
I'm investigating a significant drop in NDCG@10 despite high pair accuracy from InfoNCE. Early diagnostics point to embedding norms diverging considerably in Experiment 2, possibly due to the low temperature and unnormalized vectors.
### Recorded reasoning summary
Re-evaluating Loss Function
I'm revisiting the loss function and prompt details. It appears I may have overlooked a crucial detail regarding normalization and temperature settings. I'm digging deeper into the `train_current_model.py` script to ensure accurate implementation.
### Recorded reasoning summary
Improving Negative Sampling
I'm now focusing on the core of the training objective, specifically how negative examples are selected and utilized. My current plan involves diving into `train_current_model.py` to scrutinize the existing negative sampling strategy and identify areas for optimization within the CPU budget constraints.
### run_shell_command · 2026-09-29T01:55:16.883Z
{
"command": "grep -C 3 -i \"negative\" /task/current_model/train_current_model.py | head -n 40",
"description": "Check docstrings/comments about negatives in baseline"
}
### Tool output · 2026-09-29T01:55:16.901Z
Output: 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.
"""
--
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
--
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,
--
"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",
}
--
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
retained += len(items)
Process Group PGID: 2111
## Step 72 · agent · 2026-09-29T01:55:19.848Z
### Recorded reasoning summary
Analyzing Model Architecture
I'm currently examining the specifics of this two-tower retriever model. My focus is on its distinct lack of user embeddings and output normalization, opting instead for a raw dot-product retrieval. I'm also delving into the use of sample-weighted binary cross-entropy and the unique negative sampling strategy.
### run_shell_command · 2026-09-29T01:55:19.848Z
{
"command": "grep -n -C 2 \"negative_filter_scope\" /task/current_model/train_current_model.py",
"description": "Check negative_filter_scope matches"
}
### Tool output · 2026-09-29T01:55:19.865Z
Output: 59- inbatch_correction_min: float = 0.25
60- inbatch_correction_max: float = 4.0
61: negative_filter_scope: str = "full-training-history"
62- target_weighting: str = "organic-strong"
63- ordinary_target_weight: float = 0.25
--
734- device: torch.device,
735-) -> tuple[torch.Tensor, torch.Tensor]:
736: if config.negative_filter_scope == "full-training-history":
737- if known_positive_packed is None:
738- raise RuntimeError("full-history negative filter was not built")
--
1565- known_positive_packed = (
1566- build_known_positive_filter(prepared, partition.history_training_counts)
1567: if config.negative_filter_scope == "full-training-history"
1568- else None
1569- )
--
1599- )
1600- stored_filter_scope = stored_config.get(
1601: "negative_filter_scope", "last-100"
1602- )
1603- stored_target_weighting = stored_config.get(
--
1627- )
1628- objective_changed = (
1629: stored_filter_scope != config.negative_filter_scope
1630- or stored_target_weighting != config.target_weighting
1631- or stored_weight_values != current_weight_values
--
1658- log(
1659- "BCE objective changed: filter "
1660: f"{stored_filter_scope}->{config.negative_filter_scope}, "
1661- "target weighting "
1662- f"{stored_target_weighting}->{config.target_weighting}, "
--
1944- "--negative-filter-scope",
1945- choices=("full-training-history", "last-100"),
1946: default=Config.negative_filter_scope,
1947- )
1948- parser.add_argument(
--
2032- learning_rate=args.learning_rate,
2033- loss_microbatch_size=args.loss_microbatch_size,
2034: negative_filter_scope=args.negative_filter_scope,
2035- target_weighting=args.target_weighting,
2036- ordinary_target_weight=args.ordinary_target_weight,
--
2137- "24-uniform-plus-8-propensity-corrected-inbatch"
2138- ),
2139: "false_negative_filter": config.negative_filter_scope,
2140- "target_weighting": config.target_weighting,
2141- },
Process Group PGID: 2115
## Step 73 · agent · 2026-09-29T01:55:22.927Z
### run_shell_command · 2026-09-29T01:55:22.927Z
{
"command": "sed -n '720,765p' /task/current_model/train_current_model.py",
"description": "Read draw_training_negatives in train_current_model.py"
}
### Tool output · 2026-09-29T01:55:22.944Z
Output: 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,
Process Group PGID: 2118
## Step 74 · agent · 2026-09-29T01:55:43.212Z
### Recorded reasoning summary
Considering Filter Options
I'm currently evaluating the `negative_filter_scope` parameter, specifically the implications of using `"full-training-history"` which accesses all known positive items, versus `"last-100"`. This is a key decision point for how we'll handle negative filtering going forward.
### Recorded reasoning summary
Refining Negative Sampling
I'm re-examining how `base.draw_filtered_mixed_negatives` interacts with discovery evaluation. It appears that using `"full-training-history"` for negative filtering is crucial to prevent training on items already known as positives during discovery. I'm also reviewing `corrected_negative_loss` to understand its impact.
### Recorded reasoning summary
Analyzing Loss Calculation
I've identified a potential arithmetic issue in the loss calculation. It appears the final division by `total` might be incorrectly averaging uniform and in-batch negative contributions. I'm tracing the summation and weighted averages to confirm.
### Recorded reasoning summary
Exploring Negative Sampling Ratios
I'm digging into the weighting of in-batch versus uniform negatives within the loss calculation. It appears the current setup allocates 75% of the negative loss to uniform samples and 25% to in-batch. I'm now investigating how changing this ratio, or the total number of negatives drawn, impacts performance and computational cost.
### Recorded reasoning summary
Investigating Negative Sampling Components
I'm now scrutinizing the precise wording of the prompt, especially regarding "the current negative-sampling strategy." This involves dissecting how negative examples are selected and used, with a deep dive into the docstring's mention of specific counts for uniform and in-batch negatives.
### Recorded reasoning summary
Analyzing In-Batch Negative Sampling
I'm dissecting how in-batch negatives are drawn, specifically examining `train_current_model.py`. I've noticed the use of `torch.unique` on targets, which, while preventing duplicate selections, unfortunately distorts the empirical target distribution. I'm now investigating the downstream impact of this.
### Recorded reasoning summary
Investigating Uniform Draw Impact
I'm observing that rejected in-batch negatives are replaced with uniform draws. Since popular items are often in a user's history, many in-batch negatives are converted to uniform. This significantly alters the intended sampling, and I'm tracing how it affects the propensity correction.
### Recorded reasoning summary
Tracing In-Batch Negatives
I'm investigating the origin of in-batch negatives, examining `targets` and `positive_items`. It appears an in-batch negative for a user can even be that same user's own past or future listening, which raises questions about how `invalid_full_history_negatives` operates.
### Recorded reasoning summary
Investigating Filtering Logic
I'm currently focused on the filtering mechanism, specifically how items already known in the training history are handled. It seems these are marked as invalid and subsequently replaced by uniform draws, which I'm tracing to understand its impact.
### run_shell_command · 2026-09-29T01:55:43.212Z
{
"description": "Check eligible candidate counts per user",
"command": "python3 -c '\nimport numpy as np\nwith np.load(\"/task/data/validation_eligible_candidates.npz\") as a:\n print(\"Files:\", a.files)\n uids = a[\"uids\"]\n items = a[\"item_ids\"]\n packed = a[\"packed_eligible\"]\n print(\"Users:\", len(uids), \"Items:\", len(items), \"Packed shape:\", packed.shape)\n # Check how many items are eligible per user on average\n unpacked_first = np.unpackbits(packed[0], count=len(items), bitorder=\"little\")\n print(\"Eligible for user 0:\", unpacked_first.sum(), \"out of\", len(items))\n # Sample a few users\n sums = [np.unpackbits(packed[i], count=len(items), bitorder=\"little\").sum() for i in range(10)]\n print(\"Eligible for first 10 users:\", sums)\n'"
}
### Tool output · 2026-09-29T01:55:43.718Z
Output: Files: ['uids', 'item_ids', 'packed_eligible']
Users: 8456 Items: 30000 Packed shape: (8456, 3750)
Eligible for user 0: 29318 out of 30000
Eligible for first 10 users: [np.uint64(29318), np.uint64(29832), np.uint64(29931), np.uint64(29320), np.uint64(28347), np.uint64(27476), np.uint64(29666), np.uint64(29501), np.uint64(28560), np.uint64(28736)]
Process Group PGID: 2121
## Step 75 · agent · 2026-09-29T01:55:47.851Z
### Recorded reasoning summary
Refining Ranking Logic
I'm focusing on optimizing the graded NDCG@10 metric. Given nearly the entire catalog is eligible, the challenge lies in ensuring the top 10 retrieved items are highly relevant to each user.
### run_shell_command · 2026-09-29T01:55:47.851Z
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\n# Load candidate catalog\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(cat.head(10)[[\"item_id\", \"popularity_rank\", \"positive_listens\", \"organic_positive_listens\"]])\n'",
"description": "Inspect top 10 items in catalog"
}
### Tool output · 2026-09-29T01:55:49.425Z
Output: item_id popularity_rank positive_listens organic_positive_listens
0 5862961 1 26615 20620
1 6901374 2 25536 17347
2 5635052 3 24691 17194
3 3542184 4 24075 17699
4 9378983 5 24036 18195
5 5463340 6 22746 17398
6 8213481 7 22505 17071
7 906358 8 22238 17183
8 2859641 9 18380 13704
9 3033749 10 16682 12267
Process Group PGID: 2131
## Step 76 · agent · 2026-09-29T01:55:59.208Z
### Recorded reasoning summary
Exploring Item Features
I'm currently analyzing the `build_item_features` function in `two_tower_model.py`. My focus is on understanding how it constructs four standardized features from the public catalog, specifically examining the `catalog.positive_` segment.
### Recorded reasoning summary
Processing Item Features
I've examined the `build_item_features` function and noted it computes four key features: log-transformed positive listens, log-transformed organic listens, a ratio of organic to positive listens, and a normalized popularity rank. These are then standardized and fed directly into the `item_tower`.
### Recorded reasoning summary
Connecting Towers and Weights
I've confirmed the item tower outputs vectors that are directly used in the final scoring with user tower outputs. I'm now investigating the `target_weighting` parameter in `train_current_model.py`, specifically the `"discovery-graded"` option, to understand its role in weighting targets based on novelty and user history.
### Recorded reasoning summary
Refining Target Weighting
I'm now deep in the details of `target_weighting`, specifically the `"discovery-graded"` logic. I'm tracing how repeated items, when encountered again, are assigned `ordinary_target_weight` if they aren't novel, and how valid discovery outcomes are identified based on timestamps and lookahead horizons within user interactions.
### run_shell_command · 2026-09-29T01:55:59.208Z
{
"description": "Calculate percentage of repeat listens in training sequence",
"command": "python3 -c '\nimport numpy as np\nwith np.load(\"/app/solution/val_cache.npz\") as a:\n items = a[\"sequence_items\"]\n offsets = a[\"offsets\"]\n total = 0\n repeats = 0\n for i in range(len(offsets)-1):\n seq = items[offsets[i]:offsets[i+1]]\n seen = set()\n for it in seq:\n total += 1\n if it in seen:\n repeats += 1\n else:\n seen.add(it)\n print(f\"Total tokens: {total}, Repeats: {repeats} ({repeats/total*100:.1f}%)\")\n'"
}
### Tool output · 2026-09-29T01:56:04.816Z
Output: Total tokens: 20056329, Repeats: 14491403 (72.3%)
Process Group PGID: 2148
## Step 77 · agent · 2026-09-29T01:56:13.340Z
### Recorded reasoning summary
Analyzing Token Repetition
I'm seeing a significant issue: 72.3% of my training tokens are repeats. This is problematic because these repeated tokens are ineligible during testing. I'm currently investigating the implications of this for my overall performance and how to address it.
### Recorded reasoning summary
Investigating Signal Degradation
The high percentage of repeated training tokens is heavily skewing my understanding of user preferences. My focus is now on how this impacts the effectiveness of negative examples and the implications of the `discovery-graded` weighting strategy, particularly the low weight assigned to repeated tracks versus novel ones.
### update_topic · 2026-09-29T01:56:13.340Z
{
"title": "Experiment 3: Discovery Graded Target Weighting",
"summary": "We discovered that 72.3% of training tokens are repeat listens, which are completely ineligible at test time. The baseline trains with target-weighting=organic-strong, weighting repeat listens equally to novel discoveries. In Experiment 3, we test target-weighting=discovery-graded to downweight repeats to 0.25 and elevate novel liked discoveries to 3.0.",
"strategic_intent": "Run Experiment 3: Test target-weighting=discovery-graded on validation NDCG@10."
}
### Tool output · 2026-09-29T01:56:13.345Z
Current topic: "Experiment 3: Discovery Graded Target Weighting"
Topic summary: We discovered that 72.3% of training tokens are repeat listens, which are completely ineligible at test time. The baseline trains with target-weighting=organic-strong, weighting repeat listens equally to novel discoveries. In Experiment 3, we test target-weighting=discovery-graded to downweight repeats to 0.25 and elevate novel liked discoveries to 3.0.
Strategic Intent: Run Experiment 3: Test target-weighting=discovery-graded on validation NDCG@10.
## Step 78 · agent · 2026-09-29T01:56:17.499Z
### run_shell_command · 2026-09-29T01:56:17.499Z
{
"description": "Run Experiment 3: target-weighting=discovery-graded",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --target-weighting discovery-graded \\\n --output /app/solution/val_exp3.parquet && \\\nPYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_exp3.parquet"
}
### Tool output · 2026-09-29T02:01:40.258Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=196.7s
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=210.0 validation_seconds=15.9
restored best validation-BCE checkpoint: epoch=1 loss=0.504831
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/val_exp3.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 315.02,
"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,
"loss_function": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5421154191580255,
"positive_logit": 0.7774311387847181,
"negative_logit": -0.640756203480356,
"pair_accuracy": 0.7602795423917307,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.96094086999983,
"validation_loss": 0.504830571483879,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.941616046000036
}
],
"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_exp3.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 2158
## Step 79 · agent · 2026-09-29T02:01:51.718Z
### Recorded reasoning summary
Improving Negative Sampling Strategy
I've seen a promising 10.5% jump in NDCG@10 for `target_weighting discovery-graded`, reaching 0.023522. My next step is to analyze and enhance the negative example selection within the training objective.
### Recorded reasoning summary
Exploring Negative Sampling Parameters
I'm investigating the impact of varying `uniform-negatives` and `inbatch-negatives` counts. I will also test larger total negative sample sizes to understand potential runtime implications during negative drawing and loss computation, particularly within the `score_target_microbatches` function.
### Recorded reasoning summary
Optimizing Item Encoding Efficiency
I've discovered that increasing `inbatch_negatives` while decreasing `uniform_negatives` drastically reduces the number of unique items the item MLP must encode, leading to faster training and potentially harder negatives. I am testing this hypothesis now.
### run_shell_command · 2026-09-29T02:01:51.718Z
{
"description": "Check unique item count for different uniform negative counts",
"command": "python3 -c '\nimport torch\nn_items = 30000\nbatch_size = 4000\ntargets = torch.randint(0, n_items, (batch_size,))\n\n# 24 uniform, 8 inbatch\nu24 = torch.randint(0, n_items, (batch_size, 24))\nall1 = torch.cat([targets, u24.reshape(-1)])\nprint(\"Unique items with 24 uniform:\", len(torch.unique(all1)))\n\n# 8 uniform, 24 inbatch\nu8 = torch.randint(0, n_items, (batch_size, 8))\nall2 = torch.cat([targets, u8.reshape(-1)])\nprint(\"Unique items with 8 uniform:\", len(torch.unique(all2)))\n'"
}
### Tool output · 2026-09-29T02:01:54.650Z
Output: Unique items with 24 uniform: 28964
Unique items with 8 uniform: 21040
Process Group PGID: 2207
## Step 80 · agent · 2026-09-29T02:01:59.068Z
### Recorded reasoning summary
Refining Item MLP Encoding
I'm evaluating the impact of uniform negatives on the item MLP's encoding workload. With 24 negatives, almost the entire catalog requires encoding. Reducing this to 8 negatives significantly decreases the load. I'm now analyzing the effect of 4 or 0 negatives.
### update_topic · 2026-09-29T02:01:59.068Z
{
"title": "Experiment 4: Harder In-batch Negative Mix",
"summary": "Uniform negatives sample mostly irrelevant tail catalog items and force the item tower to encode almost the full 30,000 catalog every step. In-batch negatives represent actively listened tracks from other users, providing much harder, more realistic negative competition while reducing unique item encoding overhead. In Experiment 4, we test uniform_negatives=8 and inbatch_negatives=24.",
"strategic_intent": "Run Experiment 4: Test shifting negative mix to uniform=8, inbatch=24 with discovery-graded weighting."
}
### Tool output · 2026-09-29T02:01:59.072Z
Current topic: "Experiment 4: Harder In-batch Negative Mix"
Topic summary: Uniform negatives sample mostly irrelevant tail catalog items and force the item tower to encode almost the full 30,000 catalog every step. In-batch negatives represent actively listened tracks from other users, providing much harder, more realistic negative competition while reducing unique item encoding overhead. In Experiment 4, we test uniform_negatives=8 and inbatch_negatives=24.
Strategic Intent: Run Experiment 4: Test shifting negative mix to uniform=8, inbatch=24 with discovery-graded weighting.
## Step 81 · agent · 2026-09-29T02:02:01.927Z
### run_shell_command · 2026-09-29T02:02:01.927Z
{
"description": "Run Experiment 4: uniform=8, inbatch=24, discovery-graded",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 8 \\\n --inbatch-negatives 24 \\\n --output /app/solution/val_exp4.parquet && \\\nPYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_exp4.parquet"
}
### Tool output · 2026-09-29T02:06:56.213Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.563400 running_bce=0.568936 elapsed=170.2s
epoch=1/1 train_loss=0.56679 validation_loss=0.53532 pair_accuracy=0.692 train_targets=1,052,953 validation_targets=130,103 train_seconds=181.6 validation_seconds=12.5
restored best validation-BCE checkpoint: epoch=1 loss=0.535323
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/val_exp4.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 286.501,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 8,
"inbatch_negatives": 24,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"loss_function": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5667940350914297,
"positive_logit": 0.6244476192561129,
"negative_logit": -0.3742563130715847,
"pair_accuracy": 0.6924440715777437,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 181.57273854699997,
"validation_loss": 0.5353229261199859,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 12.459902714999771
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7650988101959229,
"query_norm_std": 0.9053598642349243,
"item_norm_mean": 1.7711830139160156,
"item_norm_std": 0.778113842010498
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_exp4.parquet",
"scores": {
"quality_ndcg@10": 0.018846,
"quality_users": 4263
}
}
Process Group PGID: 2220
## Step 82 · agent · 2026-09-29T02:07:02.544Z
### Recorded reasoning summary
Analyzing Performance Degradation
My recent training run, though faster, saw a concerning drop in NDCG@10. This coincided with a decrease in pair accuracy and higher negative logits, which I'm now investigating to understand the impact of increased in-batch negatives.
### run_shell_command · 2026-09-29T02:07:02.544Z
{
"description": "Run Experiment 5: uniform=32, inbatch=0, discovery-graded",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --target-weighting discovery-graded \\\n --uniform-negatives 32 \\\n --inbatch-negatives 0 \\\n --output /app/solution/val_exp5.parquet && \\\nPYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_exp5.parquet"
}
### Tool output · 2026-09-29T02:12:09.017Z
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=180.4s
epoch=1/1 train_loss=0.52544 validation_loss=0.48448 pair_accuracy=0.798 train_targets=1,052,953 validation_targets=130,103 train_seconds=192.1 validation_seconds=15.9
restored best validation-BCE checkpoint: epoch=1 loss=0.484480
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/val_exp5.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 298.779,
"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,
"loss_function": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5254377375400164,
"positive_logit": 0.9194933260366023,
"negative_logit": -0.8096884073226461,
"pair_accuracy": 0.7977172592223964,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 192.11838058800004,
"validation_loss": 0.4844796404731151,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.908488683000087
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.4759684801101685,
"query_norm_std": 0.6978867053985596,
"item_norm_mean": 1.9009125232696533,
"item_norm_std": 0.8285666108131409
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_exp5.parquet",
"scores": {
"quality_ndcg@10": 0.022328,
"quality_users": 4263
}
}
Process Group PGID: 2269
## Step 83 · agent · 2026-09-29T02:12:30.326Z
### Recorded reasoning summary
Observing in-batch negative impact
I've been experimenting with in-batch negatives and it seems they do indeed offer a performance boost. My latest runs indicate that even a small number of in-batch negatives improve NDCG compared to solely relying on uniform negatives. This suggests a potential trade-off with BCE loss as uniform negative counts decrease.
### Recorded reasoning summary
Adjusting negative sample strategy
I'm now focusing on the ratios and quantities of uniform versus in-batch negatives, exploring combinations like 28/4 and 24/4. I'm also re-evaluating the `inbatch_correction_power` parameter, particularly with different `target_weighting` schemes. Additionally, I'm investigating the impact of training for more than one epoch, given the current hyperparameter setting.
### Recorded reasoning summary
Testing multiple epochs
My latest thoughts question the initial decision to train for only one epoch. The validation loss was still decreasing significantly after epoch one, indicating the model hadn't converged. I'm now considering increasing epochs to two or three and re-evaluating the learning rate and potential schedules.
### Recorded reasoning summary
Evaluating target discarding impact
I've realized a substantial portion of potential training targets are being discarded due to `max_train_targets_per_user`. The help text suggests this is to retain recent history, but the sheer volume of discarded items is concerning. I'm now exploring if increasing this limit, even with longer training times, could improve performance.
### update_topic · 2026-09-29T02:12:30.326Z
{
"title": "Experiment 6: Two Epochs with Discovery Graded",
"strategic_intent": "Run Experiment 6: Test epochs=2 with discovery-graded target weighting.",
"summary": "In Experiment 3, discovery-graded target weighting increased NDCG@10 from 0.02128 to 0.02352. The baseline only runs for 1 epoch, leaving the model underfitted as training loss continues downward. In Experiment 6, we test epochs=2 with discovery-graded weighting and validation BCE early stopping."
}
### Tool output · 2026-09-29T02:12:30.330Z
Current topic: "Experiment 6: Two Epochs with Discovery Graded"
Topic summary: In Experiment 3, discovery-graded target weighting increased NDCG@10 from 0.02128 to 0.02352. The baseline only runs for 1 epoch, leaving the model underfitted as training loss continues downward. In Experiment 6, we test epochs=2 with discovery-graded weighting and validation BCE early stopping.
Strategic Intent: Run Experiment 6: Test epochs=2 with discovery-graded target weighting.
## Step 84 · agent · 2026-09-29T02:12:33.514Z
### run_shell_command · 2026-09-29T02:12:33.514Z
{
"description": "Run Experiment 6: epochs=2 with discovery-graded weighting",
"command": "python3 /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --target-weighting discovery-graded \\\n --epochs 2 \\\n --output /app/solution/val_exp6.parquet && \\\nPYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_exp6.parquet"
}
### Tool output · 2026-09-29T02:21:47.711Z
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=1/2 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=196.9s
epoch=1/2 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.1 validation_seconds=15.4
epoch=2/2 batches=250 targets=988,643 batch_bce=0.433180 running_bce=0.461293 elapsed=199.8s
epoch=2/2 train_loss=0.45940 validation_loss=0.44754 pair_accuracy=0.833 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.8 validation_seconds=15.6
restored best validation-BCE checkpoint: epoch=2 loss=0.447536
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_exp6.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 546.482,
"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": 2,
"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,
"loss_function": "bce",
"temperature": 0.1,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5421154218485913,
"positive_logit": 0.7774311432444541,
"negative_logit": -0.6407561979247062,
"pair_accuracy": 0.7602795720701684,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 210.12956850499995,
"validation_loss": 0.5048305724119851,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.434231172000182
},
{
"epoch": 2.0,
"loss": 0.45939870862432,
"positive_logit": 1.1698626854661596,
"negative_logit": -1.427536000199676,
"pair_accuracy": 0.8329747089851114,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 212.83388495500003,
"validation_loss": 0.4475361476513663,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.634811792000164
}
],
"embedding_diagnostics": {
"query_norm_mean": 2.0063655376434326,
"query_norm_std": 0.8563200831413269,
"item_norm_mean": 2.6795334815979004,
"item_norm_std": 1.1226530075073242
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/val_exp6.parquet",
"scores": {
"quality_ndcg@10": 0.019155,
"quality_users": 4263
}
}
Process Group PGID: 2321
## Step 85 · agent · 2026-09-29T02:22:16.131Z
### Recorded reasoning summary
Analyzing Epoch 2 Results
I'm seeing a concerning drop in NDCG@10 despite improvements in BCE loss and other metrics during Epoch 2. This suggests a potential disconnect between raw accuracy and ranking performance that needs further investigation.
### Recorded reasoning summary
Investigating Norm Spikes
I've identified a significant jump in `item_norm_mean` and `query_norm_mean` as the likely culprit for the declining NDCG@10. The model seems to be over-expanding norms, indicating a loss of generalization.
### Recorded reasoning summary
Exploring Negative Sampling Ratios
I've pinpointed an interesting trend: NDCG@10 peaks when 25% of negatives are in-batch. I'm now testing different ratios (4, 6, 10, 12) and exploring the impact of `inbatch_correction_power` under `discovery-graded`, as well as varying `negative_filter_scope`.
### Recorded reasoning summary
Clarifying Negative Sampling Logic
I'm digging into the nuances of negative sampling, specifically the distinction between "full-training-history" and "last-100". It appears my "full-training-history" logic might be incorrectly rejecting previously *in-batch* positive examples, replacing them with uniform negatives. This warrants a deeper look into the rejection mechanism and its interaction with in-batch sampling.
### Recorded reasoning summary
Revising Target Weights
I'm reconsidering the weighting for repeat items. Given their prevalence, assigning them a lower weight (perhaps 0.05 or 0.1) could allow the model to focus more on novel items, which are key for discovery evaluation. This adjustment might prevent context loss while boosting performance on the target metrics.
### Recorded reasoning summary
Refining Negative Sampling Strategy
I'm re-evaluating how negative examples are selected and utilized within the training objective, specifically focusing on the interplay between uniform and in-batch negatives. My current approach might be unintentionally impacting the model's ability to learn from relevant in-batch examples due to logic in the rejection mechanism.
### Recorded reasoning summary
Adjusting In-Batch Negative Sampling
I'm re-examining how in-batch negatives are selected. It seems that using `torch.unique` to populate `unique_targets` might inadvertently lead to the selection of positive examples as negatives, impacting metrics like `pair_accuracy`, especially when a high number of in-batch negatives are requested.
## Recorded session usage
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