yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-fixed-r05-3d6fca60
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T05:36:37.309Z
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
- /task
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
Showing up to 200 items (files + folders).
/task/
├───.keep
├───evaluate_public.py
├───ranking_evaluation.py
├───requirements.txt
├───current_model/
│ ├───train_current_model.py
│ └───two_tower_model.py
└───data/
├───candidate_catalog.parquet
├───target_users.parquet
├───test_eligible_candidates.npz
├───test_preference_state.parquet
├───train_dislikes.parquet
├───train_likes.parquet
├───train_listens.parquet
├───train_undislikes.parquet
├───train_unlikes.parquet
├───val_dislikes.parquet
├───val_likes.parquet
├───val_listens.parquet
├───val_undislikes.parquet
├───val_unlikes.parquet
├───validation_eligible_candidates.npz
└───validation_preference_state.parquet
## Step 2 · user · 2026-09-29T05:36:37.354Z
# 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 · 2026-09-29T05:36:43.270Z
### Recorded reasoning summary
Analyzing Initial Mandates
My current focus is on deconstructing the prompt and identifying the core directives. I'm performing a preliminary assessment of available tools and the explicit requirement to initiate a topic update immediately.
### Recorded reasoning summary
Refining Retrieval Strategies
I'm now concentrating on enhancing the offline retrieval aspect of the two-tower music discovery model. My aim is to ensure the output precisely matches the specified format, containing 100 distinct items ranked 1-100 for each target user, drawn solely from their eligible candidate sets.
### update_topic · 2026-09-29T05:36:43.270Z
{
"strategic_intent": "Explore the codebase and understand the current two-tower model and evaluation pipeline.",
"summary": "Starting investigation into the production two-tower music discovery model. I will inspect the current model training code, model architecture, evaluation script, and data layout to diagnose negative sampling limitations and understand baseline performance.",
"title": "Initial Codebase and Baseline Investigation"
}
### Tool output · 2026-09-29T05:36:43.304Z
Current topic: "Initial Codebase and Baseline Investigation"
Topic summary: Starting investigation into the production two-tower music discovery model. I will inspect the current model training code, model architecture, evaluation script, and data layout to diagnose negative sampling limitations and understand baseline performance.
Strategic Intent: Explore the codebase and understand the current two-tower model and evaluation pipeline.
### run_shell_command · 2026-09-29T05:36:43.270Z
{
"description": "List task directory contents",
"command": "ls -la /task /task/current_model /task/data"
}
### Tool output · 2026-09-29T05:36:43.304Z
Output: /task:
total 40
dr-xr-xr-x 1 root root 4096 Sep 29 05:33 .
drwxr-xr-x 1 root root 4096 Sep 29 05:36 ..
-r--r--r-- 1 root root 0 Sep 29 05:31 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 05:31 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 05:32 data
-r--r--r-- 1 root root 5774 Sep 29 05:31 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 05:31 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 05:31 requirements.txt
/task/current_model:
total 104
dr-xr-xr-x 1 root root 4096 Sep 29 05:31 .
dr-xr-xr-x 1 root root 4096 Sep 29 05:33 ..
-r--r--r-- 1 root root 80058 Sep 29 05:31 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 05:31 two_tower_model.py
/task/data:
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 05:32 .
dr-xr-xr-x 1 root root 4096 Sep 29 05:33 ..
-r--r--r-- 1 root root 349352 Sep 29 05:31 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 05:31 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 05:31 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 05:32 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 05:31 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 05:31 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 05:31 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 05:31 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 05:31 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 05:31 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 05:31 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 05:31 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 05:31 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 05:31 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 05:31 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 05:32 validation_preference_state.parquet
Process Group PGID: 1612
## Step 4 · agent · 2026-09-29T05:36:45.647Z
### run_shell_command · 2026-09-29T05:36:45.647Z
{
"command": "nproc && free -h && python --version",
"description": "Check CPU count, memory, and python version"
}
### Tool output · 2026-09-29T05:36:45.667Z
Output: 8
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1615
## Step 5 · agent · 2026-09-29T05:36:47.358Z
### run_shell_command · 2026-09-29T05:36:47.358Z
{
"command": "cat /proc/meminfo | grep MemTotal && python -c \"import torch; print(torch.__version__)\"",
"description": "Check system RAM and PyTorch version"
}
### Tool output · 2026-09-29T05:36:50.273Z
Output: MemTotal: 15988964 kB
2.8.0+cpu
Process Group PGID: 1625
## Step 6 · agent · 2026-09-29T05:36:52.183Z
### read_file · 2026-09-29T05:36:52.183Z
{
"start_line": 1,
"file_path": "/task/evaluate_public.py"
}
### Tool output · 2026-09-29T05:36:52.189Z
#!/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()
## Step 7 · agent · 2026-09-29T05:37:04.132Z
### read_file · 2026-09-29T05:37:04.132Z
{
"start_line": 1,
"file_path": "/task/ranking_evaluation.py"
}
### Tool output · 2026-09-29T05:37:04.137Z
"""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),
}
## Step 8 · agent · 2026-09-29T05:37:05.924Z
### read_file · 2026-09-29T05:37:05.924Z
{
"file_path": "/task/current_model/two_tower_model.py",
"start_line": 1,
"end_line": 200
}
### Tool output · 2026-09-29T05:37:05.928Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-200 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 201.
--- FILE CONTENT (truncated) ---
"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn
DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100
@dataclass(frozen=True)
class TowerConfig:
sequence_length: int = 100
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
seed: int = 29
@dataclass(frozen=True)
class ListenEvents:
timestamps: np.ndarray
items: np.ndarray
organic: np.ndarray
completion_pct: np.ndarray
@dataclass(frozen=True)
class ExplicitEvents:
timestamps: np.ndarray
items: np.ndarray
channel: np.ndarray
active: np.ndarray
organic_like: np.ndarray
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
"""Return four standardized features built only from the public catalog."""
positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
features = np.column_stack(
[
np.log1p(positives),
np.log1p(organic),
organic / np.maximum(positives, 1.0),
1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
]
)
means = features.mean(axis=0, keepdims=True)
scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
standardized = ((features - means) / scales).astype(np.float32)
if not np.isfinite(standardized).all():
raise RuntimeError("catalog features contain non-finite values")
return standardized
def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
if len(parts) == 1:
return parts[0]
timestamps = np.concatenate([part.timestamps for part in parts])
items = np.concatenate([part.items for part in parts])
organic = np.concatenate([part.organic for part in parts])
completion = np.concatenate([part.completion_pct for part in parts])
order = np.argsort(timestamps, kind="stable")
return ListenEvents(
timestamps=timestamps[order],
items=items[order],
organic=organic[order],
completion_pct=completion[order],
)
def iter_listen_users(
path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Stream one uid-sorted public listen file one user at a time."""
parquet = pq.ParquetFile(path)
pending_uid: int | None = None
pending: list[ListenEvents] = []
previous_uid = -1
for batch in parquet.iter_batches(
batch_size=750_000,
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
):
frame = batch.to_pandas()
mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
if not np.any(keep):
continue
uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
items = mapped[keep].astype(np.int32, copy=False)
organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
if np.any(uids[1:] < uids[:-1]):
raise RuntimeError(f"{path.name} is not sorted by uid")
boundaries = np.r_[
0,
np.flatnonzero(uids[1:] != uids[:-1]) + 1,
len(uids),
]
for start, end in zip(boundaries[:-1], boundaries[1:]):
uid = int(uids[start])
part = ListenEvents(
timestamps[start:end].copy(),
items[start:end].copy(),
organic[start:end].copy(),
completion[start:end].copy(),
)
if pending_uid is None:
pending_uid = uid
elif uid != pending_uid:
if pending_uid < previous_uid:
raise RuntimeError(f"{path.name} user order regressed")
yield pending_uid, _combine_listen_chunks(pending)
previous_uid = pending_uid
pending_uid = uid
pending = []
pending.append(part)
if pending_uid is not None:
yield pending_uid, _combine_listen_chunks(pending)
def merge_listen_streams(
paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Merge uid-sorted public listen files without materializing them."""
streams = [iter(iter_listen_users(path, item_index)) for path in paths]
heads: list[tuple[int, ListenEvents] | None] = [
next(stream, None) for stream in streams
]
while any(head is not None for head in heads):
uid = min(head[0] for head in heads if head is not None)
parts: list[ListenEvents] = []
for position, head in enumerate(heads):
if head is not None and head[0] == uid:
parts.append(head[1])
heads[position] = next(streams[position], None)
yield uid, _combine_listen_chunks(parts)
def load_explicit_events(
data: Path,
prefixes: tuple[str, ...],
item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
"""Load public explicit actions as chronological state changes."""
specifications = (
("likes", 0, 1, 0),
("unlikes", 0, 0, 1),
("dislikes", 1, 1, 0),
("undislikes", 1, 0, 1),
)
parts: list[pd.DataFrame] = []
for family, channel, active, tie_priority in specifications:
for prefix in prefixes:
frame = pd.read_parquet(
data / f"{prefix}_{family}.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
mapped = item_index.get_indexer(
frame.item_id.to_numpy(dtype=np.int64, copy=False)
)
keep = mapped >= 0
if not np.any(keep):
continue
retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
## Step 9 · agent · 2026-09-29T05:37:08.257Z
### read_file · 2026-09-29T05:37:08.257Z
{
"file_path": "/task/current_model/two_tower_model.py",
"start_line": 201,
"end_line": 434
}
### Tool output · 2026-09-29T05:37:08.261Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 201-434 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 435.
--- FILE CONTENT (truncated) ---
retained["item"] = mapped[keep].astype(np.int32, copy=False)
retained["channel"] = np.int8(channel)
retained["active"] = np.int8(active)
retained["tie_priority"] = np.int8(tie_priority)
retained["organic_like"] = np.int8(
family == "likes"
) * retained.is_organic.astype(np.int8)
parts.append(retained.drop(columns="is_organic"))
if not parts:
return {}
events = pd.concat(parts, ignore_index=True)
events = events.sort_values(
["uid", "timestamp", "tie_priority"], kind="mergesort"
).reset_index(drop=True)
result: dict[int, ExplicitEvents] = {}
for uid, rows in events.groupby("uid", sort=False):
result[int(uid)] = ExplicitEvents(
timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
items=rows.item.to_numpy(dtype=np.int32, copy=True),
channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
active=rows.active.to_numpy(dtype=np.int8, copy=True),
organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
)
log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
return result
def empty_explicit_events() -> ExplicitEvents:
return ExplicitEvents(
timestamps=np.empty(0, dtype=np.int64),
items=np.empty(0, dtype=np.int32),
channel=np.empty(0, dtype=np.int8),
active=np.empty(0, dtype=np.int8),
organic_like=np.empty(0, dtype=np.int8),
)
def pack_active_items(
active: dict[int, int], width: int, padding_item: int
) -> np.ndarray:
packed = np.full(width, padding_item, dtype=np.uint16)
if not active:
return packed
ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
packed[: len(ordered)] = np.fromiter(
(item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
)
return packed
def load_inference_state(
path: Path,
item_index: pd.Index,
uids: np.ndarray,
n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Pack the task-provided active explicit state."""
state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
state = state.loc[keep, ["uid", "state"]].copy()
state["item"] = mapped[keep].astype(np.int32, copy=False)
user_rows = pd.Index(uids)
liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
like_counts = np.zeros(len(uids), dtype=np.int32)
dislike_counts = np.zeros(len(uids), dtype=np.int32)
for value, width, destination, counts in (
("liked", LIKE_ITEMS, liked, like_counts),
("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
):
selected = state[state.state == value]
for uid, rows in selected.groupby("uid", sort=False):
row = int(user_rows.get_indexer([int(uid)])[0])
if row < 0:
continue
items = np.sort(rows.item.unique())
counts[row] = len(items)
retained = items[:width]
destination[row, : len(retained)] = retained.astype(np.uint16)
return liked, disliked, like_counts, dislike_counts
class SequentialTwoTower(nn.Module):
"""Order-sensitive user tower and independent catalog-aware item tower."""
def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
super().__init__()
n_items, n_item_features = item_features.shape
dim = config.embedding_dim
self.n_items = n_items
self.context_embedding = nn.Embedding(
n_items + 1, dim, padding_idx=n_items
)
self.event_projection = nn.Sequential(
nn.Linear(dim + 3, config.hidden_dim),
nn.GELU(),
)
self.sequence_encoder = nn.GRU(
input_size=config.hidden_dim,
hidden_size=dim,
num_layers=1,
batch_first=True,
)
self.user_tower = nn.Sequential(
nn.Linear(dim * 3 + 6, config.user_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_1, config.user_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_2, dim),
)
self.candidate_embedding = nn.Embedding(n_items, dim)
self.register_buffer(
"item_features", torch.from_numpy(item_features), persistent=True
)
self.item_tower = nn.Sequential(
nn.Linear(dim + n_item_features, config.item_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_1, config.item_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_2, dim),
)
nn.init.normal_(self.context_embedding.weight, std=0.02)
nn.init.normal_(self.candidate_embedding.weight, std=0.02)
with torch.no_grad():
self.context_embedding.weight[n_items].zero_()
def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
mask = items.ne(self.n_items)
vectors = self.context_embedding(items)
return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
dim=1, keepdim=True
).clamp_min(1)
def encode_items(self, items: torch.Tensor) -> torch.Tensor:
inputs = torch.cat(
[self.candidate_embedding(items), self.item_features[items]], dim=1
)
return self.item_tower(inputs)
def invalid_negative_mask(
negatives: torch.Tensor,
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
) -> torch.Tensor:
"""Identify targets, known positives, active likes, and row duplicates."""
invalid = negatives.eq(targets[:, None])
invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= negatives[:, column, None].eq(
negatives[:, :column]
).any(dim=1)
return invalid
def draw_filtered_mixed_negatives(
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
n_items: int,
uniform_count: int,
inbatch_count: int,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw uniform and unique-target in-batch negatives."""
batch_size = len(targets)
uniform = torch.randint(
n_items,
(batch_size, uniform_count),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
-1.0,
)
columns = torch.topk(priorities, k=corrected_count, dim=1).indices
inbatch = unique_targets[columns]
if corrected_count < inbatch_count:
filler = torch.randint(
n_items,
(batch_size, inbatch_count - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
:, uniform_count : uniform_count + corrected_count
] = item_correction[inbatch[:, :corrected_count]]
negatives = negatives_cpu.to(device)
correction = correction_cpu.to(device)
for _ in range(32):
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if not bool(invalid.any()):
return negatives, correction
replacements = torch.randint(
n_items,
(int(invalid.sum().detach().cpu()),),
generator=generator,
device="cpu",
).to(device)
negatives[invalid] = replacements
correction[invalid] = 1.0
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if bool(invalid.any()):
raise RuntimeError("mixed negative resampling failed false-negative filter")
return negatives, correction
## Step 10 · agent · 2026-09-29T05:37:10.693Z
### Recorded reasoning summary
Examining Training Pipeline
I'm currently deep-diving into `draw_filtered_mixed_negatives` and `invalid_negative_mask` functions. My next step is to meticulously review `train_current_model.py` to understand the intricate wiring, loss computation, and evaluation/ranking mechanisms.
### grep_search · 2026-09-29T05:37:10.693Z
{
"pattern": "def ",
"dir_path": "/task/current_model"
}
### Tool output · 2026-09-29T05:37:10.712Z
Found 57 matches for pattern "def " in path "/task/current_model":
---
File: train_current_model.py
L82: def read(cls, path: Path) -> "CandidateSet":
L118: def lengths(self) -> np.ndarray:
L122: def target_count(self) -> int:
L136: def selected_count(self) -> int:
L140: def log(message: str) -> None:
L144: def seed_everything(seed: int) -> None:
L150: def model_config(config: Config) -> base.TowerConfig:
L174: def append_state_snapshot(
L190: def causal_activity_features(
L247: def build_sequence_parts(
L314: def final_user_features(
L341: def prepare_data(
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L481: def save_cache(
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L511: def load_or_prepare(
L534: def build_item_correction(
L578: def build_known_positive_filter(
L609: def invalid_full_history_negatives(
L630: def draw_full_history_filtered_negatives(
L724: def draw_training_negatives(
L763: def event_sequence(
L774: def pool_explicit_states(
L798: def user_queries(
L813: def candidate_vectors(
L828: def history_windows(
L845: def corrected_negative_loss(
L867: def retained_target_window_starts(
L888: def chronological_target_partition(
L914: def load_organic_like_outcomes(
L955: def build_target_weights(
L1035: def build_dense_block(
L1111: def score_target_microbatches(
L1198: def release_accelerator_cache(device: torch.device) -> None:
L1203: def run_training_epoch(
L1364: def evaluate_validation_bce(
L1488: def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
L1495: def save_epoch_checkpoint(
L1521: def train_model(
L1782: def inference_queries(
L1861: def rank_candidates(
L1912: def write_ranking(frame: pd.DataFrame, output: Path) -> None:
L1922: def main() -> int:
---
File: two_tower_model.py
L65: def log(message: str) -> None:
L69: def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
L90: def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
L106: def iter_listen_users(
L157: def merge_listen_streams(
L175: def load_explicit_events(
L228: def empty_explicit_events() -> ExplicitEvents:
L238: def pack_active_items(
L251: def load_inference_state(
L287: def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
L332: def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
L339: def encode_items(self, items: torch.Tensor) -> torch.Tensor:
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---
## Step 11 · agent · 2026-09-29T05:37:13.446Z
### Recorded reasoning summary
Testing Current Model
I'm initiating the validation script for the current model now. My immediate focus is on observing its execution time to understand performance characteristics before proceeding further.
### read_file · 2026-09-29T05:37:13.446Z
{
"end_line": 170,
"start_line": 1,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:37:13.451Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-170 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 171.
--- FILE CONTENT (truncated) ---
#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.
Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss. The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split. A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
into the next block.
The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations
import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F
import two_tower_model as base
CACHE_VERSION = 2
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
item_ids: np.ndarray
packed: np.ndarray
@classmethod
def read(cls, path: Path) -> "CandidateSet":
with np.load(path, allow_pickle=False) as artifact:
result = cls(
uids=artifact["uids"].astype(np.int64, copy=True),
item_ids=artifact["item_ids"].astype(np.int64, copy=True),
packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
)
expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
if result.packed.shape != expected:
raise ValueError(
f"invalid candidate artifact shape {result.packed.shape}; "
f"expected {expected}"
)
return result
@dataclass(frozen=True)
class SequenceData:
item_ids: np.ndarray
item_features: np.ndarray
user_ids: np.ndarray
offsets: np.ndarray
sequence_timestamps: np.ndarray
sequence_items: np.ndarray
event_features: np.ndarray
activity_features: np.ndarray
state_ids: np.ndarray
liked_states: np.ndarray
disliked_states: np.ndarray
state_counts: np.ndarray
inference_user_rows: np.ndarray
inference_liked_items: np.ndarray
inference_disliked_items: np.ndarray
inference_user_features: np.ndarray
@property
def lengths(self) -> np.ndarray:
return np.diff(self.offsets)
@property
def target_count(self) -> int:
return int(np.maximum(self.lengths - 1, 0).sum())
@dataclass(frozen=True)
class TargetPartition:
"""A contiguous recent target window and its chronological BCE split."""
window_starts: np.ndarray
training_counts: np.ndarray
validation_counts: np.ndarray
history_training_counts: np.ndarray
@property
def selected_count(self) -> int:
return int(self.training_counts.sum() + self.validation_counts.sum())
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def model_config(config: Config) -> base.TowerConfig:
"""Map the dense trainer configuration onto the unchanged tower."""
return base.TowerConfig(
sequence_length=config.bptt_steps,
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim,
user_hidden_1=config.user_hidden_1,
user_hidden_2=config.user_hidden_2,
item_hidden_1=config.item_hidden_1,
item_hidden_2=config.item_hidden_2,
dropout=config.dropout,
epochs=config.epochs,
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
gradient_clip=config.gradient_clip,
uniform_negatives=config.uniform_negatives,
inbatch_negatives=config.inbatch_negatives,
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
seed=config.seed,
## Step 12 · agent · 2026-09-29T05:37:15.847Z
### read_file · 2026-09-29T05:37:15.847Z
{
"end_line": 760,
"start_line": 530,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:37:15.851Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 530-760 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 761.
--- FILE CONTENT (truncated) ---
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""Estimate in-batch propensities from the selected training targets."""
counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
for user_row, count in enumerate(target_counts):
if not count:
continue
offset = int(prepared.offsets[user_row])
target_start = int(target_starts[user_row])
counts += np.bincount(
prepared.sequence_items[
offset
+ target_start
+ 1 : offset
+ target_start
+ int(count)
+ 1
].astype(np.int64),
minlength=len(prepared.item_ids),
)
total = float(counts.sum())
probabilities = (counts + 1.0) / (total + len(counts))
raw = np.power(
(1.0 / len(counts)) / probabilities,
config.inbatch_correction_power,
)
normalization = float(np.sum(counts * raw) / max(total, 1.0))
correction = np.clip(
raw / max(normalization, 1.0e-12),
config.inbatch_correction_min,
config.inbatch_correction_max,
).astype(np.float32)
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
log(
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
)
return torch.from_numpy(correction)
def build_known_positive_filter(
prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
"""Pack each user's complete public-training positive-item set."""
width = (len(prepared.item_ids) + 7) // 8
packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
retained = 0
for user_row, target_count in enumerate(training_counts):
if not target_count:
continue
offset = int(prepared.offsets[user_row])
# T adjacent targets consume sequence positions 0 through T. The
# chronological BCE suffix starts at T + 1 and is intentionally absent.
items = np.unique(
prepared.sequence_items[
offset : offset + int(target_count) + 1
].astype(np.int64)
)
byte_columns = items >> 3
bit_values = np.left_shift(
np.uint8(1), (items & 7).astype(np.uint8)
)
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
retained += len(items)
log(
f"full-history false-negative filter: users={len(prepared.user_ids):,} "
f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
)
return packed
def invalid_full_history_negatives(
negatives: np.ndarray,
targets: np.ndarray,
user_rows: np.ndarray,
known_positive_packed: np.ndarray,
liked_items: np.ndarray,
) -> np.ndarray:
"""Return row-level invalid negatives using the complete training prefix."""
byte_columns = negatives >> 3
bit_columns = negatives & 7
known = known_positive_packed[user_rows[:, None], byte_columns]
invalid = ((known >> bit_columns) & 1).astype(bool)
invalid |= negatives == targets[:, None]
invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= (
negatives[:, column, None] == negatives[:, :column]
).any(axis=1)
return invalid
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
def draw_training_negatives(
positive_items: torch.Tensor,
known_history: torch.Tensor,
liked_items: torch.Tensor,
user_rows: np.ndarray,
known_positive_packed: np.ndarray | None,
prepared: SequenceData,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
if config.negative_filter_scope == "full-training-history":
if known_positive_packed is None:
raise RuntimeError("full-history negative filter was not built")
return draw_full_history_filtered_negatives(
positive_items,
user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
config,
item_correction,
generator,
device,
)
return base.draw_filtered_mixed_negatives(
positive_items,
known_history,
liked_items,
len(prepared.item_ids),
config.uniform_negatives,
config.inbatch_negatives,
item_correction,
generator,
device,
)
## Step 13 · agent · 2026-09-29T05:37:18.075Z
### read_file · 2026-09-29T05:37:18.075Z
{
"end_line": 1000,
"start_line": 761,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:37:18.078Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 761-1000 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1001.
--- FILE CONTENT (truncated) ---
def event_sequence(
model: base.SequentialTwoTower,
items: torch.Tensor,
features: torch.Tensor,
hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
inputs = torch.cat([model.context_embedding(items), features], dim=-1)
projected = model.event_projection(inputs)
return model.sequence_encoder(projected, hidden)
def pool_explicit_states(
model: base.SequentialTwoTower,
prepared: SequenceData,
state_ids: np.ndarray,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
unique_states, inverse = np.unique(state_ids, return_inverse=True)
liked_items = torch.from_numpy(
prepared.liked_states[unique_states].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.disliked_states[unique_states].astype(np.int64)
).to(device)
liked_vectors = model._mean_context_channel(liked_items)
disliked_vectors = model._mean_context_channel(disliked_items)
inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
counts = prepared.state_counts[state_ids]
return (
liked_vectors[inverse_rows],
disliked_vectors[inverse_rows],
torch.from_numpy(counts.astype(np.float32)).to(device),
)
def user_queries(
model: base.SequentialTwoTower,
sequence_vectors: torch.Tensor,
liked_vectors: torch.Tensor,
disliked_vectors: torch.Tensor,
user_features: torch.Tensor,
) -> torch.Tensor:
return model.user_tower(
torch.cat(
[sequence_vectors, liked_vectors, disliked_vectors, user_features],
dim=1,
)
)
def candidate_vectors(
model: base.SequentialTwoTower,
positives: torch.Tensor,
negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
all_items = torch.cat([positives, negatives.reshape(-1)])
unique_items, inverse = torch.unique(all_items, return_inverse=True)
encoded = model.encode_items(unique_items)
positive_vectors = encoded[inverse[: len(positives)]]
negative_vectors = encoded[inverse[len(positives) :]].reshape(
len(positives), negatives.shape[1], -1
)
return positive_vectors, negative_vectors
def history_windows(
sequence: np.ndarray,
start: int,
count: int,
width: int,
padding_item: int,
) -> np.ndarray:
base_start = max(0, start - width + 1)
segment = sequence[base_start : start + count]
padded = np.concatenate(
[np.full(width - 1, padding_item, dtype=np.uint16), segment]
)
windows = np.lib.stride_tricks.sliding_window_view(padded, width)
offset = start - base_start
return windows[offset : offset + count].copy()
def corrected_negative_loss(
logits: torch.Tensor,
correction: torch.Tensor,
config: Config,
) -> torch.Tensor:
raw = F.softplus(logits)
total = config.uniform_negatives + config.inbatch_negatives
result = torch.zeros(len(logits), device=logits.device)
if config.uniform_negatives:
result += config.uniform_negatives * raw[
:, : config.uniform_negatives
].mean(dim=1)
if config.inbatch_negatives:
selected = slice(config.uniform_negatives, total)
weights = correction[:, selected]
result += config.inbatch_negatives * (
(raw[:, selected] * weights).sum(dim=1)
/ weights.sum(dim=1).clamp_min(1.0e-6)
)
return result / total
def retained_target_window_starts(
total_targets: np.ndarray,
validation_counts: np.ndarray,
training_counts: np.ndarray,
) -> np.ndarray:
"""Return the discarded prefix before the retained chronological suffix."""
training_region_ends = total_targets - validation_counts
window_starts = training_region_ends - training_counts
if (
np.any(window_starts < 0)
or np.any(training_counts < 0)
or np.any(validation_counts < 0)
):
raise RuntimeError("chronological target partition produced negative counts")
if not np.array_equal(
window_starts + training_counts + validation_counts, total_targets
):
raise RuntimeError("chronological target window is not a final contiguous suffix")
return window_starts
def chronological_target_partition(
prepared: SequenceData,
validation_fraction: float,
max_train_targets_per_user: int,
max_validation_targets_per_user: int,
) -> TargetPartition:
"""Select a recent contiguous window with a final out-of-time BCE suffix."""
totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
validation = np.floor(totals * validation_fraction).astype(np.int64)
validation[(totals >= 2) & (validation == 0)] = 1
if max_validation_targets_per_user:
validation = np.minimum(validation, max_validation_targets_per_user)
history_training = totals - validation
training = history_training.copy()
if max_train_targets_per_user:
training = np.minimum(training, max_train_targets_per_user)
window_starts = retained_target_window_starts(totals, validation, training)
return TargetPartition(
window_starts=window_starts,
training_counts=training,
validation_counts=validation,
history_training_counts=history_training,
)
def load_organic_like_outcomes(
public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
"""Load mapped organic-like timestamps from public fitting history."""
prefixes = ("train",) if stage == "validation" else ("train", "val")
frames = [
pd.read_parquet(
public / "data" / f"{prefix}_likes.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
for prefix in prefixes
]
likes = pd.concat(frames, ignore_index=True)
likes = likes[likes.is_organic == 1].copy()
mapped = pd.Index(item_ids).get_indexer(
likes.item_id.to_numpy(dtype=np.int64)
)
likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
nested: dict[int, dict[int, list[int]]] = {}
for raw_uid, raw_timestamp, raw_item in likes.itertuples(
index=False, name=None
):
nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
int(raw_timestamp)
)
result = {
uid: {
item: np.asarray(timestamps, dtype=np.int64)
for item, timestamps in items.items()
}
for uid, items in nested.items()
}
log(
f"loaded organic-like outcomes: rows={len(likes):,} "
f"users={len(result):,}"
)
return result
def build_target_weights(
public: Path,
stage: str,
prepared: SequenceData,
training_counts: np.ndarray,
config: Config,
) -> np.ndarray:
"""Build nonzero per-token weights without dropping adjacent targets."""
if config.target_weighting == "uniform":
weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
else:
weights = np.full(
len(prepared.sequence_items),
config.ordinary_target_weight,
dtype=np.float32,
)
organic_strong = (
(prepared.event_features[:, 0] > 0.5)
& (prepared.event_features[:, 1] >= 0.8)
)
weights[organic_strong] = config.organic_strong_target_weight
if config.target_weighting == "discovery-graded":
like_outcomes = load_organic_like_outcomes(
public, stage, prepared.item_ids
)
history_end = (
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])
## Step 14 · agent · 2026-09-29T05:37:20.105Z
### read_file · 2026-09-29T05:37:20.105Z
{
"file_path": "/task/current_model/train_current_model.py",
"end_line": 1350,
"start_line": 1001
}
### Tool output · 2026-09-29T05:37:20.108Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1001-1350 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1351.
--- FILE CONTENT (truncated) ---
novel = item not in seen
seen.add(item)
if not novel:
# Repeats remain training examples, but they cannot be
# eligible discovery outcomes at the corresponding
# cutoff and therefore keep only the ordinary weight.
weights[position] = config.ordinary_target_weight
continue
timestamps = user_likes.get(item)
if timestamps is None:
continue
timestamp = int(prepared.sequence_timestamps[position])
upper = min(
timestamp + horizon,
split_timestamp
if position <= last_training_target
else history_end,
)
outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
weights[position] = config.organic_like_target_weight
grade_three += 1
log(
f"grade-3 first-encounter targets={grade_three:,} "
f"horizon_days={config.discovery_horizon_days}"
)
log(
f"target weighting={config.target_weighting} "
f"min={weights.min():.3f} mean={weights.mean():.3f} "
f"max={weights.max():.3f}"
)
return weights
def build_dense_block(
prepared: SequenceData,
target_weights: np.ndarray,
users: np.ndarray,
window_starts: np.ndarray,
process_counts: np.ndarray,
score_starts: np.ndarray,
start: int,
steps: int,
config: Config,
) -> tuple[np.ndarray, ...]:
"""Build one causal sequence block and mark the target rows to score."""
batch_size = len(users)
padding_item = len(prepared.item_ids)
input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
targets = np.zeros((batch_size, steps), dtype=np.uint16)
weights = np.ones((batch_size, steps), dtype=np.float32)
scored = np.zeros((batch_size, steps), dtype=bool)
histories = np.full(
(batch_size, steps, config.bptt_steps),
padding_item,
dtype=np.uint16,
)
processed = np.zeros(batch_size, dtype=np.int32)
lengths = prepared.lengths
for row, user_row in enumerate(users):
count = min(steps, max(int(process_counts[row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[user_row])
window_start = int(window_starts[row])
absolute_start = window_start + start
positions = slice(
offset + absolute_start,
offset + absolute_start + count,
)
target_positions = slice(
offset + absolute_start + 1,
offset + absolute_start + count + 1,
)
input_items[row, :count] = prepared.sequence_items[positions]
event_features[row, :count] = prepared.event_features[positions]
activity_features[row, :count] = prepared.activity_features[positions]
state_ids[row, :count] = prepared.state_ids[positions]
targets[row, :count] = prepared.sequence_items[target_positions]
weights[row, :count] = target_weights[target_positions]
score_from = min(count, max(int(score_starts[row]) - start, 0))
scored[row, score_from:count] = True
processed[row] = count
user_sequence = prepared.sequence_items[
offset : offset + int(lengths[user_row])
]
histories[row, :count] = history_windows(
user_sequence,
absolute_start,
count,
config.bptt_steps,
padding_item,
)
return (
input_items,
event_features,
activity_features,
state_ids,
targets,
scored,
histories,
processed,
weights,
)
def score_target_microbatches(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
sequence_vectors: torch.Tensor,
flat_states: np.ndarray,
flat_activity: np.ndarray,
positive_items: torch.Tensor,
negatives: torch.Tensor,
negative_weights: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
"""Compute one logical block's exact BCE in bounded-memory slices."""
target_count = len(positive_items)
total_weight = float(sample_weights.sum())
if total_weight <= 0:
raise RuntimeError("target block has no positive sample weight")
sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
statistics = {
"loss_sum": 0.0,
"positive_sum": 0.0,
"negative_sum": 0.0,
"pair_correct": 0.0,
"weight_sum": 0.0,
}
for begin in range(0, target_count, config.loss_microbatch_size):
end = min(begin + config.loss_microbatch_size, target_count)
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
def run_training_epoch(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
generator: torch.Generator,
device: torch.device,
epoch: int,
) -> dict[str, float]:
model.train()
train_users = np.flatnonzero(target_counts > 0)
sorted_users = train_users[
np.argsort(target_counts[train_users], kind="stable")
]
groups = [
sorted_users[start : start + config.user_batch_size]
for start in range(0, len(sorted_users), config.user_batch_size)
]
group_order = torch.randperm(len(groups), generator=generator).numpy()
negative_count = config.uniform_negatives + config.inbatch_negatives
total_loss = 0.0
total_targets = 0
total_weight = 0.0
total_positive = 0.0
total_negative = 0.0
total_pair_correct = 0.0
batches = 0
started = time.monotonic()
for group_number in group_order:
users = groups[int(group_number)]
group_counts = target_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_counts.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_counts,
np.zeros(len(users), dtype=np.int64),
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0).detach()
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
## Step 15 · agent · 2026-09-29T05:37:22.072Z
### read_file · 2026-09-29T05:37:22.072Z
{
"end_line": 1600,
"start_line": 1351,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:37:22.076Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1351-1600 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1601.
--- FILE CONTENT (truncated) ---
"loss": total_loss / max(total_weight, 1.0e-12),
"positive_logit": total_positive / max(total_targets, 1),
"negative_logit": total_negative / max(total_targets * negative_count, 1),
"pair_accuracy": total_pair_correct
/ max(total_targets * negative_count, 1),
"targets": float(total_targets),
"target_weight_sum": total_weight,
"batches": float(batches),
"seconds": time.monotonic() - started,
}
@torch.no_grad()
def evaluate_validation_bce(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
np.argsort(totals[users_with_validation], kind="stable")
]
groups = [
ordered[start : start + config.user_batch_size]
for start in range(0, len(ordered), config.user_batch_size)
]
generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
total_loss = 0.0
total_targets = 0
total_weight = 0.0
started = time.monotonic()
for group_number, users in enumerate(groups, start=1):
group_totals = totals[users]
group_starts = training_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_totals.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_totals,
group_starts,
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0)
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
statistics, _ = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=False,
)
total_loss += statistics["loss_sum"]
total_targets += len(positive_items)
total_weight += statistics["weight_sum"]
if group_number % 25 == 0:
release_accelerator_cache(device)
return {
"validation_loss": total_loss / max(total_weight, 1.0e-12),
"validation_targets": float(total_targets),
"validation_target_weight_sum": total_weight,
"validation_seconds": time.monotonic() - started,
}
def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
return {
name: value.detach().cpu().clone()
for name, value in model.state_dict().items()
}
def save_epoch_checkpoint(
checkpoint_dir: Path,
epoch: int,
model_state: dict[str, torch.Tensor],
config: Config,
diagnostics: dict[str, float],
optimizer: torch.optim.Optimizer,
generator: torch.Generator,
) -> Path:
checkpoint_dir.mkdir(parents=True, exist_ok=True)
path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": model_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
path,
)
log(f"saved epoch checkpoint: {path}")
return path
def train_model(
public: Path,
stage: str,
prepared: SequenceData,
config: Config,
device: torch.device,
checkpoint_dir: Path | None,
resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
training_counts = partition.training_counts
validation_counts = partition.validation_counts
log(
f"chronological BCE window: train={int(training_counts.sum()):,} "
f"validation={int(validation_counts.sum()):,} "
f"selected={partition.selected_count:,} "
f"discarded_old_targets={int(partition.window_starts.sum()):,} "
f"all={prepared.target_count:,}"
)
model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
device
)
optimizer = torch.optim.AdamW(
model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
)
generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
item_correction = build_item_correction(
prepared,
config,
partition.window_starts,
training_counts,
)
target_weights = build_target_weights(
public,
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
## Step 16 · agent · 2026-09-29T05:37:24.101Z
### read_file · 2026-09-29T05:37:24.101Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 1601,
"end_line": 2000
}
### Tool output · 2026-09-29T05:37:24.105Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1601-2000 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2001.
--- FILE CONTENT (truncated) ---
"negative_filter_scope", "last-100"
)
stored_target_weighting = stored_config.get(
"target_weighting", "uniform"
)
stored_weight_values = (
stored_config.get("ordinary_target_weight", 1.0),
stored_config.get("organic_strong_target_weight", 1.0),
stored_config.get("organic_like_target_weight", 3.0),
stored_config.get("discovery_horizon_days", 7),
)
current_weight_values = (
config.ordinary_target_weight,
config.organic_strong_target_weight,
config.organic_like_target_weight,
config.discovery_horizon_days,
)
stored_window_values = (
stored_config.get("validation_fraction", 0.10),
stored_config.get("max_train_targets_per_user", 0),
stored_config.get("max_validation_targets_per_user", 0),
)
current_window_values = (
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
objective_changed = (
stored_filter_scope != config.negative_filter_scope
or stored_target_weighting != config.target_weighting
or stored_weight_values != current_weight_values
or stored_window_values != current_window_values
)
model.load_state_dict(payload["model_state"])
if "optimizer_state" in payload and not objective_changed:
optimizer.load_state_dict(payload["optimizer_state"])
for group in optimizer.param_groups:
group["lr"] = config.learning_rate
group["weight_decay"] = config.weight_decay
log(f"restored optimizer state from {resume_checkpoint}")
else:
log(
f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
)
if "generator_state" in payload and not objective_changed:
generator.set_state(payload["generator_state"])
start_epoch = int(payload["epoch"])
if start_epoch >= config.epochs:
raise RuntimeError(
f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
)
prior = {
str(name): float(value)
for name, value in payload.get("diagnostics", {}).items()
}
if objective_changed:
prior.pop("validation_loss", None)
log(
"BCE objective changed: filter "
f"{stored_filter_scope}->{config.negative_filter_scope}, "
"target weighting "
f"{stored_target_weighting}->{config.target_weighting}, "
f"target window {stored_window_values}->{current_window_values}; "
"recomputing the resume checkpoint's validation BCE"
)
if "validation_loss" not in prior:
prior.update(
evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
)
history.append(prior)
best_loss = float(prior["validation_loss"])
best_epoch = start_epoch
best_state = snapshot_model(model)
log(
f"resumed validation-BCE training: epoch={start_epoch} "
f"loss={best_loss:.6f}"
)
for epoch in range(start_epoch + 1, config.epochs + 1):
diagnostics = run_training_epoch(
model,
optimizer,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
item_correction,
known_positive_packed,
generator,
device,
epoch,
)
validation = evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
diagnostics.update(validation)
history.append(diagnostics)
current_state = snapshot_model(model)
if checkpoint_dir is not None:
save_epoch_checkpoint(
checkpoint_dir,
epoch,
current_state,
config,
diagnostics,
optimizer,
generator,
)
improved = (
diagnostics["validation_loss"]
< best_loss - config.early_stopping_min_delta
)
if improved:
best_loss = diagnostics["validation_loss"]
best_epoch = epoch
best_state = current_state
epochs_without_improvement = 0
if checkpoint_dir is not None:
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": current_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
checkpoint_dir / "best.pt",
)
else:
epochs_without_improvement += 1
log(
f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
f"validation_loss={diagnostics['validation_loss']:.5f} "
f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
f"train_targets={int(diagnostics['targets']):,} "
f"validation_targets={int(diagnostics['validation_targets']):,} "
f"train_seconds={diagnostics['seconds']:.1f} "
f"validation_seconds={diagnostics['validation_seconds']:.1f}"
)
release_accelerator_cache(device)
if epochs_without_improvement >= config.early_stopping_patience:
log(
f"early stopping after epoch={epoch}; "
f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
)
break
if best_state is None:
raise RuntimeError("training did not produce a valid checkpoint")
model.load_state_dict(best_state)
model.to(device).eval()
log(
f"restored best validation-BCE checkpoint: epoch={best_epoch} "
f"loss={best_loss:.6f}"
)
return model, history
@torch.inference_mode()
def inference_queries(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
device: torch.device,
) -> torch.Tensor:
sequence_rows = prepared.inference_user_rows
lengths = np.where(
sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
)
ordered = np.argsort(lengths, kind="stable")
result = torch.zeros(
len(sequence_rows), config.embedding_dim, dtype=torch.float32
)
for group_start in range(0, len(ordered), config.user_batch_size):
output_rows = ordered[group_start : group_start + config.user_batch_size]
rows = sequence_rows[output_rows]
group_lengths = lengths[output_rows]
hidden = torch.zeros(
1, len(output_rows), config.embedding_dim, device=device
)
for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
steps = min(
config.bptt_steps,
int(group_lengths.max(initial=0)) - start,
)
items = np.full(
(len(output_rows), steps),
len(prepared.item_ids),
dtype=np.uint16,
)
features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
counts = np.zeros(len(output_rows), dtype=np.int32)
for local_row, sequence_row in enumerate(rows):
if sequence_row < 0:
continue
count = min(steps, max(int(group_lengths[local_row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[sequence_row])
selected = slice(offset + start, offset + start + count)
items[local_row, :count] = prepared.sequence_items[selected]
features[local_row, :count] = prepared.event_features[selected]
counts[local_row] = count
outputs, _ = event_sequence(
model,
torch.from_numpy(items.astype(np.int64)).to(device),
torch.from_numpy(features.astype(np.float32)).to(device),
hidden,
)
next_rows: list[torch.Tensor] = []
for local_row, count in enumerate(counts):
next_rows.append(
outputs[local_row, count - 1]
if count
else hidden[0, local_row]
)
hidden = torch.stack(next_rows).unsqueeze(0)
liked_items = torch.from_numpy(
prepared.inference_liked_items[output_rows].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.inference_disliked_items[output_rows].astype(np.int64)
).to(device)
queries = user_queries(
model,
hidden.squeeze(0),
model._mean_context_channel(liked_items),
model._mean_context_channel(disliked_items),
torch.from_numpy(
prepared.inference_user_features[output_rows].astype(np.float32)
).to(device),
)
result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
return result
@torch.inference_mode()
def rank_candidates(
model: base.SequentialTwoTower,
prepared: SequenceData,
eligibility: CandidateSet,
config: Config,
device: torch.device,
ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
model.eval()
queries = inference_queries(model, prepared, config, device)
item_parts: list[torch.Tensor] = []
for start in range(0, len(prepared.item_ids), 2_048):
rows = torch.arange(
start,
min(start + 2_048, len(prepared.item_ids)),
dtype=torch.long,
device=device,
)
item_parts.append(model.encode_items(rows).cpu())
items = torch.cat(item_parts)
rows: list[tuple[int, int, int]] = []
for start in range(0, len(eligibility.uids), ranking_batch_size):
end = min(start + ranking_batch_size, len(eligibility.uids))
scores = queries[start:end] @ items.T
eligible = np.unpackbits(
eligibility.packed[start:end],
axis=1,
count=len(prepared.item_ids),
bitorder="little",
).astype(bool, copy=False)
scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
for local_row, columns in enumerate(selected):
uid = int(eligibility.uids[start + local_row])
rows.extend(
(uid, int(prepared.item_ids[column]), rank)
for rank, column in enumerate(columns, start=1)
)
if end == len(eligibility.uids) or end % 1_024 == 0:
log(f"ranked users={end:,}/{len(eligibility.uids):,}")
query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
diagnostics = {
"query_norm_mean": float(query_norm.mean()),
"query_norm_std": float(query_norm.std()),
"item_norm_mean": float(item_norm.mean()),
"item_norm_std": float(item_norm.std()),
}
return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
def write_ranking(frame: pd.DataFrame, output: Path) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
if output.suffix == ".parquet":
frame.to_parquet(output, index=False, compression="zstd")
elif output.suffix == ".csv":
frame.to_csv(output, index=False)
else:
raise ValueError("--output must end in .parquet or .csv")
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
## Step 17 · agent · 2026-09-29T05:37:28.032Z
### read_file · 2026-09-29T05:37:28.032Z
{
"end_line": 2165,
"start_line": 2001,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:37:28.037Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2001-2165 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2166.
--- FILE CONTENT (truncated) ---
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
if device.type == "mps" and not torch.backends.mps.is_available():
parser.error("MPS was requested but is unavailable")
started = time.monotonic()
prepared, eligibility = load_or_prepare(
public,
args.stage,
config,
args.prepared_cache.resolve() if args.prepared_cache else None,
)
target_partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
if args.prepare_only:
print(
json.dumps(
{
"stage": args.stage,
"sequence_users": len(prepared.user_ids),
"sequence_tokens": len(prepared.sequence_items),
"next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(
target_partition.window_starts.sum()
),
"target_users": len(eligibility.uids),
"runtime_seconds": round(time.monotonic() - started, 3),
"config": asdict(config),
},
indent=2,
)
)
return 0
model, training_history = train_model(
public,
args.stage,
prepared,
config,
device,
args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
)
ranking, embedding_diagnostics = rank_candidates(
model,
prepared,
eligibility,
config,
device,
args.ranking_batch_size,
)
write_ranking(ranking, args.output.resolve())
print(
json.dumps(
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": args.stage,
"output": str(args.output.resolve()),
"rows": len(ranking),
"users": int(ranking.uid.nunique()),
"runtime_seconds": round(time.monotonic() - started, 3),
"public_inputs_only": True,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": (
"every-adjacent-target-within-recent-chronological-window"
),
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": False,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": (
"24-uniform-plus-8-propensity-corrected-inbatch"
),
"false_negative_filter": config.negative_filter_scope,
"target_weighting": config.target_weighting,
},
"config": asdict(config),
"sequence_tokens": len(prepared.sequence_items),
"all_next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(target_partition.window_starts.sum()),
"training_history": training_history,
"embedding_diagnostics": embedding_diagnostics,
"torch_version": torch.__version__,
"device": str(device),
},
indent=2,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
## Step 18 · agent · 2026-09-29T05:37:30.997Z
### Recorded reasoning summary
Running Reproduction
I'm executing the reproduction command now to see the immediate outcome. This should give us a baseline before any further refinements.
### run_shell_command · 2026-09-29T05:37:30.997Z
{
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepare-only",
"description": "Test data preparation runtime for validation stage"
}
### Tool output · 2026-09-29T05:37:34.953Z
Output: usage: train_current_model.py [-h] [--stage {validation,submission}] --output
OUTPUT --public-dir PUBLIC_DIR
[--prepared-cache PREPARED_CACHE]
[--device {cpu,mps}] [--threads THREADS]
[--bptt-steps BPTT_STEPS]
[--user-batch-size USER_BATCH_SIZE]
[--epochs EPOCHS]
[--learning-rate LEARNING_RATE]
[--loss-microbatch-size LOSS_MICROBATCH_SIZE]
[--negative-filter-scope {full-training-history,last-100}]
[--target-weighting {discovery-graded,organic-strong,uniform}]
[--ordinary-target-weight ORDINARY_TARGET_WEIGHT]
[--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]
[--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]
[--discovery-horizon-days DISCOVERY_HORIZON_DAYS]
[--validation-fraction VALIDATION_FRACTION]
[--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]
[--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]
[--early-stopping-patience EARLY_STOPPING_PATIENCE]
[--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]
[--checkpoint-dir CHECKPOINT_DIR]
[--resume-checkpoint RESUME_CHECKPOINT]
[--seed SEED]
[--ranking-batch-size RANKING_BATCH_SIZE]
[--prepare-only]
train_current_model.py: error: the following arguments are required: --output
Exit Code: 2
Process Group PGID: 1639
## Step 19 · agent · 2026-09-29T05:37:37.854Z
### read_file · 2026-09-29T05:37:37.854Z
{
"end_line": 535,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 470
}
### Tool output · 2026-09-29T05:37:37.861Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 470-535 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 536.
--- FILE CONTENT (truncated) ---
def cache_metadata(stage: str, config: Config) -> dict[str, object]:
return {
"cache_version": CACHE_VERSION,
"stage": stage,
"positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
"profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
"event_time_feature": "exp-negative-gap-over-60-days",
}
def save_cache(
path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
np.savez(
path,
metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
**prepared.__dict__,
)
log(f"saved public-derived full-sequence cache: {path}")
def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
with np.load(path, allow_pickle=False) as artifact:
actual = json.loads(str(artifact["metadata"].item()))
expected = cache_metadata(stage, config)
if actual != expected:
raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
values = {
field: artifact[field].copy()
for field in SequenceData.__dataclass_fields__
}
prepared = SequenceData(**values)
log(
f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
f"targets={prepared.target_count:,}"
)
return prepared
def load_or_prepare(
public: Path,
stage: str,
config: Config,
cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
candidate_name = (
"validation_eligible_candidates.npz"
if stage == "validation"
else "test_eligible_candidates.npz"
)
eligibility = CandidateSet.read(public / "data" / candidate_name)
if cache is not None and cache.is_file():
prepared = load_cache(cache, stage, config)
if not np.array_equal(prepared.item_ids, eligibility.item_ids):
raise RuntimeError("cached catalog differs from candidate artifact")
return prepared, eligibility
prepared, built_eligibility = prepare_data(public, stage, config)
if cache is not None:
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
## Step 20 · agent · 2026-09-29T05:37:43.070Z
### Recorded reasoning summary
Exploring Cache Locations
I'm currently considering where to best store the prepared cache. The constraint to write deliverables and intermediate artifacts only under `/app/solution/` is key here, though I also need to evaluate if `/tmp` is a viable option for temporary caching to optimize performance and resource usage.
### run_shell_command · 2026-09-29T05:37:43.070Z
{
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --output /app/solution/production_validation.parquet",
"description": "Run baseline production model validation training and ranking"
}
### Tool output · 2026-09-29T05:43:49.163Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.5s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_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=198.4s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=211.5 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/production_validation.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 361.702,
"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": 0.5398380482933169,
"positive_logit": 0.7427799585458127,
"negative_logit": -0.6222247023150492,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 211.50029177400006,
"validation_loss": 0.49663621852386697,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.381582812999909
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497010231018,
"item_norm_mean": 1.7570278644561768,
"item_norm_std": 0.8279485106468201
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1650
## Step 21 · agent · 2026-09-29T05:43:54.305Z
### run_shell_command · 2026-09-29T05:43:54.305Z
{
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet",
"description": "Score baseline validation ranking"
}
### Tool output · 2026-09-29T05:43:57.689Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1710
## Step 22 · agent · 2026-09-29T05:44:00.316Z
### run_shell_command · 2026-09-29T05:44:00.316Z
{
"description": "Check contents and sizes in /app/solution",
"command": "ls -lh /app/solution/"
}
### Tool output · 2026-09-29T05:44:00.334Z
Output: total 512M
-rw-r--r-- 1 agent agent 1.2M Sep 29 05:43 production_validation.parquet
-rw-r--r-- 1 agent agent 511M Sep 29 05:38 validation_cache.npz
Process Group PGID: 1730
## Step 23 · agent · 2026-09-29T05:44:02.406Z
### run_shell_command · 2026-09-29T05:44:02.406Z
{
"command": "df -h /app",
"description": "Check disk space"
}
### Tool output · 2026-09-29T05:44:02.424Z
Output: Filesystem Size Used Avail Use% Mounted on
overlay 96G 15G 82G 15% /
Process Group PGID: 1733
## Step 24 · agent · 2026-09-29T05:44:39.526Z
### Recorded reasoning summary
Generating Baseline Submission
I'm currently focused on creating a temporary, valid baseline submission file immediately. My priority is to ensure this file is saved, adhering to the instruction to retain the best valid submission throughout the process and survive the deadline. I'm also considering the possibility of refitting on the `submission` stage.
### Recorded reasoning summary
Investigating Negative Sampling
My thoughts are now zeroed in on the trainer's negative sampling strategy. I'm dissecting how it selects and utilizes negative examples within the training objective to identify potential limitations. My immediate goal is to diagnose these issues before exploring improvements, all while keeping the CPU budget and public backtest validation in mind.
### Recorded reasoning summary
Refining Negative Sampling
I'm currently deep-diving into the specifics of negative example selection within the training objective. My focus is on understanding the existing mechanisms, their limitations, and potential code paths for improvement, always keeping the CPU budget and public backtest validation in sight.
### Recorded reasoning summary
Analyzing Loss Function
I'm analyzing the specific loss function being employed, particularly the implications of using sample-weighted binary cross-entropy. My current focus is on understanding if alternative objectives like softmax, InfoNCE, or BPR are viable and could potentially yield better results within the existing framework.
### Recorded reasoning summary
Exploring Negative Sampling Logic
I'm now delving into the specifics of `draw_full_history_filtered_negatives`, `corrected_negative_loss`, `score_target_microbatches`, and `build_item_correction`. My aim is to thoroughly understand the current negative sampling implementation and identify any associated limitations, ensuring all changes align with the CPU budget and public backtest validation.
### update_topic · 2026-09-29T05:44:39.526Z
{
"summary": "Baseline model reproduced with validation NDCG@10 of 0.02128. Sequence cache is saved. Now analyzing the negative sampling implementation, candidate generation, loss function, and filtering logic to diagnose bottlenecks and limitations.",
"strategic_intent": "Analyze the current negative sampling strategy and loss objective in detail to identify limitations.",
"title": "Diagnosing Negative Sampling and Training Objective"
}
### Tool output · 2026-09-29T05:44:39.539Z
Current topic: "Diagnosing Negative Sampling and Training Objective"
Topic summary: Baseline model reproduced with validation NDCG@10 of 0.02128. Sequence cache is saved. Now analyzing the negative sampling implementation, candidate generation, loss function, and filtering logic to diagnose bottlenecks and limitations.
Strategic Intent: Analyze the current negative sampling strategy and loss objective in detail to identify limitations.
### read_file · 2026-09-29T05:44:39.526Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 535,
"end_line": 725
}
### Tool output · 2026-09-29T05:44:39.539Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 535-725 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 726.
--- FILE CONTENT (truncated) ---
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""Estimate in-batch propensities from the selected training targets."""
counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
for user_row, count in enumerate(target_counts):
if not count:
continue
offset = int(prepared.offsets[user_row])
target_start = int(target_starts[user_row])
counts += np.bincount(
prepared.sequence_items[
offset
+ target_start
+ 1 : offset
+ target_start
+ int(count)
+ 1
].astype(np.int64),
minlength=len(prepared.item_ids),
)
total = float(counts.sum())
probabilities = (counts + 1.0) / (total + len(counts))
raw = np.power(
(1.0 / len(counts)) / probabilities,
config.inbatch_correction_power,
)
normalization = float(np.sum(counts * raw) / max(total, 1.0))
correction = np.clip(
raw / max(normalization, 1.0e-12),
config.inbatch_correction_min,
config.inbatch_correction_max,
).astype(np.float32)
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
log(
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
)
return torch.from_numpy(correction)
def build_known_positive_filter(
prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
"""Pack each user's complete public-training positive-item set."""
width = (len(prepared.item_ids) + 7) // 8
packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
retained = 0
for user_row, target_count in enumerate(training_counts):
if not target_count:
continue
offset = int(prepared.offsets[user_row])
# T adjacent targets consume sequence positions 0 through T. The
# chronological BCE suffix starts at T + 1 and is intentionally absent.
items = np.unique(
prepared.sequence_items[
offset : offset + int(target_count) + 1
].astype(np.int64)
)
byte_columns = items >> 3
bit_values = np.left_shift(
np.uint8(1), (items & 7).astype(np.uint8)
)
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
retained += len(items)
log(
f"full-history false-negative filter: users={len(prepared.user_ids):,} "
f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
)
return packed
def invalid_full_history_negatives(
negatives: np.ndarray,
targets: np.ndarray,
user_rows: np.ndarray,
known_positive_packed: np.ndarray,
liked_items: np.ndarray,
) -> np.ndarray:
"""Return row-level invalid negatives using the complete training prefix."""
byte_columns = negatives >> 3
bit_columns = negatives & 7
known = known_positive_packed[user_rows[:, None], byte_columns]
invalid = ((known >> bit_columns) & 1).astype(bool)
invalid |= negatives == targets[:, None]
invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= (
negatives[:, column, None] == negatives[:, :column]
).any(axis=1)
return invalid
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
def draw_training_negatives(
positive_items: torch.Tensor,
## Step 25 · agent · 2026-09-29T05:44:53.406Z
### Recorded reasoning summary
Analyzing Loss Function
I'm currently scrutinizing the `corrected_negative_loss` definition, paying close attention to lines 845-865 to understand its precise calculation.
### Recorded reasoning summary
Refining Loss Components
I'm now focusing on how the `corrected_negative_loss` integrates with `draw_full_history_filtered_negatives`, specifically examining the calculation of `corrected_count` and its implications for the loss distribution.
### Recorded reasoning summary
Investigating Sampling Strategy
I'm examining the negative sampling logic, particularly how `priorities` are masked and how `torch.topk` selects indices. It seems `unique_targets` represents all unique items in the batch, and I need to clarify the origin and scope of the `targets` variable used in this context.
### Recorded reasoning summary
Clarifying Logit Calculation
I'm reviewing the calculation of `positive_logits` and `negative_logits`, particularly within the `score_target_microbatches` function. I need to confirm if the current loss formulation equates to a Binary Cross-Entropy (BCE) loss, given the use of `F.softplus` on `positive_logits` and the subsequent aggregation with `negative_loss`.
### Recorded reasoning summary
Questioning Loss Averaging
I'm questioning the division by 2.0 in the loss calculation. It appears to be averaging positive and negative losses. I need to re-evaluate how this averaging impacts the effective weight of each negative example, particularly in the context of multiple negatives per positive.
### Recorded reasoning summary
Revising Negative Sampling
I'm re-evaluating the negative sampling strategy. The current approach uniformly samples from `unique_targets`, not proportionally to frequency, which might be a limitation. I need to investigate this further.
### Recorded reasoning summary
Revising In-Batch Sampling
I'm realizing the in-batch sampling from `unique_targets` is uniform, not proportional to frequency, despite subsequent `item_correction` implying otherwise. This discrepancy requires further investigation.
### grep_search · 2026-09-29T05:44:53.406Z
{
"pattern": "dislike",
"dir_path": "/task"
}
### Tool output · 2026-09-29T05:44:53.427Z
Found 40 matches for pattern "dislike" in path "/task":
---
File: current_model/train_current_model.py
L110: disliked_states: np.ndarray
L114: inference_disliked_items: np.ndarray
L176: active_dislikes: dict[int, int],
L179: disliked_states: list[np.ndarray],
L183: disliked_states.append(
L184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186: state_counts.append((len(active_likes), len(active_dislikes)))
L252: disliked_states: list[np.ndarray],
L279: active_dislikes: dict[int, int] = {}
L293: else active_dislikes
L304: active_dislikes,
L307: disliked_states,
L320: dislike_count: int,
L337: result[5] = np.log1p(dislike_count) / 4.0
L369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383: disliked_states = [
L384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405: disliked_states,
L427: int(dislike_counts[inference_row]),
L440: np.log1p(dislike_counts[missing]) / 4.0
L455: disliked_states=np.stack(disliked_states),
L459: inference_disliked_items=inference_dislikes,
L784: disliked_items = torch.from_numpy(
L785: prepared.disliked_states[unique_states].astype(np.int64)
L788: disliked_vectors = model._mean_context_channel(disliked_items)
L793: disliked_vectors[inverse_rows],
L802: disliked_vectors: torch.Tensor,
L807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159: disliked_vectors,
L1844: disliked_items = torch.from_numpy(
L1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851: model._mean_context_channel(disliked_items),
---
File: current_model/two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184: ("dislikes", 1, 1, 0),
L185: ("undislikes", 1, 0, 1),
L265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281: return liked, disliked, like_counts, dislike_counts
---
## Step 26 · agent · 2026-09-29T05:45:05.006Z
### Recorded reasoning summary
Analyzing Dislikes Data
I've realized that dislikes are solely an input feature for the user tower, specifically as `disliked_vectors`, and are not utilized as negative training examples. My next step is to thoroughly examine the columns within `train_listens.parquet` to understand all available data.
### read_file · 2026-09-29T05:45:05.006Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 240,
"end_line": 340
}
### Tool output · 2026-09-29T05:45:05.013Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 240-340 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 341.
--- FILE CONTENT (truncated) ---
(completion_sum - before_completion)
/ np.maximum(counts, 1)
/ 100.0
).astype(np.float16)
return result
def build_sequence_parts(
listens: base.ListenEvents,
explicit: base.ExplicitEvents,
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
items = listens.items[qualifying].astype(np.uint16, copy=False)
organic = listens.organic[qualifying].astype(np.float32, copy=False)
completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
if not len(items):
return None
event_features = np.zeros((len(items), 3), dtype=np.float16)
event_features[:, 0] = organic.astype(np.float16)
event_features[:, 1] = (
np.minimum(completion, 100.0) / 100.0
).astype(np.float16)
if len(items) > 1:
gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
event_features[1:, 2] = np.exp(
-gaps.astype(np.float64) / (60.0 * base.DAY)
).astype(np.float16)
activity = causal_activity_features(
timestamps, items, organic, completion
)
states = np.zeros(len(items), dtype=np.uint32)
active_likes: dict[int, int] = {}
active_dislikes: dict[int, int] = {}
explicit_position = 0
current_state = 0
for row, timestamp in enumerate(timestamps):
changed = False
while (
explicit_position < len(explicit.timestamps)
and int(explicit.timestamps[explicit_position]) < int(timestamp)
):
item = int(explicit.items[explicit_position])
event_timestamp = int(explicit.timestamps[explicit_position])
destination = (
active_likes
if int(explicit.channel[explicit_position]) == 0
else active_dislikes
)
if int(explicit.active[explicit_position]) == 1:
destination[item] = event_timestamp
else:
destination.pop(item, None)
explicit_position += 1
changed = True
if changed:
current_state = append_state_snapshot(
active_likes,
active_dislikes,
n_items,
liked_states,
disliked_states,
state_counts,
)
states[row] = np.uint32(current_state)
return timestamps, items.copy(), event_features, activity, states
def final_user_features(
timestamps: np.ndarray,
items: np.ndarray,
event_features: np.ndarray,
history_end: int,
like_count: int,
dislike_count: int,
) -> np.ndarray:
start = int(
np.searchsorted(
timestamps,
history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
)
result = np.zeros(6, dtype=np.float32)
recent_items = items[start:]
if len(recent_items):
result[0] = np.log1p(len(recent_items)) / 8.0
result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
result[2] = float(np.mean(event_features[start:, 0]))
result[3] = float(np.mean(event_features[start:, 1]))
result[4] = np.log1p(like_count) / 6.0
result[5] = np.log1p(dislike_count) / 4.0
return result
## Step 27 · agent · 2026-09-29T05:45:07.673Z
### read_file · 2026-09-29T05:45:07.673Z
{
"file_path": "/task/current_model/train_current_model.py",
"end_line": 470,
"start_line": 341
}
### Tool output · 2026-09-29T05:45:07.679Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 341-470 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 471.
--- FILE CONTENT (truncated) ---
def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
if n_items >= np.iinfo(np.uint16).max:
raise RuntimeError("uint16 item storage is too small for this catalog")
if stage == "validation":
prefixes = ("train",)
history_end = base.TRAIN_END
candidate_name = "validation_eligible_candidates.npz"
state_name = "validation_preference_state.parquet"
else:
prefixes = ("train", "val")
history_end = base.VALIDATION_END
candidate_name = "test_eligible_candidates.npz"
state_name = "test_preference_state.parquet"
eligibility = CandidateSet.read(data / candidate_name)
if not np.array_equal(eligibility.item_ids, item_ids):
raise RuntimeError("catalog and candidate artifact item order differ")
explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
inference_likes, inference_dislikes, like_counts, dislike_counts = (
base.load_inference_state(
data / state_name,
item_index,
eligibility.uids,
n_items,
)
)
inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
# Snapshot zero is the shared empty explicit state.
liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
disliked_states = [
np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
]
state_counts: list[tuple[int, int]] = [(0, 0)]
user_ids: list[int] = []
offsets = [0]
timestamp_parts: list[np.ndarray] = []
item_parts: list[np.ndarray] = []
event_parts: list[np.ndarray] = []
activity_parts: list[np.ndarray] = []
state_parts: list[np.ndarray] = []
listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
started = time.monotonic()
for seen_users, (uid, listens) in enumerate(
base.merge_listen_streams(listen_paths, item_index), start=1
):
parts = build_sequence_parts(
listens,
explicit_by_user.get(uid, base.empty_explicit_events()),
n_items,
liked_states,
disliked_states,
state_counts,
)
if parts is None:
continue
sequence_row = len(user_ids)
user_ids.append(uid)
timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
item_parts.append(parts[1])
event_parts.append(parts[2])
activity_parts.append(parts[3])
state_parts.append(parts[4])
offsets.append(offsets[-1] + len(parts[1]))
inference_row = inference_rows.get(uid)
if inference_row is not None:
inference_sequence_rows[inference_row] = sequence_row
inference_user_features[inference_row] = final_user_features(
parts[0],
parts[1],
parts[2],
history_end,
int(like_counts[inference_row]),
int(dislike_counts[inference_row]),
)
if seen_users % 1_000 == 0:
log(
f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
f"states={len(liked_states):,} "
f"elapsed={time.monotonic() - started:.1f}s"
)
missing = np.flatnonzero(inference_sequence_rows < 0)
if len(missing):
inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
inference_user_features[missing, 5] = (
np.log1p(dislike_counts[missing]) / 4.0
)
log(f"target users without qualifying catalog sequence={len(missing):,}")
prepared = SequenceData(
item_ids=item_ids,
item_features=base.build_item_features(catalog),
user_ids=np.asarray(user_ids, dtype=np.int64),
offsets=np.asarray(offsets, dtype=np.int64),
sequence_timestamps=np.concatenate(timestamp_parts),
sequence_items=np.concatenate(item_parts),
event_features=np.concatenate(event_parts),
activity_features=np.concatenate(activity_parts),
state_ids=np.concatenate(state_parts),
liked_states=np.stack(liked_states),
disliked_states=np.stack(disliked_states),
state_counts=np.asarray(state_counts, dtype=np.int32),
inference_user_rows=inference_sequence_rows,
inference_liked_items=inference_likes,
inference_disliked_items=inference_dislikes,
inference_user_features=inference_user_features,
)
log(
f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
f"tokens={len(prepared.sequence_items):,} "
f"next_item_targets={prepared.target_count:,} "
f"states={len(prepared.liked_states):,}"
)
return prepared, eligibility
## Step 28 · agent · 2026-09-29T05:46:15.252Z
### Recorded reasoning summary
Exploring Negative Sampling
I'm currently delving into the specifics of negative sampling, particularly examining the `draw_full_history_filtered_negatives` function. My focus is on understanding how training data and the sampling process interact.
### Recorded reasoning summary
Refining Negative Sampling Strategies
I'm now focusing on the mechanics of sampling, specifically the creation of uniform and in-batch negatives. I'm analyzing how `torch.randint` and `torch.unique` contribute to this process within the `draw_full_history_filtered_negatives` function.
### Recorded reasoning summary
Implementing Correction Logic
I'm now focused on integrating the `item_correction` into the generated `negatives`. My current thinking revolves around precisely applying these corrections to the in-batch negatives, especially when `corrected_count` is present, to ensure accuracy.
### Recorded reasoning summary
Validating Negative Data
I'm now examining the `invalid_full_history_negatives` function and comparing its logic with `build_known_positive_filter`. My primary focus is on how known positives are identified and used to filter out invalid negatives during training.
### Recorded reasoning summary
Understanding Sequence Boundaries
I'm now scrutinizing how sequence boundaries are defined. It appears the `training_counts` variable, derived from `chronological_target_partition`, dictates the length of sequences considered, impacting the items packed into `prepared.sequence_items` for processing.
### Recorded reasoning summary
Clarifying Sequence Construction
I'm refining my understanding of how sequences are built. It seems `chronological_target_partition` dictates sequence lengths using `history_training_counts`, impacting `prepared.sequence_items`. I'm now questioning if `prepared.sequence_items` reflects all user interactions, especially those not meeting completion thresholds or explicit likes.
### Recorded reasoning summary
Investigating Positive Identification
I'm now re-evaluating how positive interactions are defined. It seems explicit likes are considered positives, but the filtering logic in `invalid_full_history_negatives` only uses `liked_items`, potentially excluding disliked or unliked items from consideration as true negatives. I'm questioning how retrieval and evaluation criteria, particularly regarding organic behavior and candidate eligibility, align with this.
### Recorded reasoning summary
Validating Evaluation Splits
I'm now realizing a crucial point regarding candidate exclusion in evaluation splits. It appears validation candidates exclude all training encounters, and test candidates exclude both training and validation encounters, which is fundamental to discovery evaluation.
### Recorded reasoning summary
Analyzing Training Targets
I am currently examining how training targets are defined within `train_current_model.py`, specifically focusing on the `build_target_weights` function. My current analysis is centered on how repeat items within sequences are handled and their impact on target weights.
### Recorded reasoning summary
Revising Target Weighting
I'm revisiting target weighting, noting the baseline defaulted to "organic-strong" which doesn't downweight repeats or specifically boost grade-3 likes. I'm now questioning if "discovery-graded" is the intended weighting for the stated objective.
### Recorded reasoning summary
Reconsidering Training Weights
I'm now questioning the default "organic-strong" weighting, as it doesn't seem to align with downweighting repeated items or boosting grade-3 likes. I'm evaluating if "discovery-graded" is a more suitable approach given the stated objectives.
### Recorded reasoning summary
Validating In-Batch Negatives
I'm now examining the `corrected_negative_loss` function, specifically lines 845-865 in `train_current_model.py`. My focus is on how logits and correction factors are used to calculate the loss for both uniform and in-batch negatives.
### Recorded reasoning summary
Calculating Negative Loss
I am now analyzing the calculation within the `corrected_negative_loss` function, specifically how uniform and in-batch negative logits are averaged. My current focus is on confirming the weighted average calculation and its division by the total negative weight to determine the final loss per negative sample.
### Recorded reasoning summary
Adjusting Loss Calculation
I'm re-examining the loss calculation, specifically the averaging of negative losses. It appears the current implementation divides the sum of negative losses by the number of negatives (32), influencing the gradient updates for each negative sample. This deviates from a standard binary cross-entropy approach where each negative loss contributes independently.
### grep_search · 2026-09-29T05:46:15.252Z
{
"dir_path": "/task",
"pattern": "negative"
}
### Tool output · 2026-09-29T05:46:15.271Z
Found 100 matches for pattern "negative" in path "/task" (results limited to 100 matches for performance):
---
File: current_model/train_current_model.py
L14: and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
L56: uniform_negatives: int = 24
L57: inbatch_negatives: int = 8
L61: negative_filter_scope: str = "full-training-history"
L165: uniform_negatives=config.uniform_negatives,
L166: inbatch_negatives=config.inbatch_negatives,
L477: "event_time_feature": "exp-negative-gap-over-60-days",
L603: f"full-history false-negative filter: users={len(prepared.user_ids):,} "
L609: def invalid_full_history_negatives(
L610: negatives: np.ndarray,
L616: """Return row-level invalid negatives using the complete training prefix."""
L617: byte_columns = negatives >> 3
L618: bit_columns = negatives & 7
L621: invalid |= negatives == targets[:, None]
L622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
L623: for column in range(1, negatives.shape[1]):
L625: negatives[:, column, None] == negatives[:, :column]
L630: def draw_full_history_filtered_negatives(
L641: """Draw mixed negatives and reject all known public-training positives."""
L647: (batch_size, config.uniform_negatives),
L653: if config.inbatch_negatives:
L656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)
L672: if corrected_count < config.inbatch_negatives:
L675: (batch_size, config.inbatch_negatives - corrected_count),
L681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()
L682: correction = np.ones_like(negatives, dtype=np.float32)
L685: :, config.uniform_negatives : config.uniform_negatives + corrected_count
L688: invalid = invalid_full_history_negatives(
L689: negatives,
L697: torch.from_numpy(negatives).to(device),
L706: negatives[invalid] = replacements
L709: invalid = invalid_full_history_negatives(
L710: negatives,
L717: raise RuntimeError("full-history negative resampling failed")
L719: torch.from_numpy(negatives).to(device),
L724: def draw_training_negatives(
L736: if config.negative_filter_scope == "full-training-history":
L738: raise RuntimeError("full-history negative filter was not built")
L739: return draw_full_history_filtered_negatives(
L750: return base.draw_filtered_mixed_negatives(
L755: config.uniform_negatives,
L756: config.inbatch_negatives,
L816: negatives: torch.Tensor,
L818: all_items = torch.cat([positives, negatives.reshape(-1)])
L822: negative_vectors = encoded[inverse[len(positives) :]].reshape(
L823: len(positives), negatives.shape[1], -1
L825: return positive_vectors, negative_vectors
L845: def corrected_negative_loss(
L851: total = config.uniform_negatives + config.inbatch_negatives
L853: if config.uniform_negatives:
L854: result += config.uniform_negatives * raw[
L855: :, : config.uniform_negatives
L857: if config.inbatch_negatives:
L858: selected = slice(config.uniform_negatives, total)
L860: result += config.inbatch_negatives * (
L880: raise RuntimeError("chronological target partition produced negative counts")
L1119: negatives: torch.Tensor,
L1120: negative_weights: torch.Tensor,
L1134: "negative_sum": 0.0,
L1162: positive_vectors, negative_vectors = candidate_vectors(
L1165: negatives[begin:end],
L1168: negative_logits = torch.einsum(
L1169: "bd,bkd->bk", query_vectors, negative_vectors
L1172: negative_loss = corrected_negative_loss(
L1173: negative_logits, negative_weights[begin:end], config
L1179: selected_weights * (positive_loss + negative_loss) / 2.0
L1190: statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
L1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
L1227: negative_count = config.uniform_negatives + config.inbatch_negatives
L1232: total_negative = 0.0
L1293: negatives, negative_weights = draw_training_negatives(
L1314: negatives,
L1315: negative_weights,
L1331: total_negative += statistics["negative_sum"]
L1353: "negative_logit": total_negative / max(total_targets * negative_count, 1),
L1355: / max(total_targets * negative_count, 1),
L1449: negatives, negative_weights = draw_training_negatives(
L1469: negatives,
L1470: negative_weights,
L1567: if config.negative_filter_scope == "full-training-history"
L1601: "negative_filter_scope", "last-100"
L1629: stored_filter_scope != config.negative_filter_scope
L1660: f"{stored_filter_scope}->{config.negative_filter_scope}, "
L1944: "--negative-filter-scope",
L1946: default=Config.negative_filter_scope,
L2034: negative_filter_scope=args.negative_filter_scope,
L2136: "negative_sampling": (
L2139: "false_negative_filter": config.negative_filter_scope,
---
File: current_model/two_tower_model.py
L40: uniform_negatives: int = 24
L41: inbatch_negatives: int = 8
L346: def invalid_negative_mask(
L347: negatives: torch.Tensor,
L353: invalid = negatives.eq(targets[:, None])
L354: invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
L355: invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
L356: for column in range(1, negatives.shape[1]):
L357: invalid[:, column] |= negatives[:, column, None].eq(
L358: negatives[:, :column]
L363: def draw_filtered_mixed_negatives(
L374: """Draw uniform and unique-target in-batch negatives."""
---
## Step 29 · agent · 2026-09-29T05:47:03.476Z
### Recorded reasoning summary
Investigating Negative Sampling
My current focus is on understanding the mechanics of negative sampling and the training process itself, specifically scrutinizing the `invalid_full_history_negatives` function and its parameters.
### Recorded reasoning summary
Exploring Data Packing
I'm currently examining how positive items are packed into bytes for efficiency. My focus is on understanding the `build_known_positive_filter` function and the `packed` array it generates, specifically how it represents user positive item sets.
### Recorded reasoning summary
Analyzing User IDs
I am currently examining the `user_row` variable within the training loop, particularly its relationship to `prepared.user_ids` and how it's utilized by `invalid_full_history_negatives`. I need to clarify its scope and origin.
### Recorded reasoning summary
Clarifying User Row Indexing
I've pinpointed that the `users` variable, used for batching, actually represents indices into `train_users`, which in turn maps to `prepared.user_ids`. This clarifies how `flat_user_rows` indexes into `known_positive_packed`.
### Recorded reasoning summary
Revising Packed Data Interpretation
I'm re-evaluating my understanding of `known_positive_packed`. It seems to include targets beyond the immediate training history, which conflicts with my prior assumptions about its exclusive content. This suggests a need to carefully examine how validation targets are indexed and handled within this structure.
### Recorded reasoning summary
Refining Negative Selection
I'm now examining how `inbatch` negatives are drawn, particularly the logic in `draw_full_history_filtered_negatives`. The key insight is that items seen in the training prefix are filtered out from the negatives, influencing the selection process for validation targets.
### Recorded reasoning summary
Refining Negative Sample Generation
I'm now focusing on how in-batch negatives are finalized. Specifically, I'm analyzing the `torch.randint` calls used to fill up any remaining `inbatch` negatives after initial filtering and how the `correction` array is adjusted to account for these additional samples.
### Recorded reasoning summary
Adjusting Invalid Negatives
I've discovered that rejected in-batch negatives are replaced with uniform draws and their correction is set to 1.0. This is significant because popular items, frequently present in user histories, are more likely to be invalidated, affecting the sampling distribution.
### Recorded reasoning summary
Questioning Loss Functions
I am now questioning the core loss function being employed. Is it a standard Binary Cross-Entropy, or does it utilize a form of Softmax, such as Sampled Softmax, In-Batch Softmax, or InfoNCE, which would significantly alter the training paradigm?
### Recorded reasoning summary
Investigating Loss Formulation
I'm now questioning the loss function's suitability for large catalogs. Binary Cross-Entropy appears to be used, but its effectiveness in massive retrieval scenarios is suspect, and a 75% pair accuracy against random negatives suggests issues.
### Recorded reasoning summary
Reassessing Model Performance
I'm recalculating the model's effectiveness based on pair accuracy, realizing a 75% rate against random negatives implies the true positive is ranked very low, around 7500th out of 30,000. This explains the poor NDCG scores.
### Recorded reasoning summary
Evaluating Loss Logic
I am re-examining the Binary Cross-Entropy loss calculation. It appears the current formulation is too lenient, allowing a significant probability for negatives to be considered positive. This is problematic for retrieval where the positive must rank highest.
### Recorded reasoning summary
Revising BCE Loss Interpretation
I'm reassessing my interpretation of the BCE loss. The presence of `F.softplus` in `corrected_negative_loss` suggests it's not a standard BCE, but rather a form of margin-based loss where negatives are penalized based on their logits. This might explain the observed behavior.
### Recorded reasoning summary
Refining Loss Interpretation
I am now understanding that `F.softplus` in `corrected_negative_loss` indicates a margin-based loss, not standard BCE. This explains the lenient penalties on negatives and clarifies why the true positive might not consistently rank highest, especially against random negatives.
### Recorded reasoning summary
Questioning Negative Sample Counts
I'm now questioning the exact number and selection method of negative samples. It appears only 8 in-batch negatives are sampled per target, despite a much larger pool of distinct items available in the batch, and I need to understand why candidate vectors are computed individually for each target.
### Recorded reasoning summary
Analyzing Batch Negative Counts
I'm realizing that my understanding of in-batch negative counts was flawed. While only 8 negatives are sampled per target initially, the `score_target_microbatches` function processes these in a single microbatch due to its size. This means all unique positive and negative items are encoded and processed together. The subsequent calculation of logits involves a matrix multiplication between query vectors and the projected negative vectors, which are structured as `(B, K, D)`. This unified processing explains how the model considers a broader set of negatives within a single step, despite the initial sampling count.
### Recorded reasoning summary
Analyzing Loss Formulation Logic
I am now analyzing the `torch.einsum` operation, realizing it performs 32 dot products per query. I'm also scrutinizing the current loss function, particularly `F.softplus` for positive loss and the `corrected_negative_loss` with propensity weighting. My focus is on understanding its limitations compared to other objectives like Softmax/InfoNCE or pairwise losses.
### Recorded reasoning summary
Comparing Loss Functions
I am now comparing the current loss formulation to alternatives like BPR or margin ranking. The existing loss, unlike BCE, directly enforces the positive item's rank over negatives, which seems more appropriate.
### Recorded reasoning summary
Revising Correction Logic
I'm now re-examining the `build_item_correction` function and its use of `config.inbatch_correction`. This logic is applied to probabilities derived from item counts and total items, aiming to adjust for sampling bias, which aligns with techniques used in other large-scale recommendation models.
### Recorded reasoning summary
Investigating Config Parameters
I am now investigating the origin of the `inbatch_correction_power`, `inbatch_correction_min`, and `inbatch_correction_max` parameters, specifically how they are incorporated into the `corrected_negative_loss` function and their implications on the loss calculation.
### Recorded reasoning summary
Adjusting Weighting Logic
I'm realizing my interpretation of the `correction` factor's impact was reversed. It actually down-weights popular in-batch negatives and up-weights rare ones within the `corrected_negative_loss`, which seems counter-intuitive for retrieval tasks where popular items are often more relevant.
### Recorded reasoning summary
Questioning Correction Application
I am now questioning the application of the correction factor. My understanding was it adjusts for popularity bias, but in-batch negatives are sampled uniformly, not proportionally to popularity. This makes the correction's purpose unclear.
### run_shell_command · 2026-09-29T05:47:03.476Z
{
"description": "Analyze baseline prediction distribution and popularity",
"command": "python -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/production_validation.parquet')\nprint('Number of rows:', len(df))\nprint('Unique items predicted:', df.item_id.nunique())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_map = dict(zip(catalog.item_id, catalog.popularity_rank))\ndf['pop_rank'] = df.item_id.map(cat_map)\nprint('Average popularity rank of top-100 predictions:', df.pop_rank.mean())\nprint('Min pop rank:', df.pop_rank.min(), 'Max pop rank:', df.pop_rank.max())\ntop10 = df[df['rank'] <= 10]\nprint('Average popularity rank of top-10 predictions:', top10.pop_rank.mean())\nprint('Top 10 most frequently predicted items:')\nprint(df.item_id.value_counts().head(10))\n\""
}
### Tool output · 2026-09-29T05:47:05.118Z
Output: Number of rows: 845600
Unique items predicted: 2575
Average popularity rank of top-100 predictions: 2991.8002459791865
Min pop rank: 1 Max pop rank: 29992
Average popularity rank of top-10 predictions: 2597.0539025543994
Top 10 most frequently predicted items:
item_id
6896222 5089
7565403 4970
2055777 4647
8722394 4627
5010168 4572
5134208 4397
3968387 4160
8823268 4157
6414220 4155
8350060 4132
Name: count, dtype: int64
Process Group PGID: 1758
## Step 30 · agent · 2026-09-29T05:47:16.229Z
### Recorded reasoning summary
Discovering Model Predictability
I'm noticing a significant concentration in my predictions. Out of thousands of items and users, a very small subset of items is being repeatedly suggested, with a single item dominating over 60% of predictions. This indicates a potential bias or over-reliance on a few popular items.
### run_shell_command · 2026-09-29T05:47:16.229Z
{
"description": "Analyze ground truth validation items and their popularity",
"command": "python -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth, DATA\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_map = dict(zip(catalog.item_id, catalog.popularity_rank))\n\nall_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_items.append((item, grade, cat_map.get(item)))\n\ndf_truth = pd.DataFrame(all_items, columns=['item', 'grade', 'pop_rank'])\nprint('Total true positive instances in validation:', len(df_truth))\nprint('Unique true positive items:', df_truth.item.nunique())\nprint('Mean pop rank of true positives:', df_truth.pop_rank.mean())\nprint('Median pop rank of true positives:', df_truth.pop_rank.median())\nprint('Top 10 most frequent true positive items:')\nprint(df_truth.item.value_counts().head(10))\n\""
}
### Tool output · 2026-09-29T05:47:17.732Z
Output: Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1778
## Step 31 · agent · 2026-09-29T05:47:21.918Z
### run_shell_command · 2026-09-29T05:47:21.918Z
{
"command": "PYTHONPATH=/task python -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth, DATA\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_map = dict(zip(catalog.item_id, catalog.popularity_rank))\n\nall_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_items.append((item, grade, cat_map.get(item)))\n\ndf_truth = pd.DataFrame(all_items, columns=['item', 'grade', 'pop_rank'])\nprint('Total true positive instances in validation:', len(df_truth))\nprint('Unique true positive items:', df_truth.item.nunique())\nprint('Mean pop rank of true positives:', df_truth.pop_rank.mean())\nprint('Median pop rank of true positives:', df_truth.pop_rank.median())\nprint('Quantiles of pop rank: 25%:', df_truth.pop_rank.quantile(0.25), '75%:', df_truth.pop_rank.quantile(0.75))\nprint('Top 10 most frequent true positive items:')\nprint(df_truth.item.value_counts().head(10))\n\"",
"description": "Analyze ground truth validation items and their popularity"
}
### Tool output · 2026-09-29T05:47:25.386Z
Output: Total true positive instances in validation: 37769
Unique true positive items: 14124
Mean pop rank of true positives: 8463.087743916969
Median pop rank of true positives: 5622.0
Quantiles of pop rank: 25%: 1733.0 75%: 13374.0
Top 10 most frequent true positive items:
item
6399259 173
6252104 109
8250570 88
2055777 83
1265696 77
3134183 60
7579671 51
1005397 46
4948220 45
6625048 43
Name: count, dtype: int64
Process Group PGID: 1789
## Step 32 · agent · 2026-09-29T05:47:53.449Z
### Recorded reasoning summary
Analyzing True Positives
I'm seeing encouraging signs in the true positive discoveries. The mean popularity rank is 8,463, with a median of 5,622. The 75th percentile sits at 13,374, and I've identified 14,124 unique true positive items so far, significantly outpacing the baseline model's 2,5 prediction.
### Recorded reasoning summary
Investigating Baseline Bias
My focus has shifted to understanding the baseline's peculiar preference for highly popular items. It appears the `build_item_features` function might be contributing to this by heavily weighting popularity metrics, potentially limiting its scope.
### Recorded reasoning summary
Refining Feature Engineering
I'm now scrutinizing how features are generated and scaled. It seems features like `log1p(positives)` and `popularity_rank` are heavily influenced by highly popular items, potentially skewing the model. I'm re-evaluating the `build_item_features` function.
**Examining Training Dynamics**
My attention has turned to the training process itself. Specifically, I'm investigating the contrast between the overwhelmingly unpopular nature of uniformly sampled negatives and the popular items dominating positive targets. This disparity seems significant.
### Recorded reasoning summary
Adjusting Loss Correction
I've identified a critical issue in how in-batch negatives are weighted and corrected. The current method heavily penalizes popular items and disproportionately boosts unpopular ones within the loss calculation, leading to skewed gradients. I'm exploring adjustments to this correction mechanism.
### Recorded reasoning summary
Correcting Loss Weighting
I've pinpointed a critical flaw in how loss is calculated. It appears my current setup heavily penalizes unpopular in-batch negatives while barely touching popular ones, leading to a learned bias towards recommending all popular items, which explains test-time performance.
### Recorded reasoning summary
Revising Negative Sampling
I'm now digging into how negative examples are selected and utilized by the training objective. The current strategy of using 24 uniform and 8 in-batch negatives feels insufficient, and I'm exploring whether increasing the count or improving the quality of these negatives is key.
### Recorded reasoning summary
Exploring Negative Sampling Strategies
I'm now considering alternative approaches for selecting negative examples, moving beyond uniform sampling. I'm investigating popularity-biased sampling and the inclusion of hard negatives, such as disliked items from user histories, to improve training.
### Recorded reasoning summary
Evaluating Loss Functions
I'm now exploring alternative loss functions to address the observed bias. I'm considering Sampled Softmax or InfoNCE with appropriate temperature scaling. My current focus is on incorporating logQ correction to mitigate sampling bias, aiming to improve gradient behavior during training.
### Recorded reasoning summary
Comparing Loss Functions
I'm analyzing how Sampled Softmax contrasts with BCE. Sampled Softmax's direct competition and amplified gradients for any negative outscoring the positive seem more potent than BCE's weaker signal, especially when many negatives barely beat the positive within an average.
### Recorded reasoning summary
Testing Loss Functions
I'm currently comparing the behavior of Sampled Softmax and BCE. Sampled Softmax's amplified gradients for harder negatives appear more impactful than BCE's subtler signal, especially given how many negatives barely outperform positives in my current setup.
### Recorded reasoning summary
Analyzing Inference Dot-Products
I've realized my current training is not aligned with the raw dot-product inference. The model needs to be trained to output logits such that their dot-product directly reflects the desired scoring, considering the constraints on inference.
### Recorded reasoning summary
Revising Training Objective
I'm re-evaluating the core training objective to ensure it aligns with inference. The goal is to train the model to output logits whose dot-product directly reflects the desired scoring, respecting the serving contract and ranking invariance due to monotonic transformations.
### grep_search · 2026-09-29T05:47:53.449Z
{
"dir_path": "/task/current_model",
"pattern": "uniform_negatives"
}
### Tool output · 2026-09-29T05:47:53.467Z
Found 12 matches for pattern "uniform_negatives" in path "/task/current_model":
---
File: train_current_model.py
L56: uniform_negatives: int = 24
L165: uniform_negatives=config.uniform_negatives,
L647: (batch_size, config.uniform_negatives),
L685: :, config.uniform_negatives : config.uniform_negatives + corrected_count
L755: config.uniform_negatives,
L851: total = config.uniform_negatives + config.inbatch_negatives
L853: if config.uniform_negatives:
L854: result += config.uniform_negatives * raw[
L855: :, : config.uniform_negatives
L858: selected = slice(config.uniform_negatives, total)
L1227: negative_count = config.uniform_negatives + config.inbatch_negatives
---
File: two_tower_model.py
L40: uniform_negatives: int = 24
---
## Step 33 · agent · 2026-09-29T05:48:04.407Z
### Recorded reasoning summary
Discovering Hardcoded Values
I've found that `uniform_negatives` and `inbatch_negatives` aren't exposed as command-line arguments in `train_current_model.py` as I initially assumed. Instead, they are fixed within the `Config` class, set to 24 and 8 respectively, with `inbatch_correction_power` at 0.5. This significantly changes my approach to parameter tuning.
### Recorded reasoning summary
Examining In-Batch Negatives
I'm now focused on understanding the role and implementation of `inbatch_negatives`. The code indicates it dynamically selects negatives from the current batch based on unique targets, with a `corrected_count` determined by the `inbatch_negatives` configuration. The `inbatch_correction_min` and `inbatch_correction_max` parameters also appear relevant to this process.
### Recorded reasoning summary
Refining Negative Sampling
I'm digging into the in-batch negative sampling logic. It appears to select negatives from unique targets within the batch, capped by `corrected_count`. I'm also observing how filler negatives are added if the dynamic selection falls short of the configured `inbatch_negatives` value.
### Recorded reasoning summary
Investigating Negative Rejection
I'm examining the loop that handles invalid negatives. It appears to replace invalid items, identified by historical data, with random selections. Notably, rejected in-batch items are then treated as ordinary uniform draws, impacting the `correction` array.
### run_shell_command · 2026-09-29T05:48:04.407Z
{
"description": "Check inbatch negative behavior and replacement rate",
"command": "python -c \"\nimport numpy as np\nimport torch\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task/current_model')\nfrom train_current_model import load_cache, Config, chronological_target_partition, build_known_positive_filter, build_item_correction, build_dense_block, draw_full_history_filtered_negatives\n\nconfig = Config()\nprepared = load_cache(Path('/app/solution/validation_cache.npz'), 'validation', config)\npartition = chronological_target_partition(prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user)\nfilter_packed = build_known_positive_filter(prepared, partition.history_training_counts)\nitem_correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts)\n\n# Let's inspect a typical batch\nusers = np.arange(64)\nblock = build_dense_block(prepared, np.ones(len(prepared.sequence_items)), users, partition.window_starts[users], partition.training_counts[users], np.zeros(64, dtype=int), 0, 100, config)\nflat_scored = block[5].reshape(-1)\npositive_items = torch.from_numpy(block[4].reshape(-1)[flat_scored].astype(np.int64))\nflat_user_rows = np.broadcast_to(users[:, None], block[5].shape).reshape(-1)[flat_scored]\nliked_items = torch.from_numpy(prepared.liked_states[block[3].reshape(-1)[flat_scored]].astype(np.int64))\n\ngenerator = torch.Generator().manual_seed(42)\nprint('Scored targets in batch:', len(positive_items))\nprint('Unique targets in batch:', len(torch.unique(positive_items)))\n\nnegatives, correction = draw_full_history_filtered_negatives(\n positive_items, flat_user_rows, liked_items, filter_packed, len(prepared.item_ids), config, item_correction, generator, torch.device('cpu')\n)\nprint('Negatives shape:', negatives.shape)\nprint('Correction shape:', correction.shape)\nprint('Correction for inbatch negatives (cols 24..31):')\ninbatch_corr = correction[:, 24:].numpy()\nprint('Fraction of inbatch corrections equal to 1.0 (meaning replaced by uniform or default):', (inbatch_corr == 1.0).mean())\nprint('Min inbatch corr:', inbatch_corr.min(), 'Max inbatch corr:', inbatch_corr.max())\n\""
}
### Tool output · 2026-09-29T05:48:09.702Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
Scored targets in batch: 5644
Unique targets in batch: 3484
Negatives shape: torch.Size([5644, 32])
Correction shape: torch.Size([5644, 32])
Correction for inbatch negatives (cols 24..31):
Fraction of inbatch corrections equal to 1.0 (meaning replaced by uniform or default): 0.06400602409638555
Min inbatch corr: 0.25 Max inbatch corr: 4.0
Process Group PGID: 1817
## Step 34 · agent · 2026-09-29T05:48:18.765Z
### Recorded reasoning summary
Investigating Loss Calculation
I'm currently examining the loss calculations, specifically focusing on the 6.4% of replacements completed. My attention has turned to `corrected_negative_loss` and its relationship with BCE loss, while also scrutinizing the use of BCE within `train_current_model.py` and the operations in `score_target_microbatches`.
### Recorded reasoning summary
Analyzing Loss Components
My current focus is on dissecting the `positive_loss` derived from `F.softplus(-positive_logits)` and its interplay with the `corrected_negative_loss`. I'm also investigating the averaging and summation within `loss_sum`, especially how `selected_weights` influence the overall calculation.
### Recorded reasoning summary
Investigating Gradient Discrepancy
I've identified a significant gradient discrepancy. My analysis shows the gradient for positives is approximately 0.5, while for negatives, it's capped at 0.0156, a factor of 32 difference. This is impacting my understanding of the loss dynamics.
### run_shell_command · 2026-09-29T05:48:18.765Z
{
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport torch\n\n# Let's inspect the baseline model item embeddings\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Top 5 catalog items by popularity:')\nprint(catalog.head(5)[['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']])\nprint('Bottom 5 catalog items:')\nprint(catalog.tail(5)[['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']])\n\"",
"description": "Check top and bottom catalog items"
}
### Tool output · 2026-09-29T05:48:22.689Z
Output: Top 5 catalog items by popularity:
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
Bottom 5 catalog items:
item_id positive_listens organic_positive_listens popularity_rank
29995 1275692 163 81 29996
29996 1280011 163 76 29997
29997 1309246 163 138 29998
29998 1634958 163 46 29999
29999 1670524 163 73 30000
Process Group PGID: 1831
## Step 35 · agent · 2026-09-29T05:48:26.139Z
### Recorded reasoning summary
Analyzing Baseline Predictions
My focus has shifted to dissecting the baseline run. I'm currently examining the top 10 most frequent predictions from `production_validation.parquet` and investigating their popularity ranks and positive listen counts.
### run_shell_command · 2026-09-29T05:48:26.139Z
{
"description": "Check popularity of top predicted items",
"command": "python -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntop_pred_items = [6896222, 7565403, 2055777, 8722394, 5010168, 5134208, 3968387, 8823268, 6414220, 8350060]\nprint(catalog[catalog.item_id.isin(top_pred_items)][['item_id', 'popularity_rank', 'positive_listens', 'organic_positive_listens']])\n\""
}
### Tool output · 2026-09-29T05:48:27.697Z
Output: item_id popularity_rank positive_listens organic_positive_listens
75 8823268 76 8956 7255
305 5134208 306 5028 3633
317 3968387 318 4914 3402
633 8722394 634 3464 1695
817 6896222 818 3020 2017
1169 2055777 1170 2465 1558
1528 8350060 1529 2115 1153
2061 7565403 2062 1763 1152
2946 5010168 2947 1385 1228
3206 6414220 3207 1305 612
Process Group PGID: 1848
## Step 36 · agent · 2026-09-29T05:49:11.085Z
### Recorded reasoning summary
Investigating Negative Sampling
My focus has shifted to a deep dive into the negative sampling and loss function mechanics. I'm particularly scrutinizing how the training objective identifies and leverages these negative examples, aiming to pinpoint any inherent limitations.
### Recorded reasoning summary
Refining Negative Sampling
I'm currently dissecting the training objective's handling of negative examples, specifically examining pointwise BCE against alternatives like InfoNCE or pairwise methods. My goal is to identify weaknesses in the current approach and enhance it within the CPU constraint, ensuring ranking performance is maintained while adhering to deployment parameters.
### Recorded reasoning summary
Exploring Loss Functions
I'm delving into the nuances of Softmax-based loss functions like InfoNCE. I'm particularly interested in how they inherently handle hard negatives due to their exponential gradient weighting, contrasting this with the current approach. I'm also investigating the impact of the number of uniform versus in-batch negatives.
### Recorded reasoning summary
Analyzing In-Batch Negatives
I'm scrutinizing the selection of in-batch negatives. Given a batch size of 64 and up to 100 timesteps, yielding around 3,000-5,000 targets, I'm questioning why only 8 in-batch items are being chosen by `draw_full_history_filtered_negatives`. I'm currently examining the code snippet provided to understand this limitation.
### Recorded reasoning summary
Investigating In-Batch Negatives
I'm questioning why only 8 in-batch negatives are being drawn despite a large pool of potential negatives available from the batch. I'm investigating the logic behind this fixed number and its implications for the sampling strategy, contrasting it with typical two-tower approaches.
### Recorded reasoning summary
Optimizing Item Encoding
I've identified a significant bottleneck in item encoding. With a substantial number of unique items generated from negatives, the item MLP is processing an excessive amount, contributing to long epoch times. I’m now exploring strategies to optimize this encoding process and its impact on retrieval performance.
### Recorded reasoning summary
Validating Experimental Setup
I'm confirming our testing strategy, noting the pre-cached validation set and the swift ~2-3 minute per experiment runtime. Baseline analysis shows a ~4-minute total for training and ranking, which seems quite reasonable for exploration. I am re-reading the problem statement to ensure alignment.
### Recorded reasoning summary
Analyzing Sampling Parameters
I am now examining the specific parameters used in the `train_current_model.py` script, focusing on the number of uniform and in-batch negatives, along with the various correction and weighting factors. I'm trying to understand the rationale behind these choices, particularly the fixed number of in-batch negatives (8), and how they interact with the overall training objective.
### Recorded reasoning summary
Investigating Target Weighting
I'm focusing on the `--target-weighting discovery-graded` option. I'm reviewing the implementation details within `train_current_model.py` to understand how "discovery-graded" weighting is applied and how it integrates with the `load_organic_like_outcomes` function and horizon calculations.
### Recorded reasoning summary
Clarifying Target Splits
I'm examining the logic for splitting training and validation targets based on user history and timestamps. I'm specifically analyzing how repeated items are handled and their assigned weights, ensuring the distinction between ordinary and discovery-graded targets is maintained correctly.
### Recorded reasoning summary
Clarifying Target Weights
I'm now scrutinizing the `discovery-graded` target weighting. Specifically, I'm analyzing why it wasn't the default and how the weights of 3.0, 1.0, and 0.25 are applied based on different user engagement metrics. I'm examining the logic for determining these grades.
### Recorded reasoning summary
Evaluating Target Weighting
I am now investigating the impact of `--target-weighting discovery-graded` versus the default `organic-strong`, focusing on how different user engagement metrics influence weight assignments. I'm clarifying the logic behind the 3.0, 1.0, and 0.25 weight grades.
### Recorded reasoning summary
Analyzing Negative Sampling Logic
I am currently reviewing the `draw_training_negatives` function and its interaction with `draw_full_history_filtered_negatives`. My focus is on understanding how specific negative examples are selected and filtered based on the `negative_filter_scope` configuration parameter.
### Recorded reasoning summary
Filtering Negatives Further
I'm now examining `invalid_negative_mask` to understand its filtering logic for negatives. It appears to be removing items already present in targets, context items, liked items, and even duplicates within the negatives themselves. My current focus is on how this interacts with the `negative_filter_scope` setting.
### Recorded reasoning summary
Examining Negative Selection
I'm now looking at how `uniform` and `inbatch` negatives are selected within `draw_full_history_filtered_negatives`. My current task is to understand the logic that determines which specific negative examples are drawn, given the available pool and configuration parameters.
### Recorded reasoning summary
Understanding In-Batch Negative Draw
I'm investigating the `draw_full_history_filtered_negatives` function and observed that `inbatch` is initialized as an empty tensor, only populated if `config.inbatch_negatives` is true. This suggests the number of in-batch negatives isn't dynamically adjusted to the available pool but rather by a configuration parameter.
### Recorded reasoning summary
Filtering In-Batch Negatives
I'm scrutinizing the priority masking within `draw_full_history_filtered_negatives`. It seems designed to prevent selecting targets of the *current* row as in-batch negatives, but I need to confirm if it adequately handles cases where another user's target in the batch is already in the current user's history.
### Recorded reasoning summary
Revising In-Batch Negative Sampling
I've uncovered a critical flaw in the in-batch negative sampling implementation. It's not leveraging the natural popularity bias of batch targets as expected, but rather sampling uniformly. This fundamentally undermines the intended benefit of in-batch negatives for recommendation systems.
### Recorded reasoning summary
Correcting In-Batch Sampling
I've identified a critical flaw: uniform sampling from unique targets negates the inherent popularity bias needed for effective in-batch negatives. The current `item_correction` logic, intended to debias, doesn't account for this initial uniform sampling error, leading to an incorrect probability distribution.
### Recorded reasoning summary
Adjusting In-Batch Corrections
I'm recalibrating the in-batch negative correction factors. My current analysis reveals that when popular items have low raw probabilities, they receive a disproportionately high correction factor (4.0), while rare items get a low one (0.25). This seems counterintuitive and requires adjustment to better align with expected behavior.
### Recorded reasoning summary
Correcting Weighting Logic
I've discovered a significant issue where uniform sampling of negatives, particularly popular items, is being severely downweighted. This is counteracted by upweighting rare items, leading the model to favor popular tracks and hindering genuine discovery. I am now focused on fixing this problematic weighting mechanism.
### Recorded reasoning summary
Fixing Weighting Logic
I'm correcting a significant issue where popular negative items were heavily downweighted by uniform sampling, while rare items were upweighted. This flawed weighting mechanism hindered discovery. I am now implementing changes to ensure a more balanced and effective training process.
### Recorded reasoning summary
Revising Loss Function
I'm now shifting my focus from negative sampling mechanics to the core training objective. I'm exploring how various loss functions like InfoNCE, sampled softmax, and BPR should leverage negative examples, considering factors like popularity bias, hard negatives, and the use of temperature.
### run_shell_command · 2026-09-29T05:49:11.085Z
{
"command": "cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py",
"description": "Copy model code to /app/solution"
}
### Tool output · 2026-09-29T05:49:11.109Z
Output: (empty)
Process Group PGID: 1871
## Step 37 · agent · 2026-09-29T05:49:36.073Z
### Recorded reasoning summary
Testing Training Objective
I'm currently exploring modifications to the training objective and negative sampling strategies within `train_current_model.py`. The goal is to assess their impact via testing.
### Recorded reasoning summary
Evaluating Training Loss
My recent thoughts have focused on the efficiency of running quick experiments. I've noted that a single epoch with current settings is around four minutes, making rapid iteration feasible. I'm now considering specific hypotheses for the `corrected_negative_loss` function, particularly regarding the downweighting of popular negatives and exploring InfoNCE/Sampled Softmax as potential alternatives to BCE.
### Recorded reasoning summary
Refining Negative Sampling
I'm currently exploring a correction to sampled softmax, specifically the log-Q correction from Yi et al. (2019). My focus is on how this correction interacts with popular negatives, especially when using popularity as the sampling probability. I'm investigating its implications for the positive term as well.
### Recorded reasoning summary
Analyzing Logit Bias
I've realized that by subtracting its training logit, the model doesn't need an artificially high dot product for popular items during training. This means the raw dot product at inference accurately reflects true affinity, free from popularity bias. Without this correction, BCE was actually learning high dot products for popular items.
**Exploring Negative Sampling Scales**
I'm now considering variations on InfoNCE and Softmax, including those with in-batch negatives and log-Q corrections. I'm also investigating the impact of increasing the number of negatives, looking at how the current encoding time scales with larger negative sets and if using all unique in-batch targets as negatives is feasible.
### Recorded reasoning summary
Optimizing Unique Target Encoding
I've discovered a significant optimization: encoding only ~3,500 unique targets per batch, rather than the previous ~15,000. This is because in-batch negatives mean all unique targets are already represented, drastically reducing encoding time by a factor of four. This approach also aligns with strategies for balancing selection bias and catalog coverage.
### Recorded reasoning summary
Investigating Batch Size
I'm questioning the current ratio of uniform to in-batch negatives, wondering if increasing in-batch negatives or using popularity-biased samples would be more effective. I'm considering testing InfoNCE directly on the existing sampling setup to isolate the loss function's impact.
### Recorded reasoning summary
Adjusting Temperature Scale
I'm now considering the optimal temperature scaling for InfoNCE. The current dot product range suggests values around 0.05 to 0.2 could significantly impact logits. I'm exploring a fixed $\tau=0.1$ and the possibility of a learnable temperature parameter.
### Recorded reasoning summary
Investigating Log-Q Correction
I'm now diving into the specifics of the log-Q correction as presented in Yi et al. (2019) for sampled softmax, considering its potential application within an InfoNCE framework. My current focus is on how incorporating $-\log p_i$ directly into the logits, particularly with temperature scaling, might influence performance and mitigate popularity bias.
### update_topic · 2026-09-29T05:49:36.073Z
{
"summary": "Diagnosed the core issue: the current model uses a pointwise BCE loss where negative gradients are diluted (divided by 64), popular negatives are severely downweighted (loss weight 0.25), and there is no direct ranking competition. This causes the model to predict only ~2,500 popular items for all users. We will now test targeted improvements starting with discovery-graded weighting and InfoNCE/ranking loss objectives.",
"title": "Testing Loss Objectives and Negative Sampling",
"strategic_intent": "Run quick experiments with target weighting and loss functions on the validation backtest."
}
### Tool output · 2026-09-29T05:55:00.050Z
Current topic: "Testing Loss Objectives and Negative Sampling"
Topic summary: Diagnosed the core issue: the current model uses a pointwise BCE loss where negative gradients are diluted (divided by 64), popular negatives are severely downweighted (loss weight 0.25), and there is no direct ranking competition. This causes the model to predict only ~2,500 popular items for all users. We will now test targeted improvements starting with discovery-graded weighting and InfoNCE/ranking loss objectives.
Strategic Intent: Run quick experiments with target weighting and loss functions on the validation backtest.
### run_shell_command · 2026-09-29T05:49:36.073Z
{
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp1_discovery_graded.parquet && \\\npython /task/evaluate_public.py /app/solution/exp1_discovery_graded.parquet",
"description": "Test baseline with discovery-graded target weighting"
}
### Tool output · 2026-09-29T05:55:00.050Z
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=196.1s
epoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.2 validation_seconds=15.5
restored best validation-BCE checkpoint: epoch=1 loss=0.504831
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp1_discovery_graded.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 316.261,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.542115418840845,
"positive_logit": 0.7774311445323794,
"negative_logit": -0.6407562046595957,
"pair_accuracy": 0.7602795423917307,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.22850921400004,
"validation_loss": 0.5048305718913402,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.475705923000078
}
],
"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/exp1_discovery_graded.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 1875
## Step 38 · agent · 2026-09-29T05:55:10.425Z
### Recorded reasoning summary
Discovering Improved NDCG
I've observed a promising baseline enhancement with `discovery-graded`, pushing NDCG@10 to 0.023522. While this is a good start, my focus is now shifting towards optimizing negative sampling strategies and refining the loss function to further elevate performance beyond the current 0.0235 mark.
### run_shell_command · 2026-09-29T05:55:10.425Z
{
"command": "python -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/exp1_discovery_graded.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_map = dict(zip(catalog.item_id, catalog.popularity_rank))\ndf['pop_rank'] = df.item_id.map(cat_map)\nprint('Unique items predicted:', df.item_id.nunique())\nprint('Mean pop rank:', df.pop_rank.mean())\n\"",
"description": "Check distribution of exp1"
}
### Tool output · 2026-09-29T05:55:12.036Z
Output: Unique items predicted: 2930
Mean pop rank: 3232.9024917218544
Process Group PGID: 1962
## Step 39 · agent · 2026-09-29T05:55:36.731Z
### Recorded reasoning summary
Analyzing Loss Function
I'm currently delving into the specifics of InfoNCE and Cross-Entropy loss within `score_target_microbatches`. My focus is on understanding its precise implementation and effect on negative sampling in `/app/solution/train_current_model.py`.
### Recorded reasoning summary
Correcting Logits
I'm re-evaluating the logits calculation. It appears I overlooked the necessity of incorporating log-Q, or propensity, corrections when dealing with sampled softmax. My next step is to accurately compute and apply these corrections to the negative logits as per standard practice for sampled softmax.
### Recorded reasoning summary
Refining Logit Calculation
I'm realizing the log-Q correction's impact on logits for sampled softmax. It seems subtracting log-Q actually suppresses the scores of popular items, which is precisely what's needed to disentangle true affinity from inherent popularity bias during training. This should lead to more accurate affinity estimations at inference.
**Investigating Negative Sampling**
I'm now examining the sampling strategy for in-batch negatives within `draw_full_history_filtered_neg`. I need to understand if the current method of drawing more in-batch negatives or sampling them proportionally to frequency aligns with the log-Q correction's goal of mitigating popularity bias effectively.
### Recorded reasoning summary
Revising Negative Sampling
I've identified a critical flaw: `unique_targets` was previously creating duplicates, leading to uniform, not popularity-weighted, in-batch negative sampling. I'm now rectifying this to ensure more accurate bias mitigation.
### run_shell_command · 2026-09-29T05:55:36.731Z
{
"command": "python -c \"\nimport torch\nimport time\nimport sys\nsys.path.insert(0, '/app/solution')\nimport two_tower_model as base\n\ncatalog_features = torch.randn(30000, 4)\nconfig = base.TowerConfig()\nmodel = base.SequentialTwoTower(catalog_features.numpy(), config)\n\nB = 5000\npositives = torch.randint(0, 30000, (B,))\n\nfor K in [32, 64, 128]:\n negatives = torch.randint(0, 30000, (B, K))\n all_items = torch.cat([positives, negatives.reshape(-1)])\n t0 = time.time()\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n pos_vec = encoded[inverse[: len(positives)]]\n neg_vec = encoded[inverse[len(positives) :]].reshape(len(positives), K, -1)\n q = torch.randn(B, 192)\n pos_logits = (q * pos_vec).sum(dim=1)\n neg_logits = torch.einsum('bd,bkd->bk', q, neg_vec)\n t1 = time.time()\n print(f'K={K}: unique_items={len(unique_items)}, time={t1-t0:.4f}s')\n\"",
"description": "Benchmark candidate encoding time vs K"
}
### Tool output · 2026-09-29T05:55:42.051Z
Output: K=32: unique_items=29862, time=0.3472s
K=64: unique_items=29999, time=0.3962s
K=128: unique_items=30000, time=0.4619s
Process Group PGID: 1981
## Step 40 · agent · 2026-09-29T05:59:39.471Z
### Recorded reasoning summary
Exploring Encoding Efficiency
My recent findings indicate remarkable efficiency even at larger negative sample sizes. With B=5000, increasing K to 128 negatives per target only marginally increases processing time to 0.46s per batch. This suggests promising scalability for candidate encoding and logit generation.
### Recorded reasoning summary
Evaluating Loss Functions
My focus has shifted to comparing BCE with InfoNCE. I'm setting up a test to directly evaluate InfoNCE performance, specifically examining its implementation within `score_target_microbatches` and considering changes to the loss function in `/app/solution/train_current_model.py`.
### Recorded reasoning summary
Refining Loss Calculations
I'm investigating the implementation of InfoNCE loss, specifically exploring how the temperature parameter `tau` should be set by examining typical dot product values from the user and item towers. This aims to optimize the loss function for better model performance.
### Recorded reasoning summary
Investigating Output Normalization
I've observed a lack of LayerNorm or L2-normalization at the output of the user and item towers. This leads to specific norms for query and item vectors, impacting dot products and potentially causing large logit differences with small `tau` values. I'm considering adding normalization.
### Recorded reasoning summary
Analyzing Temperature Scaling
I'm now considering the impact of different temperature (`tau`) values, exploring options like 0.1, 0.2, and 0.5, and whether it should be learnable or a CLI argument. My attention is also turning to the core negative sampling strategy itself, specifically how it selects and utilizes these examples to identify potential limitations.
### Recorded reasoning summary
Examining Negative Sampling
I'm now scrutinizing the exact methodology for selecting negative examples, specifically diving into the `draw_full_history_filtered_negatives` function. I'm analyzing how uniform and in-batch negatives are drawn and filtered, paying close attention to how known positives are excluded.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm now focused on the precise implementation of `draw_full_history_filtered_negatives`. I've been examining how unique targets are handled to ensure the correct count of in-batch negatives, and I'm scrutinizing the logic for filling remaining negative slots with random items when necessary.
### Recorded reasoning summary
Clarifying Negative Sampling Weighting
I'm analyzing the `draw_full_history_filtered_negatives` function's use of `torch.unique(targets)`. This deduplication means popular items have an equal chance of being selected as negatives as unpopular ones, potentially hindering the model's ability to learn to avoid recommending overly popular items.
### Recorded reasoning summary
Investigating Sampling Probabilities
I'm re-evaluating how in-batch negatives are sampled. If sampled proportionally from `targets_cpu`, tracks appearing frequently as positives would also appear more frequently as negatives, creating a more balanced learning signal than uniform random sampling. This could significantly impact how effectively the model learns to distinguish between relevant and irrelevant items.
### run_shell_command · 2026-09-29T05:59:39.471Z
{
"description": "Inspect train_dislikes.parquet",
"command": "python -c \"\nimport pandas as pd\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Dislikes shape:', dislikes.shape)\nprint('Unique users with dislikes:', dislikes.uid.nunique())\nprint('Unique items disliked:', dislikes.item_id.nunique())\nprint(dislikes.head(5))\n\""
}
### Tool output · 2026-09-29T05:59:41.020Z
Output: Dislikes shape: (101075, 4)
Unique users with dislikes: 5732
Unique items disliked: 50876
uid timestamp item_id is_organic
0 100 3087560 9170134 1
1 100 3936560 8661238 1
2 100 20432825 4927727 1
3 400 2980800 399811 1
4 400 3157520 1575108 1
Process Group PGID: 2025
## Step 41 · agent · 2026-09-29T06:01:46.504Z
### Recorded reasoning summary
Analyzing Dislike Distribution
I'm currently digging into the dislike data. It appears dislikes aren't evenly spread, with a concentration in certain areas, suggesting issues beyond individual user preferences, like catalog or popularity-driven negatives. I'm also examining item correction data to understand its relationship.
### Recorded reasoning summary
Investigating _negative_loss_
I'm revisiting the `_negative_loss` calculation, specifically focusing on the `build_item_correction` function. My current line of inquiry is into how the item correction probabilities are derived from the observed counts and the total number of items, aiming to pinpoint potential issues in this formulation.
### Recorded reasoning summary
Refining Propensity Calculation
I'm scrutinizing how in-batch propensities are computed. Specifically, I'm analyzing the relationship between item target counts, total items, and the `inbatch_correction_power` parameter in the `build_item_correction` function to understand its impact on the resulting `raw` values.
### Recorded reasoning summary
Adjusting Logit Weighting
I'm realizing the baseline's multiplication of BCE loss with a correction factor amplifies issues. Popular items get their negative loss reduced, while rare items see it drastically increased, a stark contrast to the logit adjustment in sampled softmax, which I now understand significantly alters item influence.
### Recorded reasoning summary
Correcting Sampling Misinterpretation
I've discovered a critical conceptual inversion. The baseline incorrectly multiplied negative loss by a correction factor derived from sampling probability ($1/Q_i$), instead of applying it to the logit as in Google's paper. This significantly altered item influence, contrary to importance sampling principles.
### Recorded reasoning summary
Revising Loss Calculation
I'm rethinking the loss function's core. It appears uniform sampling was applied instead of proportional, and the negative loss was normalized incorrectly. Downweighting popular items as negatives is problematic, as they're the hardest to differentiate, leading to models favoring them universally.
### Recorded reasoning summary
Implementing InfoNCE Loss
I'm now focused on integrating the InfoNCE loss. My plan is to first test this in a small prototype script to verify the mechanics before implementing it directly within `train_current_model.py`, ensuring correct calculation of logits for both positive and negative items.
### Recorded reasoning summary
Integrating InfoNCE Components
I'm now thoroughly examining the integration of InfoNCE loss components. My immediate focus is on how `pair_correct` and statistics within `score_target_microbatches` interact with the calculated `loss_sum`, `positive_sum`, `negative_sum`, and `weight_sum`, and I'm cross-referencing this with `evaluate_validation_bce` to ensure all facets are accounted for.
### Recorded reasoning summary
Adjusting Temperature Scaling
I'm currently exploring the impact of the temperature parameter $\tau$ on the InfoNCE loss. Varying its value significantly alters gradients, and I'm investigating optimal ranges, considering scenarios from strong positive/negative separation to near-equal scores, to ensure effective model training.
### Recorded reasoning summary
Sampling Negatives Differently
I'm rethinking how negatives are sampled within batches. Instead of uniform random priorities from unique targets, I'm considering sampling directly from the batch targets or proportionally to catalog popularity. This might offer a more nuanced approach to selecting informative negatives.
### Recorded reasoning summary
Optimizing Negative Sampling
I've discovered a highly efficient method for in-batch negative sampling that directly leverages batch frequency, bypassing costly operations. I'm now testing variations in the number of in-batch versus uniform negatives and will proceed to experiment with replacing BCE loss with InfoNCE loss using a $\tau$ of 0.1.
### read_file · 2026-09-29T06:01:46.504Z
{
"start_line": 1140,
"file_path": "/app/solution/train_current_model.py",
"end_line": 1210
}
### Tool output · 2026-09-29T06:01:46.511Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1140-1210 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1211.
--- FILE CONTENT (truncated) ---
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
def run_training_epoch(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
## Step 42 · agent · 2026-09-29T06:04:35.020Z
### Recorded reasoning summary
Refining Loss Configuration
I'm considering adding `loss_type` and `temperature` to the `Config` dataclass to allow for flexible loss function selection and temperature scaling. My next step is to trace all instances of `Config` usage within the `train_current_model.py` script to ensure seamless integration.
### Recorded reasoning summary
Integrating Loss Parameters
I've confirmed that the existing validation cache will remain valid as the new loss-related `Config` parameters aren't serialized there. My focus is now on verifying how `score_target_microbatches` uses these new parameters, ensuring smooth functionality.
### replace · 2026-09-29T06:04:35.020Z
{
"file_path": "/app/solution/train_current_model.py",
"allow_multiple": false,
"new_string": " negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n loss_type: str = \"infonce\"\n temperature: float = 0.1\n ordinary_target_weight: float = 0.25",
"old_string": " negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25",
"instruction": "Add loss_type and temperature to Config and implement InfoNCE loss in score_target_microbatches"
}
### Tool error · 2026-09-29T06:04:35.029Z
EACCES: permission denied, open '/app/solution/train_current_model.py'
## Step 43 · agent · 2026-09-29T06:04:43.669Z
### run_shell_command · 2026-09-29T06:04:43.669Z
{
"command": "ls -la /app/solution",
"description": "Check permissions in /app/solution"
}
### Tool output · 2026-09-29T06:04:43.688Z
Output: total 525228
drwxr-xr-x 1 agent agent 4096 Sep 29 05:54 .
drwxr-xr-x 1 agent agent 4096 Sep 29 05:33 ..
-rw-r--r-- 1 agent agent 1249069 Sep 29 05:54 exp1_discovery_graded.parquet
-rw-r--r-- 1 agent agent 1244230 Sep 29 05:43 production_validation.parquet
-r--r--r-- 1 agent agent 80058 Sep 29 05:49 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 05:49 two_tower_model.py
-rw-r--r-- 1 agent agent 535225808 Sep 29 05:38 validation_cache.npz
Process Group PGID: 2072
## Step 44 · agent · 2026-09-29T06:04:47.317Z
### run_shell_command · 2026-09-29T06:04:47.317Z
{
"command": "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py",
"description": "Make copied scripts writable in /app/solution"
}
### Tool output · 2026-09-29T06:04:47.335Z
Output: (empty)
Process Group PGID: 2075
## Step 45 · agent · 2026-09-29T06:05:03.781Z
### replace · 2026-09-29T06:05:03.781Z
{
"new_string": " negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n loss_type: str = \"infonce\"\n temperature: float = 0.1\n ordinary_target_weight: float = 0.25",
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add loss_type and temperature to Config",
"old_string": " negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25",
"allow_multiple": false
}
### Tool output · 2026-09-29T06:05:03.802Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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"
loss_type: str = "infonce"
temperature: float = 0.1
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
...
## Step 46 · agent · 2026-09-29T06:05:07.606Z
### read_file · 2026-09-29T06:05:07.606Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1195,
"start_line": 1165
}
### Tool output · 2026-09-29T06:05:08.908Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1165-1195 of 2167 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1196.
--- FILE CONTENT (truncated) ---
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
## Step 47 · agent · 2026-09-29T06:05:13.335Z
### read_file · 2026-09-29T06:05:13.335Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 2050,
"start_line": 1950
}
### Tool output · 2026-09-29T06:05:13.341Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1950-2050 of 2167 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2051.
--- FILE CONTENT (truncated) ---
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
## Step 48 · agent · 2026-09-29T06:05:18.941Z
### Recorded reasoning summary
Implementing Configuration Options
I'm currently focused on integrating new command-line arguments, specifically `--loss-type` and `--temperature`, into our argument parser. These will then be supported within the `Config` object. My immediate priority is updating the `score_target_microbatches` functionality to accommodate these changes.
### replace · 2026-09-29T06:05:18.941Z
{
"new_string": " positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_type == \"infonce\":\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n scaled_logits = all_logits / config.temperature\n targets_idx = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n sample_loss = F.cross_entropy(scaled_logits, targets_idx, reduction=\"none\")\n loss_sum = (selected_weights * sample_loss).sum()\n else:\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"instruction": "Compute InfoNCE loss when config.loss_type == 'infonce' in score_target_microbatches",
"old_string": " positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"file_path": "/app/solution/train_current_model.py",
"allow_multiple": false
}
### Tool output · 2026-09-29T06:05:18.959Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
if config.loss_type == "infonce":
all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)
scaled_logits = all_logits / config.temperature
targets_idx = torch.zeros(len(positive_logits), dtype=torch.long, device=device)
sample_loss = F.cross_entropy(scaled_logits, targets_idx, reduction="none")
loss_sum = (selected_weights * sample_loss).sum()
else:
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
...
## Step 49 · agent · 2026-09-29T06:05:26.722Z
### replace · 2026-09-29T06:05:26.722Z
{
"new_string": " parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--loss-type\",\n choices=(\"infonce\", \"bce\"),\n default=Config.loss_type,\n )\n parser.add_argument(\n \"--temperature\",\n type=float,\n default=Config.temperature,\n )",
"instruction": "Add --loss-type and --temperature arguments to main()",
"old_string": " parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )",
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:05:26.740Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--loss-type",
choices=("infonce", "bce"),
default=Config.loss_type,
)
parser.add_argument(
"--temperature",
type=float,
default=Config.temperature,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
...
## Step 50 · agent · 2026-09-29T06:05:30.090Z
### read_file · 2026-09-29T06:05:30.090Z
{
"end_line": 2070,
"file_path": "/app/solution/train_current_model.py",
"start_line": 2040
}
### Tool output · 2026-09-29T06:05:30.096Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2040-2070 of 2184 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2071.
--- FILE CONTENT (truncated) ---
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
if device.type == "mps" and not torch.backends.mps.is_available():
## Step 51 · agent · 2026-09-29T06:05:34.574Z
### replace · 2026-09-29T06:05:34.574Z
{
"new_string": " negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n loss_type=args.loss_type,\n temperature=args.temperature,\n ordinary_target_weight=args.ordinary_target_weight,",
"old_string": " negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,",
"file_path": "/app/solution/train_current_model.py",
"allow_multiple": false,
"instruction": "Pass loss_type and temperature to Config in main()"
}
### Tool output · 2026-09-29T06:05:34.589Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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,
loss_type=args.loss_type,
temperature=args.temperature,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
...
## Step 52 · agent · 2026-09-29T06:05:41.078Z
### Recorded reasoning summary
Checking model imports
I'm examining `train_current_model.py` to confirm where the `two_tower_model` is being sourced from. It appears to be imported as `base`, and I'm currently verifying the exact path it's referencing.
### run_shell_command · 2026-09-29T06:05:41.078Z
{
"description": "Run experiment with InfoNCE loss (tau=0.1) and evaluate",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --loss-type infonce \\\n --temperature 0.1 \\\n --output /app/solution/exp2_infonce_01.parquet && \\\npython /task/evaluate_public.py /app/solution/exp2_infonce_01.parquet"
}
### Tool output · 2026-09-29T06:11:05.426Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=2.882234 running_bce=2.888013 elapsed=196.7s
epoch=1/1 train_loss=2.87533 validation_loss=2.66998 pair_accuracy=0.775 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.7 validation_seconds=15.8
restored best validation-BCE checkpoint: epoch=1 loss=2.669981
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp2_infonce_01.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 316.706,
"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",
"loss_type": "infonce",
"temperature": 0.1,
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 2.8753293551465733,
"positive_logit": 0.013731765006966084,
"negative_logit": -0.1270103591336814,
"pair_accuracy": 0.7748257460209526,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 209.6856498139996,
"validation_loss": 2.669981021485753,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.780993051999758
}
],
"embedding_diagnostics": {
"query_norm_mean": 0.6981536149978638,
"query_norm_std": 0.19272513687610626,
"item_norm_mean": 0.8023920655250549,
"item_norm_std": 0.17788256704807281
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp2_infonce_01.parquet",
"scores": {
"quality_ndcg@10": 0.018216,
"quality_users": 4263
}
}
Process Group PGID: 2084
## Step 53 · agent · 2026-09-29T06:22:48.005Z
### Recorded reasoning summary
Investigating Temperature Effects
I'm analyzing the significant performance drop observed with InfoNCE at $\tau = 0.1$. My current focus is understanding why the norms of the query and item embeddings appear to have shrunk, which seems correlated with the decreased score. I'm examining the associated logits for further clues.
### Recorded reasoning summary
Revisiting Training Objectives
I'm re-evaluating the training objectives, specifically why BCE is outperforming InfoNCE. The recent insight suggests the temperature scaling in InfoNCE might be causing gradient issues, leading to embedding shrinkage, which then impacts performance. I'm now focused on dissecting this interaction.
### Recorded reasoning summary
Analyzing Negative Sampling
My focus has shifted to understanding precisely how the current trainer selects and utilizes negative examples within its training objective. I'm meticulously examining functions like `build_item_correction`, `draw_full_history_filtered_negatives`, and `draw_training_negatives` to diagnose limitations in the current negative-sampling strategy. The goal is to propose improvements that fit within the established CPU budget.
### Recorded reasoning summary
Exploring In-Batch Correction
I'm now scrutinizing the `build_item_correction` function. My goal is to understand how it estimates in-batch propensities by analyzing its use of `target_counts` and `prepared.sequence_items`. I'm particularly interested in the calculation of `probabilities` and the subsequent `raw` power transformation, aiming to link this to potential training objective impacts.
### Recorded reasoning summary
Investigating Correction Calculations
I'm examining the `build_item_correction` function, specifically its normalization and clipping steps. I'm also looking into how `draw_full_history_filtered_negatives` handles known positives to ensure sampling efficiency.
### Recorded reasoning summary
Refining Negative Sampling
I'm diving deeper into the negative sampling logic, specifically how the code prioritizes and selects in-batch negatives based on randomness and avoidance of true positives. This detailed analysis aims to optimize efficiency and effectiveness within the existing CPU constraints.
### Recorded reasoning summary
Refining Negative Sampling Strategies
I'm analyzing the logic for generating negative samples, specifically how it incorporates both uniform and in-batch negatives, and then applies corrections. My current focus is on how the code handles filling remaining negative slots when in-batch negatives are insufficient and how it addresses invalid samples.
### Recorded reasoning summary
Analyzing Correction Application
I'm examining how the system handles rejected in-batch negatives by replacing them with uniform draws and resetting their correction factor. I'm also looking at the `corrected_negative_loss` function to understand how it applies these corrections to the softplus output, aiming to pinpoint potential issues in the loss calculation.
### Recorded reasoning summary
Revisiting Loss Calculation
I'm re-examining the `corrected_negative_loss` function and specifically the `weights` variable. It appears `weights` is being set to `correction[:, selected]`, which is then used to weight the `raw` values, where `raw` is actually the output of `F.softplus`. This suggests a potential misunderstanding of how corrections are being applied.
### Recorded reasoning summary
Adjusting Loss Weighting
I've discovered an inversion in how corrections are applied within the `corrected_negative_loss` function. It appears the calculated correction factors are being used as weights, but in a manner that amplifies the loss for less popular items and diminishes it for popular ones, contrary to the intended effect.
### Recorded reasoning summary
Revising Weighting Logic
I'm reassessing the loss weighting within the `corrected_negative_loss` function. The observation suggests an inversion where less popular items are amplified and popular ones diminished, contrary to the goal of propensity weighting. My focus is on correcting this to align with the intended inverse relationship between item popularity and weight.
### Recorded reasoning summary
Correcting Loss Inversion
I've identified a critical inversion in how loss is weighted. Instead of downweighting popular items when they're sampled as negatives, the current logic amplifies them. My immediate focus is to invert this weighting mechanism to correctly penalize popular items in negative samples, aligning with the intent of in-batch correction.
### replace · 2026-09-29T06:22:48.005Z
{
"new_string": "def corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n return raw.mean(dim=1)",
"allow_multiple": false,
"old_string": "def corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total",
"instruction": "Set loss_type default to bce and test unweighted negative loss in corrected_negative_loss",
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:22:48.022Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
logits: torch.Tensor,
correction: torch.Tensor,
config: Config,
) -> torch.Tensor:
raw = F.softplus(logits)
return raw.mean(dim=1)
def retained_target_window_starts(
total_targets: np.ndarray,
validation_counts: np.ndarray,
...
## Step 54 · agent · 2026-09-29T06:22:52.495Z
### run_shell_command · 2026-09-29T06:22:52.495Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --loss-type bce \\\n --output /app/solution/exp3_bce_unweighted.parquet && \\\npython /task/evaluate_public.py /app/solution/exp3_bce_unweighted.parquet",
"description": "Run experiment with unweighted negative BCE loss"
}
### Tool output · 2026-09-29T06:28:20.269Z
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=0.563095 running_bce=0.563300 elapsed=198.1s
epoch=1/1 train_loss=0.56103 validation_loss=0.52015 pair_accuracy=0.763 train_targets=1,052,953 validation_targets=130,103 train_seconds=211.1 validation_seconds=15.2
restored best validation-BCE checkpoint: epoch=1 loss=0.520147
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp3_bce_unweighted.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 320.086,
"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",
"loss_type": "bce",
"temperature": 0.1,
"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": 0.5610289522722313,
"positive_logit": 0.6264490667548376,
"negative_logit": -0.6609028545055078,
"pair_accuracy": 0.7632027201119138,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 211.13066360600033,
"validation_loss": 0.5201468392721145,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.211160614000164
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.8937625885009766,
"query_norm_std": 0.849906325340271,
"item_norm_mean": 1.7925578355789185,
"item_norm_std": 0.7955362796783447
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp3_bce_unweighted.parquet",
"scores": {
"quality_ndcg@10": 0.015049,
"quality_users": 4263
}
}
Process Group PGID: 2227
## Step 55 · agent · 2026-09-29T06:29:55.860Z
### Recorded reasoning summary
Analyzing Score Drop
My current focus is understanding the significant score drop observed after implementing `raw.mean(dim=1)`. I'm investigating the internal mechanisms of `corrected_negative_loss` and how the `correction` and `weights` are being affected by this change, as this seems to be the root cause of the performance degradation.
### run_shell_command · 2026-09-29T06:29:55.860Z
{
"command": "python -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/exp3_bce_unweighted.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_map = dict(zip(catalog.item_id, catalog.popularity_rank))\ndf['pop_rank'] = df.item_id.map(cat_map)\nprint('Unique items predicted:', df.item_id.nunique())\nprint('Mean pop rank:', df.pop_rank.mean())\n\"",
"description": "Check distribution of exp3"
}
### Tool output · 2026-09-29T06:29:57.474Z
Output: Unique items predicted: 3066
Mean pop rank: 3535.2990444654683
Process Group PGID: 2317
## Step 56 · agent · 2026-09-29T06:30:16.167Z
### Recorded reasoning summary
Analyzing Correction Logic
I'm currently delving into the rationale behind the `correction` mechanism. My focus is on scrutinizing the `build_item_correction` function to understand its specific role in shaping the probability calculations.
### Recorded reasoning summary
Refining Correction Logic
I'm currently examining how the `build_item_correction` function impacts probability calculations, specifically focusing on the `raw` values. I'm also investigating the implications of different `inbatch_correction_power` values and how they interact with the `counts` derived from selected training targets.
### Recorded reasoning summary
Investigating Negative Sampling
I'm exploring the implications of varying `uniform_negatives` and `inbatch_negatives` counts, and how different distributions might affect training. I'm also scrutinizing the `draw_full_history_filtered_negatives` function for a deeper understanding of in-batch negative sampling strategies.
### Recorded reasoning summary
Clarifying Target Definitions
I'm re-examining the distinction between `unique_targets` and `targets_cpu` within the `draw_full_history_filtered_negatives` function. My current focus is on understanding the precise nature and dimensionality of `targets_cpu`, which appears to represent target items from a significant number of users and timesteps, to clarify its role in the sampling process.
### Recorded reasoning summary
Filtering Invalid Negatives
I'm analyzing how invalid negatives are handled, particularly after identifying that `known_positive_packed` effectively masks all previously interacted items for a user. This seems to be the primary mechanism preventing sampling of already known positives, which is crucial.
### Recorded reasoning summary
Analyzing Correction Logic
I am now focusing on the transformation of rejected in-batch items and the nuanced differences between uniform and in-batch negatives. I'm also investigating the role of `inbatch_correction_power` in weighting the loss based on item rarity, noting how it shifts focus towards rarer items.
### Recorded reasoning summary
Understanding In-Batch Negatives
I'm exploring how in-batch negatives, items listened to by other users in the batch, provide more valuable training signals than purely random negatives. These provide niche signals specific to other users' tastes, helping the model learn finer distinctions crucial for music discovery.
### Recorded reasoning summary
Exploring Negative Sampling Ratios
I'm investigating how different ratios of in-batch versus uniform negatives might impact training. Specifically, I'm considering scenarios with equal numbers, more in-batch negatives, or entirely in-batch negatives, while ensuring the `corrected_negative_loss` mechanism remains intact.
### replace · 2026-09-29T06:30:16.167Z
{
"new_string": "def corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total",
"allow_multiple": false,
"instruction": "Restore original corrected_negative_loss in train_current_model.py",
"file_path": "/app/solution/train_current_model.py",
"old_string": "def corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n return raw.mean(dim=1)"
}
### Tool output · 2026-09-29T06:30:16.184Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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,
...
## Step 57 · agent · 2026-09-29T06:30:22.494Z
### Recorded reasoning summary
Exploring Configuration Options
I'm currently investigating the feasibility of introducing CLI arguments for `--uniform-negatives`, `--inbatch-negatives`, and `--inbatch-correction-power`. My immediate focus is on integrating these into the `Config` structure.
### read_file · 2026-09-29T06:30:22.494Z
{
"start_line": 1940,
"end_line": 2070,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:30:22.501Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1940-2070 of 2186 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2071.
--- FILE CONTENT (truncated) ---
parser.add_argument("--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(
"--loss-type",
choices=("infonce", "bce"),
default=Config.loss_type,
)
parser.add_argument(
"--temperature",
type=float,
default=Config.temperature,
)
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,
loss_type=args.loss_type,
temperature=args.temperature,
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)
## Step 58 · agent · 2026-09-29T06:30:26.772Z
### Recorded reasoning summary
Adding Training Parameters
I'm integrating new parameters into the argument parser and configuration for `/app/solution/train_current_model.py`. This will allow for finer control over negative sampling and its correction during training.
### replace · 2026-09-29T06:30:26.772Z
{
"new_string": " parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--uniform-negatives\",\n type=int,\n default=Config.uniform_negatives,\n )\n parser.add_argument(\n \"--inbatch-negatives\",\n type=int,\n default=Config.inbatch_negatives,\n )\n parser.add_argument(\n \"--inbatch-correction-power\",\n type=float,\n default=Config.inbatch_correction_power,\n )\n parser.add_argument(\n \"--inbatch-correction-min\",\n type=float,\n default=Config.inbatch_correction_min,\n )\n parser.add_argument(\n \"--inbatch-correction-max\",\n type=float,\n default=Config.inbatch_correction_max,\n )",
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add negative sampling options to parser and config in main()",
"allow_multiple": false,
"old_string": " parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )"
}
### Tool output · 2026-09-29T06:30:26.799Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--uniform-negatives",
type=int,
default=Config.uniform_negatives,
)
parser.add_argument(
"--inbatch-negatives",
type=int,
default=Config.inbatch_negatives,
)
parser.add_argument(
"--inbatch-correction-power",
type=float,
default=Config.inbatch_correction_power,
)
parser.add_argument(
"--inbatch-correction-min",
type=float,
default=Config.inbatch_correction_min,
)
parser.add_argument(
"--inbatch-correction-max",
type=float,
default=Config.inbatch_correction_max,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
...
## Step 59 · agent · 2026-09-29T06:30:30.363Z
### read_file · 2026-09-29T06:30:30.363Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 2070,
"end_line": 2100
}
### Tool output · 2026-09-29T06:30:30.369Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2070-2100 of 2211 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2101.
--- FILE CONTENT (truncated) ---
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,
loss_type=args.loss_type,
temperature=args.temperature,
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()
## Step 60 · agent · 2026-09-29T06:30:33.585Z
### replace · 2026-09-29T06:30:33.585Z
{
"instruction": "Pass uniform_negatives and inbatch parameters to Config in main()",
"file_path": "/app/solution/train_current_model.py",
"allow_multiple": false,
"old_string": " negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,",
"new_string": " negative_filter_scope=args.negative_filter_scope,\n uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n inbatch_correction_power=args.inbatch_correction_power,\n inbatch_correction_min=args.inbatch_correction_min,\n inbatch_correction_max=args.inbatch_correction_max,\n target_weighting=args.target_weighting,"
}
### Tool output · 2026-09-29T06:30:33.601Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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,
uniform_negatives=args.uniform_negatives,
inbatch_negatives=args.inbatch_negatives,
inbatch_correction_power=args.inbatch_correction_power,
inbatch_correction_min=args.inbatch_correction_min,
inbatch_correction_max=args.inbatch_correction_max,
target_weighting=args.target_weighting,
loss_type=args.loss_type,
temperature=args.temperature,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
...
## Step 61 · agent · 2026-09-29T06:30:38.557Z
### Recorded reasoning summary
Exploring Negative Sampling
I'm currently investigating the impact of varying negative sampling strategies, specifically focusing on the interplay between `inbatch_negatives` and `uniform_negatives`. My current exploration involves increasing the quantity of in-batch negatives to observe its effect on model performance.
### run_shell_command · 2026-09-29T06:30:38.557Z
{
"description": "Test with uniform=16 inbatch=16 negatives",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --loss-type bce \\\n --uniform-negatives 16 \\\n --inbatch-negatives 16 \\\n --output /app/solution/exp4_u16_ib16.parquet && \\\npython /task/evaluate_public.py /app/solution/exp4_u16_ib16.parquet"
}
### Tool output · 2026-09-29T06:35:57.185Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.556066 running_bce=0.556343 elapsed=190.2s
epoch=1/1 train_loss=0.55431 validation_loss=0.52006 pair_accuracy=0.726 train_targets=1,052,953 validation_targets=130,103 train_seconds=203.0 validation_seconds=13.7
restored best validation-BCE checkpoint: epoch=1 loss=0.520058
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/exp4_u16_ib16.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 309.606,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 16,
"inbatch_negatives": 16,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"loss_type": "bce",
"temperature": 0.1,
"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": 0.5543083887019831,
"positive_logit": 0.6960524660219143,
"negative_logit": -0.5012892177810132,
"pair_accuracy": 0.7255772574844271,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 202.97917069799996,
"validation_loss": 0.5200578049660878,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 13.70762099400008
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7176122665405273,
"query_norm_std": 0.8431686758995056,
"item_norm_mean": 1.8370518684387207,
"item_norm_std": 0.7449614405632019
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
{
"model": "/app/solution/exp4_u16_ib16.parquet",
"scores": {
"quality_ndcg@10": 0.020984,
"quality_users": 4263
}
}
Process Group PGID: 2340
## Step 62 · agent · 2026-09-29T06:36:04.914Z
### Recorded reasoning summary
Analyzing NDCG Performance
I'm investigating the recent NDCG results, specifically noting the dip with increasing `inbatch` size and its impact on pair accuracy. It seems the harder in-batch negatives are a contributing factor, and I'm currently exploring how `inbatch_correction` might be influencing this.
## Recorded session usage
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