yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-fixed-r04-3d6fca60
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T05:36:41.212Z
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 · 2026-09-29T05:36:41.257Z
# 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 · 2026-09-29T05:36:47.736Z
### Recorded reasoning summary
Analyzing Model Requirements
My current focus is on dissecting the core mandates for enhancing the music-discovery tower model. I'm reviewing topic updates, identifying critical constraints, and understanding the operational environment to refine the task instructions.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now drilling into the training objective, specifically how negative examples are selected and utilized. My priority is to pinpoint limitations within the current negative-sampling strategy and devise improvements that fit within our strict CPU constraints. This diagnostic work aims to prepare for validation against the public backtest.
### update_topic · 2026-09-29T05:36:47.736Z
{
"title": "Baseline Exploration and Benchmarking",
"strategic_intent": "Investigate the production two-tower baseline and evaluate its public validation performance.",
"summary": "Beginning the project by examining the production codebase in /task/current_model, reviewing the evaluation logic in evaluate_public.py and ranking_evaluation.py, and running the baseline validation pipeline to establish a performance benchmark."
}
### Tool output · 2026-09-29T05:36:47.755Z
Current topic: "Baseline Exploration and Benchmarking"
Topic summary: Beginning the project by examining the production codebase in /task/current_model, reviewing the evaluation logic in evaluate_public.py and ranking_evaluation.py, and running the baseline validation pipeline to establish a performance benchmark.
Strategic Intent: Investigate the production two-tower baseline and evaluate its public validation performance.
### read_file · 2026-09-29T05:36:47.736Z
{
"start_line": 1,
"file_path": "/task/evaluate_public.py",
"end_line": 150
}
### Tool output · 2026-09-29T05:36:47.755Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py "
)
### read_file · 2026-09-29T05:36:47.736Z
{
"file_path": "/task/ranking_evaluation.py",
"end_line": 150,
"start_line": 1
}
### Tool output · 2026-09-29T05:36:47.755Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
"""Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
### read_file · 2026-09-29T05:36:47.736Z
{
"start_line": 1,
"file_path": "/task/current_model/train_current_model.py",
"end_line": 150
}
### Tool output · 2026-09-29T05:36:47.755Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.
Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss. The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split. A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
into the next block.
The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations
import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F
import two_tower_model as base
CACHE_VERSION = 2
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
item_ids: np.ndarray
packed: np.ndarray
@classmethod
def read(cls, path: Path) -> "CandidateSet":
with np.load(path, allow_pickle=False) as artifact:
result = cls(
uids=artifact["uids"].astype(np.int64, copy=True),
item_ids=artifact["item_ids"].astype(np.int64, copy=True),
packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
)
expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
if result.packed.shape != expected:
raise ValueError(
f"invalid candidate artifact shape {result.packed.shape}; "
f"expected {expected}"
)
return result
@dataclass(frozen=True)
class SequenceData:
item_ids: np.ndarray
item_features: np.ndarray
user_ids: np.ndarray
offsets: np.ndarray
sequence_timestamps: np.ndarray
sequence_items: np.ndarray
event_features: np.ndarray
activity_features: np.ndarray
state_ids: np.ndarray
liked_states: np.ndarray
disliked_states: np.ndarray
state_counts: np.ndarray
inference_user_rows: np.ndarray
inference_liked_items: np.ndarray
inference_disliked_items: np.ndarray
inference_user_features: np.ndarray
@property
def lengths(self) -> np.ndarray:
return np.diff(self.offsets)
@property
def target_count(self) -> int:
return int(np.maximum(self.lengths - 1, 0).sum())
@dataclass(frozen=True)
class TargetPartition:
"""A contiguous recent target window and its chronological BCE split."""
window_starts: np.ndarray
training_counts: np.ndarray
validation_counts: np.ndarray
history_training_counts: np.ndarray
@property
def selected_count(self) -> int:
return int(self.training_counts.sum() + self.validation_counts.sum())
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def model_config(config: Config) -> base.TowerConfig:
### read_file · 2026-09-29T05:36:47.736Z
{
"file_path": "/task/current_model/two_tower_model.py",
"start_line": 1,
"end_line": 150
}
### Tool output · 2026-09-29T05:36:47.755Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn
DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100
@dataclass(frozen=True)
class TowerConfig:
sequence_length: int = 100
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
seed: int = 29
@dataclass(frozen=True)
class ListenEvents:
timestamps: np.ndarray
items: np.ndarray
organic: np.ndarray
completion_pct: np.ndarray
@dataclass(frozen=True)
class ExplicitEvents:
timestamps: np.ndarray
items: np.ndarray
channel: np.ndarray
active: np.ndarray
organic_like: np.ndarray
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
"""Return four standardized features built only from the public catalog."""
positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
features = np.column_stack(
[
np.log1p(positives),
np.log1p(organic),
organic / np.maximum(positives, 1.0),
1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
]
)
means = features.mean(axis=0, keepdims=True)
scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
standardized = ((features - means) / scales).astype(np.float32)
if not np.isfinite(standardized).all():
raise RuntimeError("catalog features contain non-finite values")
return standardized
def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
if len(parts) == 1:
return parts[0]
timestamps = np.concatenate([part.timestamps for part in parts])
items = np.concatenate([part.items for part in parts])
organic = np.concatenate([part.organic for part in parts])
completion = np.concatenate([part.completion_pct for part in parts])
order = np.argsort(timestamps, kind="stable")
return ListenEvents(
timestamps=timestamps[order],
items=items[order],
organic=organic[order],
completion_pct=completion[order],
)
def iter_listen_users(
path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Stream one uid-sorted public listen file one user at a time."""
parquet = pq.ParquetFile(path)
pending_uid: int | None = None
pending: list[ListenEvents] = []
previous_uid = -1
for batch in parquet.iter_batches(
batch_size=750_000,
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
):
frame = batch.to_pandas()
mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
if not np.any(keep):
continue
uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
items = mapped[keep].astype(np.int32, copy=False)
organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
if np.any(uids[1:] < uids[:-1]):
raise RuntimeError(f"{path.name} is not sorted by uid")
boundaries = np.r_[
0,
np.flatnonzero(uids[1:] != uids[:-1]) + 1,
len(uids),
]
for start, end in zip(boundaries[:-1], boundaries[1:]):
uid = int(uids[start])
part = ListenEvents(
timestamps[start:end].copy(),
items[start:end].copy(),
organic[start:end].copy(),
completion[start:end].copy(),
)
if pending_uid is None:
pending_uid = uid
elif uid != pending_uid:
if pending_uid < previous_uid:
raise RuntimeError(f"{path.name} user order regressed")
yield pending_uid, _combine_listen_chunks(pending)
previous_uid = pending_uid
pending_uid = uid
## Step 4 · agent · 2026-09-29T05:36:50.225Z
### run_shell_command · 2026-09-29T05:36:50.225Z
{
"command": "nproc && free -h",
"description": "Check CPU cores and available memory"
}
### Tool output · 2026-09-29T05:36:50.250Z
Output: 8
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1618
## Step 5 · agent · 2026-09-29T05:36:52.365Z
### run_shell_command · 2026-09-29T05:36:52.365Z
{
"command": "python3 -c \"import psutil; print('RAM (GB):', psutil.virtual_memory().total / (1024**3)); print('PyTorch threads:', __import__('torch').get_num_threads())\"",
"description": "Check RAM and PyTorch default threads"
}
### Tool output · 2026-09-29T05:36:52.397Z
Output: Traceback (most recent call last):
File "", line 1, in
ModuleNotFoundError: No module named 'psutil'
Exit Code: 1
Process Group PGID: 1622
## Step 6 · agent · 2026-09-29T05:37:05.566Z
### run_shell_command · 2026-09-29T05:37:05.566Z
{
"command": "python3 -c \"import torch; print('Torch threads:', torch.get_num_threads()); f = open('/proc/meminfo').readlines(); print(f[0], f[1], f[2])\"",
"description": "Check /proc/meminfo and PyTorch threads"
}
### Tool output · 2026-09-29T05:37:08.472Z
Output: Torch threads: 4
MemTotal: 15988968 kB
MemFree: 1286700 kB
MemAvailable: 14349892 kB
Process Group PGID: 1625
## Step 7 · agent · 2026-09-29T05:37:11.911Z
### Recorded reasoning summary
Analyzing Current Script
I'm currently examining the `train_current_model.py` and `two_tower_model.py` scripts to understand their functionality. My immediate plan is to execute the baseline reproduction command to gauge its runtime and performance metrics. My system has 16 GB RAM and 8 vCPUs.
### run_shell_command · 2026-09-29T05:37:11.911Z
{
"command": "mkdir -p /app/solution && time python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet",
"description": "Run the baseline training and ranking on validation stage"
}
### Tool output · 2026-09-29T05:43:21.064Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
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=200.7s
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=214.0 validation_seconds=15.6
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": 364.792,
"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": 0.5398380454468475,
"positive_logit": 0.7427799638768455,
"negative_logit": -0.622224704133723,
"pair_accuracy": 0.7547732721688433,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 213.955934511,
"validation_loss": 0.49663622024592563,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.638400071999968
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.6030172109603882,
"query_norm_std": 0.7697498202323914,
"item_norm_mean": 1.7570279836654663,
"item_norm_std": 0.8279486298561096
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
real 6m9.134s
user 31m9.260s
sys 1m42.522s
Process Group PGID: 1635
## Step 8 · agent · 2026-09-29T05:43:23.430Z
### run_shell_command · 2026-09-29T05:43:23.430Z
{
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet",
"description": "Evaluate baseline validation ranking"
}
### Tool output · 2026-09-29T05:43:26.824Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1695
## Step 9 · agent · 2026-09-29T05:43:37.216Z
### Recorded reasoning summary
Establishing a Valid Foundation
I'm focused on ensuring the initial submission is robust. My current thought process involves verifying the generation of a valid submission early on and thoroughly testing its integrity to secure the best possible baseline performance, aiming to preserve it.
### read_file · 2026-09-29T05:43:37.216Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 151,
"end_line": 300
}
### Tool output · 2026-09-29T05:43:37.224Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-300 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: 301.
--- FILE CONTENT (truncated) ---
"""Map the dense trainer configuration onto the unchanged tower."""
return base.TowerConfig(
sequence_length=config.bptt_steps,
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim,
user_hidden_1=config.user_hidden_1,
user_hidden_2=config.user_hidden_2,
item_hidden_1=config.item_hidden_1,
item_hidden_2=config.item_hidden_2,
dropout=config.dropout,
epochs=config.epochs,
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
gradient_clip=config.gradient_clip,
uniform_negatives=config.uniform_negatives,
inbatch_negatives=config.inbatch_negatives,
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
seed=config.seed,
)
def append_state_snapshot(
active_likes: dict[int, int],
active_dislikes: dict[int, int],
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> int:
liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))
disliked_states.append(
base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
)
state_counts.append((len(active_likes), len(active_dislikes)))
return len(liked_states) - 1
def causal_activity_features(
timestamps: np.ndarray,
items: np.ndarray,
organic: np.ndarray,
completion: np.ndarray,
) -> np.ndarray:
"""Compute the first four reviewed user features at every event cutoff."""
length = len(items)
result = np.zeros((length, 4), dtype=np.float16)
if not length:
return result
left_rows = np.searchsorted(
timestamps,
timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
rows = np.arange(length, dtype=np.int64)
counts = rows - left_rows + 1
organic_sum = np.cumsum(organic.astype(np.float64))
completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
before_organic = np.where(
left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
)
before_completion = np.where(
left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
)
unique_counts = np.empty(length, dtype=np.int32)
frequencies: dict[int, int] = {}
left = 0
for right, raw_item in enumerate(items):
item = int(raw_item)
frequencies[item] = frequencies.get(item, 0) + 1
required_left = int(left_rows[right])
while left < required_left:
old_item = int(items[left])
remaining = frequencies[old_item] - 1
if remaining:
frequencies[old_item] = remaining
else:
del frequencies[old_item]
left += 1
unique_counts[right] = len(frequencies)
result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
result[:, 2] = (
(organic_sum - before_organic) / np.maximum(counts, 1)
).astype(np.float16)
result[:, 3] = (
(completion_sum - before_completion)
/ np.maximum(counts, 1)
/ 100.0
).astype(np.float16)
return result
def build_sequence_parts(
listens: base.ListenEvents,
explicit: base.ExplicitEvents,
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
items = listens.items[qualifying].astype(np.uint16, copy=False)
organic = listens.organic[qualifying].astype(np.float32, copy=False)
completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
if not len(items):
return None
event_features = np.zeros((len(items), 3), dtype=np.float16)
event_features[:, 0] = organic.astype(np.float16)
event_features[:, 1] = (
np.minimum(completion, 100.0) / 100.0
).astype(np.float16)
if len(items) > 1:
gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
event_features[1:, 2] = np.exp(
-gaps.astype(np.float64) / (60.0 * base.DAY)
).astype(np.float16)
activity = causal_activity_features(
timestamps, items, organic, completion
)
states = np.zeros(len(items), dtype=np.uint32)
active_likes: dict[int, int] = {}
active_dislikes: dict[int, int] = {}
explicit_position = 0
current_state = 0
for row, timestamp in enumerate(timestamps):
changed = False
while (
explicit_position < len(explicit.timestamps)
and int(explicit.timestamps[explicit_position]) < int(timestamp)
):
item = int(explicit.items[explicit_position])
event_timestamp = int(explicit.timestamps[explicit_position])
destination = (
active_likes
if int(explicit.channel[explicit_position]) == 0
else active_dislikes
)
if int(explicit.active[explicit_position]) == 1:
destination[item] = event_timestamp
else:
destination.pop(item, None)
explicit_position += 1
changed = True
## Step 10 · agent · 2026-09-29T05:43:38.999Z
### read_file · 2026-09-29T05:43:38.999Z
{
"end_line": 600,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 301
}
### Tool output · 2026-09-29T05:43:39.004Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 301-600 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: 601.
--- FILE CONTENT (truncated) ---
if changed:
current_state = append_state_snapshot(
active_likes,
active_dislikes,
n_items,
liked_states,
disliked_states,
state_counts,
)
states[row] = np.uint32(current_state)
return timestamps, items.copy(), event_features, activity, states
def final_user_features(
timestamps: np.ndarray,
items: np.ndarray,
event_features: np.ndarray,
history_end: int,
like_count: int,
dislike_count: int,
) -> np.ndarray:
start = int(
np.searchsorted(
timestamps,
history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
)
result = np.zeros(6, dtype=np.float32)
recent_items = items[start:]
if len(recent_items):
result[0] = np.log1p(len(recent_items)) / 8.0
result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
result[2] = float(np.mean(event_features[start:, 0]))
result[3] = float(np.mean(event_features[start:, 1]))
result[4] = np.log1p(like_count) / 6.0
result[5] = np.log1p(dislike_count) / 4.0
return result
def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
if n_items >= np.iinfo(np.uint16).max:
raise RuntimeError("uint16 item storage is too small for this catalog")
if stage == "validation":
prefixes = ("train",)
history_end = base.TRAIN_END
candidate_name = "validation_eligible_candidates.npz"
state_name = "validation_preference_state.parquet"
else:
prefixes = ("train", "val")
history_end = base.VALIDATION_END
candidate_name = "test_eligible_candidates.npz"
state_name = "test_preference_state.parquet"
eligibility = CandidateSet.read(data / candidate_name)
if not np.array_equal(eligibility.item_ids, item_ids):
raise RuntimeError("catalog and candidate artifact item order differ")
explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
inference_likes, inference_dislikes, like_counts, dislike_counts = (
base.load_inference_state(
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
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,
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)
## Step 11 · agent · 2026-09-29T05:43:41.316Z
### read_file · 2026-09-29T05:43:41.316Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 1900,
"end_line": 2165
}
### Tool output · 2026-09-29T05:43:41.321Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1900-2165 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) ---
log(f"ranked users={end:,}/{len(eligibility.uids):,}")
query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
diagnostics = {
"query_norm_mean": float(query_norm.mean()),
"query_norm_std": float(query_norm.std()),
"item_norm_mean": float(item_norm.mean()),
"item_norm_std": float(item_norm.std()),
}
return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
def write_ranking(frame: pd.DataFrame, output: Path) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
if output.suffix == ".parquet":
frame.to_parquet(output, index=False, compression="zstd")
elif output.suffix == ".csv":
frame.to_csv(output, index=False)
else:
raise ValueError("--output must end in .parquet or .csv")
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
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 12 · agent · 2026-09-29T05:43:45.055Z
### Recorded reasoning summary
Exploring Cache Optimization
I've noticed the `--prepare-only` and `--prepared-cache` flags and how `load_or_prepare` now saves the cache with the latter. My current focus is on determining if we can pre-generate this prepared cache for validation and submission. The goal is to shave off roughly 50 seconds of data preparation per training run by avoiding redundant preparation.
### grep_search · 2026-09-29T05:43:45.055Z
{
"dir_path": "/task/current_model",
"pattern": "negative"
}
### Tool output · 2026-09-29T05:43:45.079Z
Found 100 matches for pattern "negative" in path "/task/current_model" (results limited to 100 matches for performance):
---
File: train_current_model.py
L14: and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
L56: uniform_negatives: int = 24
L57: inbatch_negatives: int = 8
L61: negative_filter_scope: str = "full-training-history"
L165: uniform_negatives=config.uniform_negatives,
L166: inbatch_negatives=config.inbatch_negatives,
L477: "event_time_feature": "exp-negative-gap-over-60-days",
L603: f"full-history false-negative filter: users={len(prepared.user_ids):,} "
L609: def invalid_full_history_negatives(
L610: negatives: np.ndarray,
L616: """Return row-level invalid negatives using the complete training prefix."""
L617: byte_columns = negatives >> 3
L618: bit_columns = negatives & 7
L621: invalid |= negatives == targets[:, None]
L622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
L623: for column in range(1, negatives.shape[1]):
L625: negatives[:, column, None] == negatives[:, :column]
L630: def draw_full_history_filtered_negatives(
L641: """Draw mixed negatives and reject all known public-training positives."""
L647: (batch_size, config.uniform_negatives),
L653: if config.inbatch_negatives:
L656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)
L672: if corrected_count < config.inbatch_negatives:
L675: (batch_size, config.inbatch_negatives - corrected_count),
L681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()
L682: correction = np.ones_like(negatives, dtype=np.float32)
L685: :, config.uniform_negatives : config.uniform_negatives + corrected_count
L688: invalid = invalid_full_history_negatives(
L689: negatives,
L697: torch.from_numpy(negatives).to(device),
L706: negatives[invalid] = replacements
L709: invalid = invalid_full_history_negatives(
L710: negatives,
L717: raise RuntimeError("full-history negative resampling failed")
L719: torch.from_numpy(negatives).to(device),
L724: def draw_training_negatives(
L736: if config.negative_filter_scope == "full-training-history":
L738: raise RuntimeError("full-history negative filter was not built")
L739: return draw_full_history_filtered_negatives(
L750: return base.draw_filtered_mixed_negatives(
L755: config.uniform_negatives,
L756: config.inbatch_negatives,
L816: negatives: torch.Tensor,
L818: all_items = torch.cat([positives, negatives.reshape(-1)])
L822: negative_vectors = encoded[inverse[len(positives) :]].reshape(
L823: len(positives), negatives.shape[1], -1
L825: return positive_vectors, negative_vectors
L845: def corrected_negative_loss(
L851: total = config.uniform_negatives + config.inbatch_negatives
L853: if config.uniform_negatives:
L854: result += config.uniform_negatives * raw[
L855: :, : config.uniform_negatives
L857: if config.inbatch_negatives:
L858: selected = slice(config.uniform_negatives, total)
L860: result += config.inbatch_negatives * (
L880: raise RuntimeError("chronological target partition produced negative counts")
L1119: negatives: torch.Tensor,
L1120: negative_weights: torch.Tensor,
L1134: "negative_sum": 0.0,
L1162: positive_vectors, negative_vectors = candidate_vectors(
L1165: negatives[begin:end],
L1168: negative_logits = torch.einsum(
L1169: "bd,bkd->bk", query_vectors, negative_vectors
L1172: negative_loss = corrected_negative_loss(
L1173: negative_logits, negative_weights[begin:end], config
L1179: selected_weights * (positive_loss + negative_loss) / 2.0
L1190: statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
L1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
L1227: negative_count = config.uniform_negatives + config.inbatch_negatives
L1232: total_negative = 0.0
L1293: negatives, negative_weights = draw_training_negatives(
L1314: negatives,
L1315: negative_weights,
L1331: total_negative += statistics["negative_sum"]
L1353: "negative_logit": total_negative / max(total_targets * negative_count, 1),
L1355: / max(total_targets * negative_count, 1),
L1449: negatives, negative_weights = draw_training_negatives(
L1469: negatives,
L1470: negative_weights,
L1567: if config.negative_filter_scope == "full-training-history"
L1601: "negative_filter_scope", "last-100"
L1629: stored_filter_scope != config.negative_filter_scope
L1660: f"{stored_filter_scope}->{config.negative_filter_scope}, "
L1944: "--negative-filter-scope",
L1946: default=Config.negative_filter_scope,
L2034: negative_filter_scope=args.negative_filter_scope,
L2136: "negative_sampling": (
L2139: "false_negative_filter": config.negative_filter_scope,
---
File: 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 13 · agent · 2026-09-29T05:43:52.912Z
### read_file · 2026-09-29T05:43:52.912Z
{
"start_line": 605,
"end_line": 770,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:43:52.918Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 605-770 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: 771.
--- FILE CONTENT (truncated) ---
)
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,
)
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)
## Step 14 · agent · 2026-09-29T05:43:57.248Z
### read_file · 2026-09-29T05:43:57.248Z
{
"end_line": 950,
"start_line": 800,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:43:57.257Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 800-950 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 951.
--- FILE CONTENT (truncated) ---
sequence_vectors: torch.Tensor,
liked_vectors: torch.Tensor,
disliked_vectors: torch.Tensor,
user_features: torch.Tensor,
) -> torch.Tensor:
return model.user_tower(
torch.cat(
[sequence_vectors, liked_vectors, disliked_vectors, user_features],
dim=1,
)
)
def candidate_vectors(
model: base.SequentialTwoTower,
positives: torch.Tensor,
negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
all_items = torch.cat([positives, negatives.reshape(-1)])
unique_items, inverse = torch.unique(all_items, return_inverse=True)
encoded = model.encode_items(unique_items)
positive_vectors = encoded[inverse[: len(positives)]]
negative_vectors = encoded[inverse[len(positives) :]].reshape(
len(positives), negatives.shape[1], -1
)
return positive_vectors, negative_vectors
def history_windows(
sequence: np.ndarray,
start: int,
count: int,
width: int,
padding_item: int,
) -> np.ndarray:
base_start = max(0, start - width + 1)
segment = sequence[base_start : start + count]
padded = np.concatenate(
[np.full(width - 1, padding_item, dtype=np.uint16), segment]
)
windows = np.lib.stride_tricks.sliding_window_view(padded, width)
offset = start - base_start
return windows[offset : offset + count].copy()
def corrected_negative_loss(
logits: torch.Tensor,
correction: torch.Tensor,
config: Config,
) -> torch.Tensor:
raw = F.softplus(logits)
total = config.uniform_negatives + config.inbatch_negatives
result = torch.zeros(len(logits), device=logits.device)
if config.uniform_negatives:
result += config.uniform_negatives * raw[
:, : config.uniform_negatives
].mean(dim=1)
if config.inbatch_negatives:
selected = slice(config.uniform_negatives, total)
weights = correction[:, selected]
result += config.inbatch_negatives * (
(raw[:, selected] * weights).sum(dim=1)
/ weights.sum(dim=1).clamp_min(1.0e-6)
)
return result / total
def retained_target_window_starts(
total_targets: np.ndarray,
validation_counts: np.ndarray,
training_counts: np.ndarray,
) -> np.ndarray:
"""Return the discarded prefix before the retained chronological suffix."""
training_region_ends = total_targets - validation_counts
window_starts = training_region_ends - training_counts
if (
np.any(window_starts < 0)
or np.any(training_counts < 0)
or np.any(validation_counts < 0)
):
raise RuntimeError("chronological target partition produced negative counts")
if not np.array_equal(
window_starts + training_counts + validation_counts, total_targets
):
raise RuntimeError("chronological target window is not a final contiguous suffix")
return window_starts
def chronological_target_partition(
prepared: SequenceData,
validation_fraction: float,
max_train_targets_per_user: int,
max_validation_targets_per_user: int,
) -> TargetPartition:
"""Select a recent contiguous window with a final out-of-time BCE suffix."""
totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
validation = np.floor(totals * validation_fraction).astype(np.int64)
validation[(totals >= 2) & (validation == 0)] = 1
if max_validation_targets_per_user:
validation = np.minimum(validation, max_validation_targets_per_user)
history_training = totals - validation
training = history_training.copy()
if max_train_targets_per_user:
training = np.minimum(training, max_train_targets_per_user)
window_starts = retained_target_window_starts(totals, validation, training)
return TargetPartition(
window_starts=window_starts,
training_counts=training,
validation_counts=validation,
history_training_counts=history_training,
)
def load_organic_like_outcomes(
public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
"""Load mapped organic-like timestamps from public fitting history."""
prefixes = ("train",) if stage == "validation" else ("train", "val")
frames = [
pd.read_parquet(
public / "data" / f"{prefix}_likes.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
for prefix in prefixes
]
likes = pd.concat(frames, ignore_index=True)
likes = likes[likes.is_organic == 1].copy()
mapped = pd.Index(item_ids).get_indexer(
likes.item_id.to_numpy(dtype=np.int64)
)
likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
nested: dict[int, dict[int, list[int]]] = {}
for raw_uid, raw_timestamp, raw_item in likes.itertuples(
index=False, name=None
):
nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
int(raw_timestamp)
)
result = {
uid: {
item: np.asarray(timestamps, dtype=np.int64)
for item, timestamps in items.items()
}
for uid, items in nested.items()
}
log(
f"loaded organic-like outcomes: rows={len(likes):,} "
f"users={len(result):,}"
## Step 15 · agent · 2026-09-29T05:43:59.114Z
### read_file · 2026-09-29T05:43:59.114Z
{
"end_line": 1250,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 951
}
### Tool output · 2026-09-29T05:43:59.120Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 951-1250 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1251.
--- FILE CONTENT (truncated) ---
)
return result
def build_target_weights(
public: Path,
stage: str,
prepared: SequenceData,
training_counts: np.ndarray,
config: Config,
) -> np.ndarray:
"""Build nonzero per-token weights without dropping adjacent targets."""
if config.target_weighting == "uniform":
weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
else:
weights = np.full(
len(prepared.sequence_items),
config.ordinary_target_weight,
dtype=np.float32,
)
organic_strong = (
(prepared.event_features[:, 0] > 0.5)
& (prepared.event_features[:, 1] >= 0.8)
)
weights[organic_strong] = config.organic_strong_target_weight
if config.target_weighting == "discovery-graded":
like_outcomes = load_organic_like_outcomes(
public, stage, prepared.item_ids
)
history_end = (
base.TRAIN_END if stage == "validation" else base.VALIDATION_END
)
horizon = config.discovery_horizon_days * base.DAY
grade_three = 0
for user_row, uid in enumerate(prepared.user_ids):
start = int(prepared.offsets[user_row])
end = int(prepared.offsets[user_row + 1])
if end - start < 2:
continue
last_training_target = start + int(training_counts[user_row])
first_validation_target = last_training_target + 1
split_timestamp = (
int(prepared.sequence_timestamps[first_validation_target])
if first_validation_target < end
else history_end
)
user_likes = like_outcomes.get(int(uid), {})
seen = {int(prepared.sequence_items[start])}
for position in range(start + 1, end):
item = int(prepared.sequence_items[position])
novel = item not in seen
seen.add(item)
if not novel:
# Repeats remain training examples, but they cannot be
# eligible discovery outcomes at the corresponding
# cutoff and therefore keep only the ordinary weight.
weights[position] = config.ordinary_target_weight
continue
timestamps = user_likes.get(item)
if timestamps is None:
continue
timestamp = int(prepared.sequence_timestamps[position])
upper = min(
timestamp + horizon,
split_timestamp
if position <= last_training_target
else history_end,
)
outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
weights[position] = config.organic_like_target_weight
grade_three += 1
log(
f"grade-3 first-encounter targets={grade_three:,} "
f"horizon_days={config.discovery_horizon_days}"
)
log(
f"target weighting={config.target_weighting} "
f"min={weights.min():.3f} mean={weights.mean():.3f} "
f"max={weights.max():.3f}"
)
return weights
def build_dense_block(
prepared: SequenceData,
target_weights: np.ndarray,
users: np.ndarray,
window_starts: np.ndarray,
process_counts: np.ndarray,
score_starts: np.ndarray,
start: int,
steps: int,
config: Config,
) -> tuple[np.ndarray, ...]:
"""Build one causal sequence block and mark the target rows to score."""
batch_size = len(users)
padding_item = len(prepared.item_ids)
input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
targets = np.zeros((batch_size, steps), dtype=np.uint16)
weights = np.ones((batch_size, steps), dtype=np.float32)
scored = np.zeros((batch_size, steps), dtype=bool)
histories = np.full(
(batch_size, steps, config.bptt_steps),
padding_item,
dtype=np.uint16,
)
processed = np.zeros(batch_size, dtype=np.int32)
lengths = prepared.lengths
for row, user_row in enumerate(users):
count = min(steps, max(int(process_counts[row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[user_row])
window_start = int(window_starts[row])
absolute_start = window_start + start
positions = slice(
offset + absolute_start,
offset + absolute_start + count,
)
target_positions = slice(
offset + absolute_start + 1,
offset + absolute_start + count + 1,
)
input_items[row, :count] = prepared.sequence_items[positions]
event_features[row, :count] = prepared.event_features[positions]
activity_features[row, :count] = prepared.activity_features[positions]
state_ids[row, :count] = prepared.state_ids[positions]
targets[row, :count] = prepared.sequence_items[target_positions]
weights[row, :count] = target_weights[target_positions]
score_from = min(count, max(int(score_starts[row]) - start, 0))
scored[row, score_from:count] = True
processed[row] = count
user_sequence = prepared.sequence_items[
offset : offset + int(lengths[user_row])
]
histories[row, :count] = history_windows(
user_sequence,
absolute_start,
count,
config.bptt_steps,
padding_item,
)
return (
input_items,
event_features,
activity_features,
state_ids,
targets,
scored,
histories,
processed,
weights,
)
def score_target_microbatches(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
sequence_vectors: torch.Tensor,
flat_states: np.ndarray,
flat_activity: np.ndarray,
positive_items: torch.Tensor,
negatives: torch.Tensor,
negative_weights: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
"""Compute one logical block's exact BCE in bounded-memory slices."""
target_count = len(positive_items)
total_weight = float(sample_weights.sum())
if total_weight <= 0:
raise RuntimeError("target block has no positive sample weight")
sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
statistics = {
"loss_sum": 0.0,
"positive_sum": 0.0,
"negative_sum": 0.0,
"pair_correct": 0.0,
"weight_sum": 0.0,
}
for begin in range(0, target_count, config.loss_microbatch_size):
end = min(begin + config.loss_microbatch_size, target_count)
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
def run_training_epoch(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
generator: torch.Generator,
device: torch.device,
epoch: int,
) -> dict[str, float]:
model.train()
train_users = np.flatnonzero(target_counts > 0)
sorted_users = train_users[
np.argsort(target_counts[train_users], kind="stable")
]
groups = [
sorted_users[start : start + config.user_batch_size]
for start in range(0, len(sorted_users), config.user_batch_size)
]
group_order = torch.randperm(len(groups), generator=generator).numpy()
negative_count = config.uniform_negatives + config.inbatch_negatives
total_loss = 0.0
total_targets = 0
total_weight = 0.0
total_positive = 0.0
total_negative = 0.0
total_pair_correct = 0.0
batches = 0
started = time.monotonic()
for group_number in group_order:
users = groups[int(group_number)]
group_counts = target_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_counts.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
## Step 16 · agent · 2026-09-29T05:44:00.897Z
### read_file · 2026-09-29T05:44:00.897Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 1251,
"end_line": 1400
}
### Tool output · 2026-09-29T05:44:00.902Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1251-1400 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1401.
--- FILE CONTENT (truncated) ---
group_counts,
np.zeros(len(users), dtype=np.int64),
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0).detach()
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
"loss": total_loss / max(total_weight, 1.0e-12),
"positive_logit": total_positive / max(total_targets, 1),
"negative_logit": total_negative / max(total_targets * negative_count, 1),
"pair_accuracy": total_pair_correct
/ max(total_targets * negative_count, 1),
"targets": float(total_targets),
"target_weight_sum": total_weight,
"batches": float(batches),
"seconds": time.monotonic() - started,
}
@torch.no_grad()
def evaluate_validation_bce(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
np.argsort(totals[users_with_validation], kind="stable")
]
groups = [
ordered[start : start + config.user_batch_size]
for start in range(0, len(ordered), config.user_batch_size)
]
generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
total_loss = 0.0
total_targets = 0
total_weight = 0.0
started = time.monotonic()
for group_number, users in enumerate(groups, start=1):
group_totals = totals[users]
group_starts = training_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_totals.max())
for start in range(0, max_targets, config.bptt_steps):
## Step 17 · agent · 2026-09-29T05:44:02.677Z
### read_file · 2026-09-29T05:44:02.677Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 1401,
"end_line": 1600
}
### Tool output · 2026-09-29T05:44:02.683Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1401-1600 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1601.
--- FILE CONTENT (truncated) ---
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_totals,
group_starts,
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0)
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
statistics, _ = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=False,
)
total_loss += statistics["loss_sum"]
total_targets += len(positive_items)
total_weight += statistics["weight_sum"]
if group_number % 25 == 0:
release_accelerator_cache(device)
return {
"validation_loss": total_loss / max(total_weight, 1.0e-12),
"validation_targets": float(total_targets),
"validation_target_weight_sum": total_weight,
"validation_seconds": time.monotonic() - started,
}
def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
return {
name: value.detach().cpu().clone()
for name, value in model.state_dict().items()
}
def save_epoch_checkpoint(
checkpoint_dir: Path,
epoch: int,
model_state: dict[str, torch.Tensor],
config: Config,
diagnostics: dict[str, float],
optimizer: torch.optim.Optimizer,
generator: torch.Generator,
) -> Path:
checkpoint_dir.mkdir(parents=True, exist_ok=True)
path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": model_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
path,
)
log(f"saved epoch checkpoint: {path}")
return path
def train_model(
public: Path,
stage: str,
prepared: SequenceData,
config: Config,
device: torch.device,
checkpoint_dir: Path | None,
resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
training_counts = partition.training_counts
validation_counts = partition.validation_counts
log(
f"chronological BCE window: train={int(training_counts.sum()):,} "
f"validation={int(validation_counts.sum()):,} "
f"selected={partition.selected_count:,} "
f"discarded_old_targets={int(partition.window_starts.sum()):,} "
f"all={prepared.target_count:,}"
)
model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
device
)
optimizer = torch.optim.AdamW(
model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
)
generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
item_correction = build_item_correction(
prepared,
config,
partition.window_starts,
training_counts,
)
target_weights = build_target_weights(
public,
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
## Step 18 · agent · 2026-09-29T05:44:05.153Z
### read_file · 2026-09-29T05:44:05.153Z
{
"end_line": 1900,
"start_line": 1750,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:44:05.162Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1750-1900 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: 1901.
--- FILE CONTENT (truncated) ---
)
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):,}")
## Step 19 · agent · 2026-09-29T05:44:09.815Z
### read_file · 2026-09-29T05:44:09.815Z
{
"end_line": 350,
"start_line": 150,
"file_path": "/task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T05:44:09.821Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 150-350 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: 351.
--- FILE CONTENT (truncated) ---
pending_uid = uid
pending = []
pending.append(part)
if pending_uid is not None:
yield pending_uid, _combine_listen_chunks(pending)
def merge_listen_streams(
paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Merge uid-sorted public listen files without materializing them."""
streams = [iter(iter_listen_users(path, item_index)) for path in paths]
heads: list[tuple[int, ListenEvents] | None] = [
next(stream, None) for stream in streams
]
while any(head is not None for head in heads):
uid = min(head[0] for head in heads if head is not None)
parts: list[ListenEvents] = []
for position, head in enumerate(heads):
if head is not None and head[0] == uid:
parts.append(head[1])
heads[position] = next(streams[position], None)
yield uid, _combine_listen_chunks(parts)
def load_explicit_events(
data: Path,
prefixes: tuple[str, ...],
item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
"""Load public explicit actions as chronological state changes."""
specifications = (
("likes", 0, 1, 0),
("unlikes", 0, 0, 1),
("dislikes", 1, 1, 0),
("undislikes", 1, 0, 1),
)
parts: list[pd.DataFrame] = []
for family, channel, active, tie_priority in specifications:
for prefix in prefixes:
frame = pd.read_parquet(
data / f"{prefix}_{family}.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
mapped = item_index.get_indexer(
frame.item_id.to_numpy(dtype=np.int64, copy=False)
)
keep = mapped >= 0
if not np.any(keep):
continue
retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
retained["item"] = mapped[keep].astype(np.int32, copy=False)
retained["channel"] = np.int8(channel)
retained["active"] = np.int8(active)
retained["tie_priority"] = np.int8(tie_priority)
retained["organic_like"] = np.int8(
family == "likes"
) * retained.is_organic.astype(np.int8)
parts.append(retained.drop(columns="is_organic"))
if not parts:
return {}
events = pd.concat(parts, ignore_index=True)
events = events.sort_values(
["uid", "timestamp", "tie_priority"], kind="mergesort"
).reset_index(drop=True)
result: dict[int, ExplicitEvents] = {}
for uid, rows in events.groupby("uid", sort=False):
result[int(uid)] = ExplicitEvents(
timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
items=rows.item.to_numpy(dtype=np.int32, copy=True),
channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
active=rows.active.to_numpy(dtype=np.int8, copy=True),
organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
)
log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
return result
def empty_explicit_events() -> ExplicitEvents:
return ExplicitEvents(
timestamps=np.empty(0, dtype=np.int64),
items=np.empty(0, dtype=np.int32),
channel=np.empty(0, dtype=np.int8),
active=np.empty(0, dtype=np.int8),
organic_like=np.empty(0, dtype=np.int8),
)
def pack_active_items(
active: dict[int, int], width: int, padding_item: int
) -> np.ndarray:
packed = np.full(width, padding_item, dtype=np.uint16)
if not active:
return packed
ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
packed[: len(ordered)] = np.fromiter(
(item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
)
return packed
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,
## Step 20 · agent · 2026-09-29T05:44:11.496Z
### read_file · 2026-09-29T05:44:11.496Z
{
"start_line": 350,
"end_line": 434,
"file_path": "/task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T05:44:11.501Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 350-434 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) ---
liked_items: torch.Tensor,
) -> torch.Tensor:
"""Identify targets, known positives, active likes, and row duplicates."""
invalid = negatives.eq(targets[:, None])
invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= negatives[:, column, None].eq(
negatives[:, :column]
).any(dim=1)
return invalid
def draw_filtered_mixed_negatives(
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
n_items: int,
uniform_count: int,
inbatch_count: int,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw uniform and unique-target in-batch negatives."""
batch_size = len(targets)
uniform = torch.randint(
n_items,
(batch_size, uniform_count),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
-1.0,
)
columns = torch.topk(priorities, k=corrected_count, dim=1).indices
inbatch = unique_targets[columns]
if corrected_count < inbatch_count:
filler = torch.randint(
n_items,
(batch_size, inbatch_count - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
:, uniform_count : uniform_count + corrected_count
] = item_correction[inbatch[:, :corrected_count]]
negatives = negatives_cpu.to(device)
correction = correction_cpu.to(device)
for _ in range(32):
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if not bool(invalid.any()):
return negatives, correction
replacements = torch.randint(
n_items,
(int(invalid.sum().detach().cpu()),),
generator=generator,
device="cpu",
).to(device)
negatives[invalid] = replacements
correction[invalid] = 1.0
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if bool(invalid.any()):
raise RuntimeError("mixed negative resampling failed false-negative filter")
return negatives, correction
## Step 21 · agent · 2026-09-29T05:44:13.667Z
### read_file · 2026-09-29T05:44:13.667Z
{
"file_path": "/task/evaluate_public.py",
"start_line": 120,
"end_line": 158
}
### Tool output · 2026-09-29T05:44:13.673Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 120-158 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.
--- FILE CONTENT (truncated) ---
)
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 22 · agent · 2026-09-29T05:44:15.576Z
### read_file · 2026-09-29T05:44:15.576Z
{
"file_path": "/task/ranking_evaluation.py",
"start_line": 140,
"end_line": 232
}
### Tool output · 2026-09-29T05:44:15.582Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 140-232 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.
--- FILE CONTENT (truncated) ---
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 23 · agent · 2026-09-29T05:44:22.391Z
### Recorded reasoning summary
Investigating Production Model
I'm currently delving into the production model, scrutinizing its retrieval performance with the provided implementation and public validation data. My aim is to identify areas for enhancement to ultimately deliver a superior ranking artifact.
### Recorded reasoning summary
Refining Negative Sampling
I'm focusing on optimizing the negative sampling strategy within the training objective. My current work involves diagnosing limitations in the existing approach and devising improvements that can be validated against the public backtest, all while adhering to the strict latency budget and CPU constraints of the production environment.
### Recorded reasoning summary
Validating Development Workflow
I'm clarifying the distinction between model development and final refitting stages, specifically how to utilize public validation windows. The goal is to ensure improvements are rigorously tested and that the final submission adheres precisely to the specified format, including all required columns and rankings for target users.
### Recorded reasoning summary
Testing Baseline Workflow
I'm now prioritizing a baseline run to establish a performance benchmark. My immediate actions involve copying existing code, investigating the baseline submission stage duration, and exploring the feasibility of precomputing cached data. This will inform our subsequent optimization efforts.
### run_shell_command · 2026-09-29T05:44:22.391Z
{
"command": "python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /tmp/dummy.parquet --prepared-cache /app/solution/prepared_val.npz --prepare-only",
"description": "Prepare and cache validation sequence data"
}
### Tool output · 2026-09-29T05:45:17.160Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.9s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.3s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.9s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/prepared_val.npz
{
"stage": "validation",
"sequence_users": 9135,
"sequence_tokens": 20056329,
"next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"target_users": 8456,
"runtime_seconds": 50.735,
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
}
}
Process Group PGID: 1723
## Step 24 · agent · 2026-09-29T05:45:49.209Z
### run_shell_command · 2026-09-29T05:45:49.209Z
{
"description": "Check size of prepared_val.npz",
"command": "ls -lh /app/solution/prepared_val.npz"
}
### Tool output · 2026-09-29T05:45:49.265Z
Output: -rw-r--r-- 1 agent agent 511M Sep 29 05:45 /app/solution/prepared_val.npz
Process Group PGID: 1753
## Step 25 · agent · 2026-09-29T05:46:04.342Z
### run_shell_command · 2026-09-29T05:46:04.342Z
{
"command": "cp /task/current_model/train_current_model.py /app/solution/ && cp /task/current_model/two_tower_model.py /app/solution/",
"description": "Copy current_model files to /app/solution/"
}
### Tool output · 2026-09-29T05:46:04.365Z
Output: (empty)
Process Group PGID: 1756
## Step 26 · agent · 2026-09-29T05:46:10.404Z
### run_shell_command · 2026-09-29T05:46:10.404Z
{
"description": "Prepare and cache submission sequence data",
"command": "python /app/solution/train_current_model.py --stage submission --public-dir /task --output /tmp/dummy_sub.parquet --prepared-cache /app/solution/prepared_sub.npz --prepare-only"
}
### Tool output · 2026-09-29T05:47:06.910Z
Output: loaded explicit events: rows=746,504 users=8,307
prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s
prepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.2s
prepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.6s
prepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.2s
prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.2s
prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.1s
prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=36.8s
prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.3s
prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.2s
target users without qualifying catalog sequence=6
prepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322
saved public-derived full-sequence cache: /app/solution/prepared_sub.npz
{
"stage": "submission",
"sequence_users": 9153,
"sequence_tokens": 20712971,
"next_item_targets": 20703818,
"selected_training_targets": 1067606,
"selected_validation_targets": 131997,
"discarded_old_targets": 19504215,
"target_users": 8456,
"runtime_seconds": 52.507,
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
}
}
Process Group PGID: 1760
## Step 27 · agent · 2026-09-29T05:47:29.748Z
### Recorded reasoning summary
Caching Data Now
I've successfully cached both `prepared_val.npz` and `prepared_sub.npz`. My immediate next step is to generate an initial baseline `submission.parquet` to ensure I have a valid submission saved before the deadline. Following that, I will proceed with training the current model.
### Recorded reasoning summary
Generating Submission File
I'm considering generating a baseline `submission.parquet` now, even though the score isn't optimal. This ensures I have a valid submission saved, providing a fallback should anything go wrong before the deadline. I'm weighing this against continuing to investigate potential issues with the negative validation.
### Recorded reasoning summary
Investigating Negatives Systematically
I'm shifting my focus from immediate submission generation to a deep dive into the negative sampling strategy. Running parallel training jobs on CPU seems counterproductive given resource constraints, so I'm prioritizing careful code inspection and diagnosis. My goal is to understand the current limitations and identify improvements to the negative sampling process itself before proceeding with further training.
### Recorded reasoning summary
Diagnosing Negative Sampling
I'm now meticulously examining how my training objective selects and utilizes negative examples. My primary aim is to identify limitations within the current negative-sampling strategy and enhance it while adhering to the fixed CPU budget. I will then validate these changes using the public backtest.
### Recorded reasoning summary
Refining Negative Selection Logic
I'm currently scrutinizing the `invalid` mask generation within the negative sampling code. My focus is on understanding its interaction with `draw_full_history_filtered_negatives` and ensuring it correctly identifies and excludes unwanted samples before I proceed with any modifications.
### Recorded reasoning summary
Analyzing Negative Sampling Code
I'm currently scrutinizing the `invalid` mask generation within the negative sampling code. My focus is on understanding its interaction with `draw_full_history_filtered_negatives` and ensuring it correctly identifies and excludes unwanted samples before I proceed with any modifications.
### Recorded reasoning summary
Debugging Inbatch Negatives
I'm now investigating the `inbatch` negative sampling, specifically where `targets` originates in `draw_training_negatives`. I'm checking `run_training_epoch` and the `build_dense_block` function for clues, as the current logic seems to be missing a critical piece.
### Recorded reasoning summary
Tracing Positive Item Origin
I'm now tracing the origin of `positive_items` in the `draw_training_negatives` function. It appears to be derived from `flat_scored`, which itself originates from a BPTT block, and I need to understand how `users` and `steps` in that block determine its dimensions and content before proceeding with the negative sampling logic.
### Recorded reasoning summary
Understanding Target Origins
I'm now analyzing how `positive_items` are derived from `flat_scored` and the `users` and `steps` within the BPTT block. This is crucial for understanding the full scope of the 6,400 targets and how they are being used to sample both uniform and in-batch negatives, which then informs the `corrected_negative_loss` calculation.
### Recorded reasoning summary
Analyzing Loss Functions
I am currently analyzing the `corrected_negative_loss` function and its relationship with `F.softplus`. I've realized it's essentially binary cross-entropy, but I need to fully understand how it treats positive samples versus averaged negative samples before proceeding. This seems to be the core of the issue.
### Recorded reasoning summary
Investigating Loss Calculation
I'm dissecting the `corrected_negative_loss` function, realizing it's effectively BCE. My focus is on understanding how the averaging of negative logits impacts the gradient, as a scaling factor of `1/K` appears to dilute the push from individual negatives.
### grep_search · 2026-09-29T05:47:29.748Z
{
"dir_path": "/task/current_model",
"pattern": "dislike"
}
### Tool output · 2026-09-29T05:47:29.768Z
Found 40 matches for pattern "dislike" in path "/task/current_model":
---
File: train_current_model.py
L110: disliked_states: np.ndarray
L114: inference_disliked_items: np.ndarray
L176: active_dislikes: dict[int, int],
L179: disliked_states: list[np.ndarray],
L183: disliked_states.append(
L184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186: state_counts.append((len(active_likes), len(active_dislikes)))
L252: disliked_states: list[np.ndarray],
L279: active_dislikes: dict[int, int] = {}
L293: else active_dislikes
L304: active_dislikes,
L307: disliked_states,
L320: dislike_count: int,
L337: result[5] = np.log1p(dislike_count) / 4.0
L369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383: disliked_states = [
L384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405: disliked_states,
L427: int(dislike_counts[inference_row]),
L440: np.log1p(dislike_counts[missing]) / 4.0
L455: disliked_states=np.stack(disliked_states),
L459: inference_disliked_items=inference_dislikes,
L784: disliked_items = torch.from_numpy(
L785: prepared.disliked_states[unique_states].astype(np.int64)
L788: disliked_vectors = model._mean_context_channel(disliked_items)
L793: disliked_vectors[inverse_rows],
L802: disliked_vectors: torch.Tensor,
L807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159: disliked_vectors,
L1844: disliked_items = torch.from_numpy(
L1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851: model._mean_context_channel(disliked_items),
---
File: two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184: ("dislikes", 1, 1, 0),
L185: ("undislikes", 1, 0, 1),
L265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281: return liked, disliked, like_counts, dislike_counts
---
## Step 28 · agent · 2026-09-29T05:48:06.683Z
### Recorded reasoning summary
Analyzing Dislike Usage
I'm currently scrutinizing the precise utilization of "dislikes" within the user tower, specifically confirming they function solely as an input feature and are never treated as negative examples. My immediate focus is on whether dislikes are incorporated into `invalid_full_history_negatives`, and I'm reviewing the relevant code at line 609 for clarification.
### Recorded reasoning summary
Confirming Dislike Functionality
I'm verifying that dislikes act solely as input and aren't misinterpreted as negative examples. My current investigation centers on their integration into `invalid_full_history_negatives`, and I'm examining line 609 for definitive confirmation of this behavior.
### Recorded reasoning summary
Investigating Input Inclusion
I'm reviewing how `prepared.sequence_items` are handled, specifically questioning if user validation prefixes or full histories are excluded. My attention is drawn to `training_counts` as a potential indicator.
### Recorded reasoning summary
Examining Dislike Handling
I'm now realizing that `build_known_positive_filter` already accounts for liked items and training history to prevent their sampling as negatives. My immediate question is whether explicit dislikes (`train_dislikes`) are similarly handled, given their nature as explicitly disliked tracks. I need to determine if the discovery objective considers them.
### Recorded reasoning summary
Clarifying Negative Sample Source
I'm now focusing on how negative examples are selected and utilized by the training objective. My current priority is to thoroughly examine the entire codebase for any mention of the negative-sampling strategy to identify potential limitations and areas for improvement, keeping the CPU budget in mind.
### Recorded reasoning summary
Confirming Target Identity
I've realized that `targets` represents all items in a BPTT block, not just those with dislikes. This clarifies their scope for in-batch negative generation.
### Recorded reasoning summary
Sampling Uniformly Incorrectly
I've discovered a significant issue: in-batch negative sampling appears to be drawing uniformly from unique targets, not proportionally to their frequency. This means popular items aren't being sampled more often, which is counter to the intended "popularity sampling" strategy and its benefits for log-Q.
### Recorded reasoning summary
Applying Correction Strategy
I've realized that my previous assumption about uniform sampling was incorrect. The `build_item_correction` function explicitly calculates in-batch propensities based on item frequency, meaning popular items *are* sampled more often. My focus now shifts to understanding how this calculated correction is applied to the loss.
### Recorded reasoning summary
Adjusting Loss Calculations
I've identified a critical error where in-batch negative sampling was uniform instead of proportional. My current focus is on how the calculated item `correction` impacts the loss, particularly when it downweights popular items, and I'm reviewing `corrected_negative_loss` for its precise application.
### Recorded reasoning summary
Analyzing Loss Weighting
I'm re-examining the `corrected_negative_loss` function. It appears dividing by `weights.sum(dim=1)` normalizes the weights, effectively making them sum to one. My current focus is understanding why this loss is BCE and not a standard softmax, given the prompt's description of the model architecture and its retrieval mechanism.
### Recorded reasoning summary
Revisiting Loss Interpretation
I'm reconsidering the nature of the `corrected_negative_loss`. It seems I was mistaken in expecting a softmax; it's indeed a binary cross-entropy, but the `correction` term, by scaling the logit, effectively biases it. My current focus is on the implication of this BCE loss with logit scaling.
### Recorded reasoning summary
Investigating Negative Sampling
I'm currently investigating how negative examples are sampled and used, specifically questioning the uniform sampling from the entire catalog for `uniform_negatives`. My immediate focus is on how in-batch negatives are drawn from the `targets` within a BPTT block and if this process is user-specific or global.
### Recorded reasoning summary
Analyzing Loss Behavior
I'm questioning the loss function's interpretation. While I initially saw BCE, the context and the term `correction` suggest it's acting more like a sampled softmax, effectively biasing logits to approximate a full softmax over candidates, which is critical for understanding the loss weighting.
### Recorded reasoning summary
Confirming Loss Type
I've confirmed that the loss is indeed a BCE loss, specifically `F.softplus(-positive_logits)` for positives and a `corrected_negative_loss` for negatives. My current focus is understanding how the `correction` term within `corrected_negative_loss` effectively biases the logits to mimic a softmax behavior despite being BCE.
### Recorded reasoning summary
Clarifying Negative Logit Calculation
I'm analyzing how negative logits are computed. Specifically, I'm looking at `torch.e` in the provided snippet, which seems incomplete. My goal is to understand if this is part of a larger operation for calculating all negative logits and if the `sum(dim=1)` operation applies here as well.
### Recorded reasoning summary
Analyzing Negative Logit Computation
I'm now realizing the `torch.e` was a placeholder for the full operation. The actual calculation involves an einsum to compute dot products between query vectors and negative vectors, generating `(6400, 32)` negative logits, which is computationally intensive.
### Recorded reasoning summary
Evaluating Target Weighting
I'm now considering the `--target-weighting` parameter and its potential impact. My focus is on the "discovery-graded" option, aiming to understand how it influences the training process and if it offers an improvement over the current "organic-strong" setting.
### Recorded reasoning summary
Evaluating Target Weighting
I'm now considering the `--target-weighting` parameter and its potential impact. My focus is on the "discovery-graded" option, aiming to understand how it influences the training process and if it offers an improvement over the current "organic-strong" setting. I'm examining the relevant code block to understand the specifics of "discovery-graded" weighting.
### Recorded reasoning summary
Investigating Negative Selection
I'm now diving deep into the core of negative sampling. My current focus is on precisely how these negatives are identified from the entire catalog, considering user listening habits and the candidate pool. I'm preparing to inspect data files directly to uncover the mechanics.
### run_shell_command · 2026-09-29T05:48:06.683Z
{
"description": "Inspect catalog, targets, and events",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog shape:', catalog.shape)\nprint('Catalog columns:', catalog.columns.tolist())\nprint(catalog.head(5))\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users count:', len(targets))\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\ntrain_dislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Train dislikes shape:', train_dislikes.shape)\nprint('Val likes shape:', val_likes.shape)\nprint('Val listens shape:', val_listens.shape)\n\""
}
### Tool output · 2026-09-29T05:48:08.273Z
Output: Catalog shape: (30000, 4)
Catalog columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
Target users count: 8456
Train dislikes shape: (101075, 4)
Val likes shape: (37795, 4)
Val listens shape: (1501697, 6)
Process Group PGID: 1800
## Step 29 · agent · 2026-09-29T05:48:11.954Z
### Recorded reasoning summary
Investigating Ranking Mechanics
I'm currently examining the ranking and scoring mechanisms within `evaluate_public.py` to understand how items are ordered. My focus is on the baseline's validation scores and predictions, specifically how they appear in `/app/solution/production_validation.parquet`, and I'm also inspecting the item distributions.
### run_shell_command · 2026-09-29T05:48:11.954Z
{
"description": "Analyze baseline predictions in top 10",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ndf = pd.read_parquet('/app/solution/production_validation.parquet')\nprint('Validation ranking head:')\nprint(df.head(10))\nprint('Rank range:', df['rank'].min(), df['rank'].max())\nprint('Number of unique items ranked in top 10:', df[df['rank'] <= 10]['item_id'].nunique())\nprint('Number of unique items ranked overall:', df['item_id'].nunique())\ntop_items = df[df['rank'] <= 10]['item_id'].value_counts().head(20)\nprint('Most frequent items in top 10:')\nprint(top_items)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\nprint('\\nPopularity of most frequent items in top 10:')\nprint(catalog.loc[top_items.index])\n\""
}
### Tool output · 2026-09-29T05:48:13.535Z
Output: Validation ranking head:
uid item_id rank
0 100 6511826 1
1 100 3972057 2
2 100 2736766 3
3 100 4948220 4
4 100 5488609 5
5 100 7958357 6
6 100 8460142 7
7 100 8250570 8
8 100 5570016 9
9 100 7510702 10
Rank range: 1 100
Number of unique items ranked in top 10: 592
Number of unique items ranked overall: 2575
Most frequent items in top 10:
item_id
6006390 2917
8213481 2800
6896222 2712
8743390 2653
5436843 2587
2651314 2580
8722394 2542
3542184 2427
5862961 2292
906358 2066
5927996 1872
9378983 1537
2185191 1522
6901374 1463
3033749 1333
5252571 1289
2408923 1215
4423556 1148
8351511 1121
3375935 1107
Name: count, dtype: int64
Popularity of most frequent items in top 10:
positive_listens organic_positive_listens popularity_rank
item_id
6006390 8970 6813 75
8213481 22505 17071 7
6896222 3020 2017 818
8743390 13518 8480 22
5436843 14897 12127 14
2651314 16286 11801 11
8722394 3464 1695 634
3542184 24075 17699 4
5862961 26615 20620 1
906358 22238 17183 8
5927996 14751 10424 16
9378983 24036 18195 5
2185191 13059 8839 25
6901374 25536 17347 2
3033749 16682 12267 10
5252571 14897 11331 13
2408923 7515 4545 111
4423556 6544 2177 157
8351511 14061 9739 19
3375935 5213 3232 282
Process Group PGID: 1820
## Step 30 · agent · 2026-09-29T05:48:18.210Z
### Recorded reasoning summary
Analyzing Item Rankings
I'm currently examining the performance of items 6006390 and 8213481. Item 6006390 is notably popular, appearing in the top 10 for over a third of users, while item 8213481 also shows strong engagement with a similar top 10 ranking.
### run_shell_command · 2026-09-29T05:48:18.210Z
{
"command": "python3 -c \"\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\n\nall_relevant_items = []\ngrades = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_relevant_items.append(item)\n grades.append(grade)\n\ns = pd.Series(all_relevant_items)\nprint('Total graded user-item interactions:', len(s))\nprint('Unique relevant items:', s.nunique())\nprint('Grade distribution:\\n', pd.Series(grades).value_counts())\nprint('Most frequent items in ground truth:')\nprint(s.value_counts().head(20))\nprint('\\nPopularity of most frequent ground truth items:')\nprint(catalog.loc[s.value_counts().head(20).index])\n\"",
"description": "Analyze validation ground truth distribution"
}
### Tool output · 2026-09-29T05:48:19.698Z
Output: Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1839
## Step 31 · agent · 2026-09-29T05:48:33.370Z
### run_shell_command · 2026-09-29T05:48:33.370Z
{
"command": "python3 -c \"\nimport sys; sys.path.append('/task')\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\n\nall_relevant_items = []\ngrades = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_relevant_items.append(item)\n grades.append(grade)\n\ns = pd.Series(all_relevant_items)\nprint('Total graded user-item interactions:', len(s))\nprint('Unique relevant items:', s.nunique())\nprint('Grade distribution:\\n', pd.Series(grades).value_counts())\nprint('Most frequent items in ground truth:')\nprint(s.value_counts().head(20))\nprint('\\nPopularity of most frequent ground truth items:')\nprint(catalog.loc[s.value_counts().head(20).index])\n\"",
"description": "Analyze validation ground truth distribution"
}
### Tool output · 2026-09-29T05:48:36.724Z
Output: Total graded user-item interactions: 37769
Unique relevant items: 14124
Grade distribution:
1 33352
3 4417
Name: count, dtype: int64
Most frequent items in ground truth:
6399259 173
6252104 109
8250570 88
2055777 83
1265696 77
3134183 60
7579671 51
1005397 46
4948220 45
6625048 43
374424 42
5570747 42
7419525 42
6006390 42
8722394 41
6762774 41
3361052 40
7931842 40
5010168 39
7227266 39
Name: count, dtype: int64
Popularity of most frequent ground truth items:
positive_listens organic_positive_listens popularity_rank
6399259 1276 1238 3329
6252104 855 820 5622
8250570 1059 1029 4263
2055777 2465 1558 1170
1265696 681 420 7350
3134183 1517 720 2602
7579671 1441 928 2789
1005397 5546 2644 240
4948220 740 721 6675
6625048 7133 5065 128
374424 323 193 16388
5570747 4953 2891 312
7419525 476 302 11086
6006390 8970 6813 75
8722394 3464 1695 634
6762774 4331 2238 413
3361052 1969 922 1726
7931842 1012 427 4554
5010168 1385 1228 2947
7227266 2823 1726 919
Process Group PGID: 1850
## Step 32 · agent · 2026-09-29T05:49:55.026Z
### Recorded reasoning summary
Analyzing Ground Truth Rankings
I'm observing a curious trend where our ground truth items, the ones users genuinely engage with, often fall within a surprisingly wide and rather low ranking range, from around 1,000 to 16,000. This is within a pool of 14,124 unique relevant items.
### Recorded reasoning summary
Investigating Popularity Bias
It appears my baseline model is heavily biased towards universally popular items, consistently ranking them highly. This is particularly striking when contrasted with the sparse engagement observed for these same items in the ground truth data. I'm delving into how the two-tower architecture, specifically within the training and negative sampling phases, might be amplifying this effect, especially when high-frequency positive targets are involved.
### Recorded reasoning summary
Refining Negative Sampling
I'm examining the baseline's negative sampling strategy, specifically the uniform and in-batch methods. It seems uniform negatives are drawn from the entire catalog with a vanishingly small probability for any specific item. I'm digging into the exact implementation of in-batch negatives to understand their impact.
### Recorded reasoning summary
Discovering Uniform Sampling
I've uncovered a critical flaw in the in-batch negative sampling. Instead of sampling proportionally to batch frequency, the current implementation uniformly samples from unique targets. This means popular and rare items have an equal chance of being selected as negatives, severely undermining the intended purpose and likely contributing to the observed biases.
### Recorded reasoning summary
Correcting Loss Calculations
I've identified a critical issue in how in-batch negatives are handled. Specifically, popular items (track 1) are being downweighted in the loss calculation. This, combined with the uniform sampling of negatives, means their impact is drastically reduced, inadvertently reinforcing popularity bias.
### Recorded reasoning summary
Revising Negative Sampling Logic
I'm reassessing how in-batch negatives function. It appears my current uniform sampling from unique targets, rather than proportional to frequency, is the key issue. I'm exploring the standard literature to ensure the in-batch negative sampling and subsequent loss calculations accurately reflect proportional sampling for effective bias correction.
### Recorded reasoning summary
Adjusting Scoring Weights
I'm realizing that my scoring function needs adjustment. The formula $s'(u_i, v_j) = s(u_i, v_j) - \log p(v_j)$ is crucial for correcting sampling bias, ensuring that rankings reflect true relevance and not just popularity. I'm investigating how the codebase applies this, noting the mention of "sample-weighted binary cross entropy" and "propensity-corrected unique in-batch negatives."
### Recorded reasoning summary
Revising In-Batch Sampling
I've realized that my in-batch negative sampling was flawed, uniformly picking from unique targets instead of proportionally to their frequency. I'm now looking at implementing proportional sampling and correcting the loss calculation to properly account for it.
### Recorded reasoning summary
Exploring Negative Candidates
I'm re-evaluating how I handle negative sampling, specifically questioning whether disliked items, which are known negatives, are being properly incorporated. I'm also investigating how the system filters out encountered items during validation and testing to ensure accurate candidate pools.
### Recorded reasoning summary
Correcting Logits Transformation
I'm delving into how the raw logits are transformed and weighted within the loss calculation. It appears a softplus function is applied before correction, and I need to confirm how this interacts with the propensity weighting.
### Recorded reasoning summary
Adjusting Loss Weights
I've discovered that the loss calculation incorrectly downweights popular items when they appear as negatives. This mechanism, intended to correct for sampling bias, is actually amplifying popularity bias by significantly reducing the penalty for predicting popular items. I'm now focused on reversing this logic.
### Recorded reasoning summary
Correcting Popularity Bias
I'm now understanding that the previous loss calculation was indeed downweighting popular items incorrectly, exacerbating the bias. I'm actively revising this to properly penalize popular items when they appear as negatives.
### Recorded reasoning summary
Revising Loss Function
I'm realizing the baseline's approach to popularity correction is flawed. Instead of directly adjusting logits with $-\log p$, it uses inverse propensity weighting within a Binary Cross Entropy loss. This inadvertently downweights popular items in the loss, reinforcing the bias I'm trying to eliminate. I'm now focused on implementing the correct logit adjustment.
### Recorded reasoning summary
Correcting Logit Calculations
I'm realizing a significant error where popular items are being penalized far less when they appear as negatives. This inverse weighting is counterproductive, effectively teaching the model to favor popular items by reducing the penalty for misclassifying them. I'm now focused on rectifying this weighting to correctly penalize popular items.
### Recorded reasoning summary
Correcting Popularity Bias
I'm now rectifying the loss calculation that incorrectly downweighted popular items when they served as negatives. This correction aims to properly penalize popular items, mitigating the observed popularity bias.
### Recorded reasoning summary
Correcting Loss Weights
I'm realizing the loss calculation was improperly downweighting popular items when they appeared as negatives. This correction aims to properly penalize popular items, mitigating the popularity bias.
### Recorded reasoning summary
Adjusting Loss Weights
I'm confirming that the loss function was indeed downweighting popular items as negatives. I'm now adjusting this to correctly penalize them, aiming to reduce popularity bias.
### Recorded reasoning summary
Analyzing Training Efficiency
I'm investigating the training bottlenecks, realizing that Python loops for building dense blocks and array operations are consuming significant time. I'm exploring profiling and optimization strategies to speed up the single epoch training time, which currently takes around 3.5 minutes.
### Recorded reasoning summary
Detecting Sampling Flaws
I'm examining code snippets related to invalid negative sampling. It appears a comparison `negatives[:, :, None] == liked_items[:, None, :]` is being used, and I'm scrutinizing the shapes involved to understand its precise impact and how it might be contributing to biases.
### Recorded reasoning summary
Refining Negative Selection
I'm scrutinizing the intricate logic for invalid negative sampling, particularly a broad boolean comparison that generates millions of flags per microbatch. Furthermore, I'm examining `torch.unique` operations on large item sets and their subsequent encoding, aiming to optimize this for efficiency and accuracy in negative candidate selection.
### Recorded reasoning summary
Analyzing Candidate Pools
I'm realizing the core task is unseen track recommendation. My validation and testing candidate sets strictly exclude previously encountered items, meaning I must focus on genuine discovery. This clarifies the importance of accurately representing interactions and the potential pitfalls of training on data that mirrors validation/test sets too closely.
### Recorded reasoning summary
Revising Weighting Logic
I'm realizing the "organic-strong" weighting scheme assigns equal importance to all listens, including repeats. This is problematic as repeat listens cannot represent genuine discovery, yet they're being weighted equally. I need to adjust this to specifically downweight repeat listens.
### Recorded reasoning summary
Analyzing Discovery Weighting
I'm examining the "discovery-graded" weighting scheme. It appears to fetch "organic-like outcomes" and uses a "discovery horizon" to grade interactions. I need to understand precisely how this grading is applied to historical data and if repeat listens are being correctly handled or downweighted as suspected.
### Recorded reasoning summary
Adjusting Discovery Weighting
I'm refining how repeat listens are handled. The current "ordinary\_target\_weight" is applied to them, preventing them from being considered genuine discovery outcomes and thus correctly downweighting their influence.
### Recorded reasoning summary
Investigating Default Weights
I'm revisiting the default weighting scheme. It seems `organic-strong` treats all listens equally, including repeats. I need to confirm if `discovery-graded` is correctly implemented to downweight these repeats and understand why `organic-strong` is the default if it doesn't align with discovery goals.
### Recorded reasoning summary
Revising Negative Sampling Strategy
I'm meticulously re-examining the negative sampling process. It appears the baseline's strategy, particularly the in-batch negative selection, is uniformly sampling from unique positive targets rather than proportionally to their frequency. I'm investigating the implications of this for popularity bias and exploring literature for optimal proportional sampling methods.
### Recorded reasoning summary
Correcting In-Batch Sampling
I'm realizing the in-batch negative sampling was uniformly picking from unique targets rather than proportionally to their frequency. I'm now looking at implementing proportional sampling and correcting the loss calculation to properly account for it.
### Recorded reasoning summary
Sampling Negatives Proportionaly
I'm realizing that the previous in-batch negative sampling was uniformly picking from unique targets, missing the crucial frequency proportionality. I'm now exploring sampling directly from the `targets` array to achieve this, which would make popular items significantly more likely to be selected as negatives. I'm also considering mixed negative sampling to incorporate global catalog popularity.
### Recorded reasoning summary
Revising Negative Sampling
I'm now focusing on the core issue: the previous in-batch negative sampling uniformly picked from unique targets, neglecting crucial frequency proportionality. I'm exploring sampling directly from the `targets` array to ensure popular items are more likely chosen as negatives.
### Recorded reasoning summary
Correcting Correction Factors
I'm analyzing the `build_item_correction` function and realizing how it generates inverse propensity weights. Items with high probabilities receive small correction factors, while those with low probabilities get large ones. This seems to be applied in the loss calculation and I'm investigating its precise impact on popularity bias.
### Recorded reasoning summary
Correcting Loss Weights
I'm seeing that popular items are being heavily downweighted as negatives, with weights as low as 0.25, while unpopular items are upweighted. This is fundamentally counterproductive and amplifying popularity bias by reducing the penalty for predicting popular negatives. I am correcting this logic.
### Recorded reasoning summary
Correcting Inverse Weighting
I'm realizing my previous implementation of inverse propensity weighting was flawed. By heavily downweighting popular items as negatives, it was amplifying popularity bias. I am now correcting this to apply appropriate weighting, ensuring popular items are correctly penalized.
### Recorded reasoning summary
Investigating Logit Calculation
I'm realizing that my current approach to logit calculation might be amplifying popularity bias. The method of downweighting popular items when they appear as negatives is counterproductive, and I'm now focused on rectifying this to ensure accurate penalization.
### Recorded reasoning summary
Understanding Retrieval Loss Functions
I'm realizing that retrievers are trained using Sampled Softmax or InfoNCE (Cross-Entropy), not Binary Cross-Entropy (BCE). InfoNCE directly optimizes for ranking the positive item above negatives, focusing gradients on the hardest negatives due to the temperature parameter, unlike BCE which treats each as an independent classification.
### Recorded reasoning summary
Testing InfoNCE Correction
I'm now investigating if InfoNCE or Cross-Entropy can be leveraged for bias correction. My current thoughts are centered on testing this approach by examining how it influences logit calculations and gradients compared to BCE, aiming to directly mitigate popularity bias during training.
### Recorded reasoning summary
Testing InfoNCE Approach
I'm exploring how InfoNCE and Cross-Entropy loss functions can be leveraged for bias correction. My current focus is on how these functions impact logit calculations and gradients, aiming to directly address popularity bias during training by optimizing the ranking of positive items over negatives.
### Recorded reasoning summary
Testing Loss Functions
I am now exploring how InfoNCE and Cross-Entropy loss functions can be used for bias correction. My current focus is on how these functions affect logit calculations and gradients, aiming to directly address popularity bias during training by optimizing the ranking of positive items over negatives.
### run_shell_command · 2026-09-29T05:49:55.026Z
{
"description": "Test validation with discovery-graded target weighting",
"command": "time python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/prepared_val.npz \\\n --target-weighting discovery-graded \\\n --output /app/solution/val_discovery_graded.parquet"
}
### Tool output · 2026-09-29T05:55:17.726Z
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=198.4s
epoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=211.6 validation_seconds=15.3
restored best validation-BCE checkpoint: epoch=1 loss=0.504831
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/val_discovery_graded.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 318.361,
"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": 0.5421154213454774,
"positive_logit": 0.7774311381163012,
"negative_logit": -0.6407561985568945,
"pair_accuracy": 0.7602795423917307,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 211.6158940570001,
"validation_loss": 0.5048305705104994,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.314511568999933
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7521852254867554,
"query_norm_std": 0.8638167977333069,
"item_norm_mean": 1.8543914556503296,
"item_norm_std": 0.8459439873695374
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
real 5m22.684s
user 29m58.539s
sys 1m42.847s
Process Group PGID: 1882
## Step 33 · agent · 2026-09-29T05:55:25.338Z
### run_shell_command · 2026-09-29T05:55:25.338Z
{
"description": "Evaluate discovery-graded validation ranking",
"command": "python /task/evaluate_public.py /app/solution/val_discovery_graded.parquet"
}
### Tool output · 2026-09-29T05:55:28.709Z
Output: {
"model": "/app/solution/val_discovery_graded.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 1943
## Step 34 · agent · 2026-09-29T05:55:32.368Z
### Recorded reasoning summary
Improving NDCG@10 Performance
I'm pleased to report a significant 10.5% relative improvement in NDCG@10, reaching 0.02352, within a swift five-minute runtime. My next steps involve documenting these findings and shifting focus to diagnosing and experimenting with negative sampling strategies.
### update_topic · 2026-09-29T05:55:32.368Z
{
"summary": "Completed the initial exploration and benchmarking of the production baseline. Established the baseline validation NDCG@10 at 0.02128. Confirmed that the model suffers from severe popularity bias where a small fraction of catalog items dominate recommendations for almost all users. Verified that using discovery-graded target weighting improves NDCG@10 to 0.02352. Now focusing on diagnosing and fixing the negative sampling and loss objective under the CPU compute budget.",
"strategic_intent": "Investigate negative sampling limitations and test improved negative sampling strategies.",
"title": "Negative Sampling Investigation and Objective Optimization"
}
### Tool output · 2026-09-29T05:55:32.372Z
Current topic: "Negative Sampling Investigation and Objective Optimization"
Topic summary: Completed the initial exploration and benchmarking of the production baseline. Established the baseline validation NDCG@10 at 0.02128. Confirmed that the model suffers from severe popularity bias where a small fraction of catalog items dominate recommendations for almost all users. Verified that using discovery-graded target weighting improves NDCG@10 to 0.02352. Now focusing on diagnosing and fixing the negative sampling and loss objective under the CPU compute budget.
Strategic Intent: Investigate negative sampling limitations and test improved negative sampling strategies.
## Step 35 · agent · 2026-09-29T05:56:12.119Z
### Recorded reasoning summary
Investigating Negative Sampling
I'm currently scrutinizing how the training objective selects and utilizes negative examples, aiming to pinpoint any limitations within the current sampling approach.
### Recorded reasoning summary
Refining Sampling Techniques
I'm now focusing on optimizing the negative sampling strategy to operate efficiently within the existing CPU constraints, ensuring all changes are rigorously validated against the public backtest while respecting established data and serving boundaries.
### Recorded reasoning summary
Optimizing In-Batch Sampling
I'm refining the in-batch negative sampling mechanism. My focus is on intelligently selecting these negatives, particularly when unique target items are scarce, to maximize their impact within the current batch.
### Recorded reasoning summary
Analyzing Sampling Flaws
I've identified a critical flaw in my in-batch negative sampling: `torch.unique` normalizes item frequencies within a batch to one. This undermines the empirical frequency distribution, causing rare and popular items to be sampled equally. I'm now investigating how the subsequent `correction` mechanism attempts to address this.
### Recorded reasoning summary
Investigating Correction Mechanism
I'm now scrutinizing how the correction mechanism operates within the loss calculation, specifically examining its interaction with the weights assigned to popular versus rare items.
### Recorded reasoning summary
Revisiting Loss Calculations
I'm now revisiting the loss calculations, specifically comparing InfoNCE and BCE with in-batch negatives. I'm analyzing how the log-Q correction and temperature parameter interact with sampling probabilities for both popular and rare items, seeking to understand gradient flow.
### Recorded reasoning summary
Evaluating Temperature Scaling
I'm now analyzing the impact of the temperature parameter on logit scaling within my current loss formulation. I'm comparing how different temperature values, particularly when vectors are L2-normalized, influence the resulting gradients and their effectiveness for ranking.
### Recorded reasoning summary
Analyzing Logit Interactions
I'm now examining how L2 normalization of item and user vectors impacts the dot product, effectively turning it into cosine similarity. I'm investigating if this affects the subsequent logit interactions within the loss calculation.
### Recorded reasoning summary
Re-evaluating Score Calculation
I'm verifying if the serving path truly requires a raw dot product. My current analysis confirms that the standard two-tower score calculation, `queries @ items.T`, is indeed a dot product, and I'm now moving to analyze the limitations within the current negative sampling strategy in `train_current_model.py`.
### Recorded reasoning summary
Investigating Weighting Imbalance
I've discovered a significant issue where popular items receive negligible penalty in the negative loss, preventing the model from learning to avoid recommending them inappropriately. This imbalance needs immediate correction.
### Recorded reasoning summary
Correcting Sampling Bias
I've identified a critical sampling flaw: `torch.unique` normalizes item frequencies, leading to uniform sampling of negatives regardless of their actual occurrence. This is further compounded by an incorrect 0.25 downweighting, effectively preventing popular items from serving as effective negatives. I'm now focused on rectifying this bias to ensure a more balanced learning signal.
### Recorded reasoning summary
Correcting Negative Sample Loss
I'm now addressing a significant issue where popular items receive minimal penalty in the negative loss, hindering the model's ability to learn appropriate avoidance. This bias must be rectified.
### Recorded reasoning summary
Analyzing Gradient Imbalance
I'm currently analyzing a critical imbalance where positive gradients are significantly stronger than negative ones, up to 32x. This prevents sharp discrimination. I'm exploring InfoNCE as a solution that forces competition and directs gradients towards harder negatives.
### Recorded reasoning summary
Testing Sampling Fixes
I'm now preparing to empirically test specific fixes for the negative sampling strategy. My focus is on removing the inverse propensity downweighting of popular negatives to observe its impact on NDCG@10.
### Recorded reasoning summary
Testing Loss Variations
I am now exploring the impact of different loss functions and sampling strategies. Specifically, I am investigating whether sampling negatives from `targets` or catalog popularity offers improvements, and testing `inbatch_correction_power = 0.0` and InfoNCE loss to analyze gradient behavior and potential for sharper discrimination.
### Recorded reasoning summary
Analyzing Loss Function Interactions
I'm currently re-evaluating how the chosen loss functions interact with sampling strategies. I'm focusing on the implications of negative sampling probabilities and potential corrections for popular items to ensure effective discrimination.
### Recorded reasoning summary
Testing Temperature and InfoNCE
I'm investigating the impact of InfoNCE loss and temperature scaling ($\tau$). Initial checks suggest a low $\tau$ (like 0.2) with L2-normalized vectors could drastically improve pair accuracy by making the difference between positive and negative scores much larger, potentially enabling 90%+ accuracy. I'm now assessing the feasibility and speed of running validation experiments.
### run_shell_command · 2026-09-29T05:56:12.119Z
{
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport torch\n\nB = 4000\nK = 32\nL = 48\nnegatives = np.random.randint(0, 30000, (B, K))\ntargets = np.random.randint(0, 30000, B)\nliked_items = np.random.randint(0, 30000, (B, L))\nuser_rows = np.random.randint(0, 9000, B)\nknown_packed = np.zeros((9000, 3750), dtype=np.uint8)\n\nt0 = time.time()\nfor _ in range(10):\n byte_cols = negatives >> 3\n bit_cols = negatives & 7\n known = known_packed[user_rows[:, None], byte_cols]\n invalid = ((known >> bit_cols) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for col in range(1, K):\n invalid[:, col] |= (negatives[:, col, None] == negatives[:, :col]).any(axis=1)\nt1 = time.time()\nprint('Time for 10 checks:', t1 - t0, 'seconds (per call:', (t1 - t0)/10, ')')\n\"",
"description": "Benchmark invalid_full_history_negatives"
}
### Tool output · 2026-09-29T05:56:15.220Z
Output: Time for 10 checks: 0.14188003540039062 seconds (per call: 0.014188003540039063 )
Process Group PGID: 1969
## Step 36 · agent · 2026-09-29T06:01:51.560Z
### Recorded reasoning summary
Analyzing Batch Performance
My recent focus has been on understanding the performance bottleneck within batch processing. I've observed that individual calls are quite fast, around 14ms. However, I'm now investigating the overhead associated with `candidate_vectors`, specifically how the concatenation, unique item identification, and subsequent encoding steps contribute to the overall ~13-second processing time for 266 batches.
### Recorded reasoning summary
Revising Loss Function
I'm now re-evaluating the training objective. The current sample-weighted Binary Cross-Entropy with softplus on negative logits, averaging across negatives, is problematic for retrieval tasks that prioritize relative ranking. I'm exploring alternatives like InfoNCE or sampled softmax to better address the lack of competition among candidates and improve gradient flow.
### Recorded reasoning summary
Refining Negative Sampling
My thoughts are now intensely focused on the severe flaws discovered in the in-batch negative sampling strategy. The uniform sampling of unique targets, rather than popularity-weighted sampling, and the subsequent inverse-propensity weighting are actively hindering performance by disproportionately downweighting popular items, leading to near-zero negative penalties for them.
### Recorded reasoning summary
Investigating Negative Examples
I'm now scrutinizing how explicit dislikes are being handled. It appears they are entirely overlooked as negative training examples, which is a significant oversight given their high confidence. Furthermore, the current scope of negative filtering, while good at excluding positives, doesn't seem to account for these explicitly disliked items.
### Recorded reasoning summary
Fixing Negative Sampling
My current focus is on rectifying the in-batch negative sampling. I'm investigating frequency-based sampling from `targets`, or popularity-weighted sampling, alongside evaluating InfoNCE loss and removing the downweighting of popular items in the negative loss.
### Recorded reasoning summary
Revising Negative Loss Calculation
I'm digging into the `corrected_negative_loss` function, specifically how the division by `weights.sum(dim=1)` might be problematic. This calculation appears to be a key area for potential optimization and refinement in how negative examples are weighted.
### Recorded reasoning summary
Revisiting Sampling Logic
I'm reassessing the core mathematical objective of in-batch negative sampling, particularly how log-Q correction impacts logits. My current hypothesis is that the correction should be a subtraction from the logit before exponentiation, not an external weight applied later, which seems to be where my implementation diverges.
### Recorded reasoning summary
Adjusting Logit Corrections
I'm reassessing how log-Q corrections are applied. My thinking now is that subtracting $-\log Q(j)$ from logits before exponentiation in the softmax can better adjust for popularity bias. This approach seems to align with correcting for biased sampling in logistic regression, ensuring learned preferences reflect true relevance.
### Recorded reasoning summary
Correcting Logit Bias
I've realized my previous approach to log-Q correction was backward. Adding $\log Q(i)$ to negative logits actually increases the penalty for popular negatives, which is the desired effect. The baseline code's multiplicative approach actually diminished this penalty. I am now investigating if InfoNCE or sampled softmax offers further advantages over BCE for this ranking task.
### Recorded reasoning summary
Testing InfoNCE Implementation
I'm now focused on verifying the practical impact of replacing Binary Cross-Entropy with InfoNCE loss within `score_target_microbatches`. My current thinking involves directly implementing this change and conducting validation runs to observe its performance characteristics, particularly regarding hard negative focus and gradient behavior.
### Recorded reasoning summary
Validating Gradient Flow
I'm now carefully examining the gradient propagation during the `backward` pass in `score_target_microbatches`. My focus is on confirming that gradients are correctly flowing back to `sequence_input` and the model parameters after the loss calculation, ensuring effective learning from the entire process.
### Recorded reasoning summary
Verifying Gradient Propagation
I'm now confirming that the `backward` call correctly propagates gradients through the GRU for any scalar loss sum, including when using InfoNCE. This also applies to `validation_loss` calculation in `evaluate_validation_bce`, ensuring early stopping based on validation InfoNCE loss is accurate. I'm also investigating the sampling logic for negatives in `draw_full_history_filtered_negatives`.
### Recorded reasoning summary
Refining Sampling Strategy
I'm now questioning the necessity of using `unique_targets` for sampling in-batch negatives. My current hypothesis is that sampling directly from `targets_cpu` might naturally increase the probability of selecting more frequent items, thereby addressing popularity bias without explicit weighting. I'm also considering how to handle cases where a sampled negative might inadvertently be the actual target.
### run_shell_command · 2026-09-29T06:01:51.560Z
{
"description": "Benchmark torch.multinomial",
"command": "python3 -c \"\nimport time\nimport torch\n\nweights = torch.rand(30000)\nt0 = time.time()\nfor _ in range(10):\n samples = torch.multinomial(weights, 6400 * 16, replacement=True)\nt1 = time.time()\nprint('Time for 10 multinomial draws of 6400*16:', t1 - t0, 's (per draw:', (t1 - t0)/10, ')')\n\""
}
### Tool output · 2026-09-29T06:01:54.555Z
Output: Time for 10 multinomial draws of 6400*16: 0.11437463760375977 s (per draw: 0.011437463760375976 )
Process Group PGID: 2009
## Step 37 · agent · 2026-09-29T06:05:05.286Z
### Recorded reasoning summary
Exploring Catalog Sampling
I'm focusing on how sampling negatives from catalog popularity, specifically with an exponent $\alpha$ like 0.5 or 0.75, impacts batch quality. This approach seems to consistently yield high-quality negatives drawn directly from the catalog distribution.
### run_shell_command · 2026-09-29T06:05:05.286Z
{
"description": "Inspect train_dislikes statistics",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Dislikes users:', dislikes.uid.nunique())\nprint('Total dislike events:', len(dislikes))\nprint('Dislikes per user min/mean/max:', dislikes.groupby('uid').size().min(), dislikes.groupby('uid').size().mean(), dislikes.groupby('uid').size().max())\n\""
}
### Tool output · 2026-09-29T06:05:06.834Z
Output: Dislikes users: 5732
Total dislike events: 101075
Dislikes per user min/mean/max: 1 17.633461270062806 2985
Process Group PGID: 2037
## Step 38 · agent · 2026-09-29T06:05:24.884Z
### Recorded reasoning summary
Analyzing Dislike Data
My current focus is on understanding the "dislike" data. I've noted that a significant number of users have dislikes, averaging around 17.6 per user. I'm also confirming that `prepared.disliked_states` is already constructed within `train_current_model.py` and is being populated at each timestep with the relevant state information.
### Recorded reasoning summary
Exploring Loss Functions
I've been investigating the impact of different loss functions on model performance. Specifically, I'm now focused on comparing Softmax/InfoNCE loss against BCE loss and exploring the efficacy of various negative sampling strategies, including popularity-based, uniform, and in-batch negatives, along with hard negatives derived from user dislikes. I am planning an experiment to test InfoNCE loss.
### Recorded reasoning summary
Refining Loss Calculations
I'm now diving into the specifics of the InfoNCE loss implementation. I'm carefully examining how the `corrected_negative_loss` interacts with the `F.cross_entropy` function, especially when incorporating proposal distribution corrections (`log_Q`) and temperature scaling. My next step is to ensure this correction is applied correctly within the sampled softmax framework.
### Recorded reasoning summary
Testing InfoNCE Objective
I'm now focused on empirically validating the InfoNCE loss. I plan to introduce an `--objective` flag to `train_current_model.py` to easily switch between BCE and InfoNCE, and will then run an immediate validation test. My initial thoughts suggest that `F.cross_entropy` may offer a performance advantage over a manual InfoNCE implementation due to its optimized C++ kernel.
### Recorded reasoning summary
Optimizing InfoNCE Training
I'm now investigating how `cross_entropy`'s fused C++ kernel could accelerate InfoNCE, potentially outperforming manual implementations. I'm exploring adding an `--objective` flag to `train_current_model.py` for easy switching. My current focus is on determining an optimal `tau` value by examining logit differences and standard two-tower retrieval temperature values, considering that my model's vectors are not unit-normalized.
### read_file · 2026-09-29T06:05:24.884Z
{
"start_line": 1120,
"end_line": 1200,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:05:24.893Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1120-1200 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1201.
--- FILE CONTENT (truncated) ---
negative_weights: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
"""Compute one logical block's exact BCE in bounded-memory slices."""
target_count = len(positive_items)
total_weight = float(sample_weights.sum())
if total_weight <= 0:
raise RuntimeError("target block has no positive sample weight")
sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
statistics = {
"loss_sum": 0.0,
"positive_sum": 0.0,
"negative_sum": 0.0,
"pair_correct": 0.0,
"weight_sum": 0.0,
}
for begin in range(0, target_count, config.loss_microbatch_size):
end = min(begin + config.loss_microbatch_size, target_count)
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
## Step 39 · agent · 2026-09-29T06:05:28.416Z
### read_file · 2026-09-29T06:05:28.416Z
{
"start_line": 35,
"end_line": 75,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:05:28.422Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 35-75 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 76.
--- FILE CONTENT (truncated) ---
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)
## Step 40 · agent · 2026-09-29T06:05:38.857Z
### Recorded reasoning summary
Refining Training Parameters
I'm currently exploring additions to the `Config` for training, specifically introducing options for objectives like "bce" or "infonce," alongside a temperature setting and parameters for catalog popularity negatives, including a power adjustment.
### Recorded reasoning summary
Analyzing Objective Logic
I'm currently re-examining the interplay between different objectives and their associated loss calculations. My focus is on precisely tracing how the `score_target_microbatches` function handles the "infonce" objective and its reliance on temperature and negative sampling strategies.
### Recorded reasoning summary
Incorporating Log-Q Correction
I'm digging into the specifics of the InfoNCE objective, realizing I need to account for the log-Q correction. I'm currently analyzing how to properly implement this, especially considering how sampling probabilities ($Q(j)$) differ for uniform versus popularity-based negatives and how that affects the logit calculation.
### read_file · 2026-09-29T06:05:38.857Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1990,
"start_line": 1930
}
### Tool output · 2026-09-29T06:05:38.865Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1930-1990 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1991.
--- FILE CONTENT (truncated) ---
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
## Step 41 · agent · 2026-09-29T06:05:45.641Z
### Recorded reasoning summary
Exploring Negative Sampling
I'm currently investigating how to enhance the negative sampling strategy. My focus is on exploring alternative methods beyond the current uniform negative sampling and in-batch negatives, aiming to optimize its effectiveness.
### Recorded reasoning summary
Analyzing In-Batch Negatives
I'm reviewing the logic for in-batch negatives. Specifically, I'm questioning why `unique_targets` is used for sampling and examining the implementation details in `/app/solution/train_current_model.py`, particularly the conditional masking.
### run_shell_command · 2026-09-29T06:05:45.641Z
{
"command": "python3 -c \"\nimport time\nimport torch\n\nt0 = time.time()\nfor _ in range(10):\n p = torch.rand((6400, 3000))\n top = torch.topk(p, 8, dim=1)\nt1 = time.time()\nprint('Time for 10 topk(6400, 3000):', t1 - t0, 's (per call:', (t1 - t0)/10, 's)')\n\"",
"description": "Benchmark topk on 6400x3000"
}
### Tool output · 2026-09-29T06:05:49.655Z
Output: Time for 10 topk(6400, 3000): 1.11387300491333 s (per call: 0.11138730049133301 s)
Process Group PGID: 2060
## Step 42 · agent · 2026-09-29T06:06:03.546Z
### Recorded reasoning summary
Optimizing Data Sampling
I'm realizing our current random sampling is incredibly inefficient, consuming significant CPU time and losing crucial frequency data. My focus is shifting towards finding a more performant and accurate method to select our data points.
### Recorded reasoning summary
Refining Sampling Strategy
My exploration into sampling methods has yielded significant insights. I've discovered a way to drastically accelerate index generation and preserve true item frequency by sampling 8 random indices per batch, achieving a 1000x speedup. I'm now investigating how incorporating catalog popularity, with exponents like 0.5 or 0.75, can further enhance this approach, drawing from established recommendation system literature.
### Recorded reasoning summary
Exploring Negative Sampling
I'm considering how to best leverage negative sampling. By combining uniform negatives for global coverage, popularity-based negatives to prevent over-biasing towards popular items, and in-batch negatives for local context, I believe we can significantly improve the model's learning. I'm now investigating the potential of incorporating user-disliked tracks into this strategy, examining existing data structures for feasibility.
### Recorded reasoning summary
Incorporating Disliked Items
I'm realizing the immense potential of using explicitly disliked items as strong negatives. My immediate plan is to incrementally test this by first experimenting with InfoNCE loss at different temperatures ($\tau = 0.1, 0.2, 0.5$) before integrating this new negative sampling strategy.
### Recorded reasoning summary
Testing Temperature Sensitivity
My current focus is on the InfoNCE loss implementation. I'm systematically evaluating how different temperature values, specifically 0.1, 0.2, and 0.5, impact the loss calculation, aiming to find the optimal setting before proceeding with the negative sampling integration.
### Recorded reasoning summary
Evaluating Loss Objective
I'm now focused on integrating the InfoNCE loss and understanding its temperature sensitivity. My immediate plan is to modify the training script to accept `--objective` and `--temperature` arguments, then run initial tests with `--target-weighting discovery-graded` and `--temperature 0.2` to assess the impact.
### replace · 2026-09-29T06:06:03.546Z
{
"instruction": "Add objective and temperature parameters to Config",
"old_string": " negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"",
"file_path": "/app/solution/train_current_model.py",
"new_string": " negative_filter_scope: str = \"full-training-history\"\n objective: str = \"bce\"\n temperature: float = 0.2\n target_weighting: str = \"organic-strong\"",
"allow_multiple": false
}
### Tool error · 2026-09-29T06:06:03.554Z
EACCES: permission denied, open '/app/solution/train_current_model.py'
## Step 43 · agent · 2026-09-29T06:06:39.409Z
### run_shell_command · 2026-09-29T06:06:39.409Z
{
"command": "ls -l /app/solution/",
"description": "Check permissions in /app/solution"
}
### Tool output · 2026-09-29T06:06:39.428Z
Output: total 1064960
-rw-r--r-- 1 agent agent 552686232 Sep 29 05:47 prepared_sub.npz
-rw-r--r-- 1 agent agent 535225808 Sep 29 05:45 prepared_val.npz
-rw-r--r-- 1 agent agent 1244230 Sep 29 05:43 production_validation.parquet
-r--r--r-- 1 agent agent 80058 Sep 29 05:46 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 05:46 two_tower_model.py
-rw-r--r-- 1 agent agent 1249069 Sep 29 05:55 val_discovery_graded.parquet
Process Group PGID: 2079
## Step 44 · agent · 2026-09-29T06:06:43.315Z
### run_shell_command · 2026-09-29T06:06:43.315Z
{
"command": "chmod u+w /app/solution/*.py",
"description": "Make copied python files writable in /app/solution"
}
### Tool output · 2026-09-29T06:06:43.333Z
Output: (empty)
Process Group PGID: 2082
## Step 45 · agent · 2026-09-29T06:06:46.031Z
### replace · 2026-09-29T06:06:46.031Z
{
"allow_multiple": false,
"old_string": " negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"",
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add objective and temperature parameters to Config",
"new_string": " negative_filter_scope: str = \"full-training-history\"\n objective: str = \"bce\"\n temperature: float = 0.2\n target_weighting: str = \"organic-strong\""
}
### Tool output · 2026-09-29T06:06:46.052Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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"
objective: str = "bce"
temperature: float = 0.2
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
...
## Step 46 · agent · 2026-09-29T06:06:50.731Z
### read_file · 2026-09-29T06:06:50.731Z
{
"start_line": 1160,
"file_path": "/app/solution/train_current_model.py",
"end_line": 1190
}
### Tool output · 2026-09-29T06:06:52.225Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1160-1190 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: 1191.
--- FILE CONTENT (truncated) ---
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())
## Step 47 · agent · 2026-09-29T06:06:57.247Z
### replace · 2026-09-29T06:06:57.247Z
{
"instruction": "Support infonce objective alongside bce 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",
"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.objective == \"infonce\":\n cat_logits = torch.cat(\n [positive_logits.unsqueeze(1), negative_logits], dim=1\n ) / config.temperature\n labels = torch.zeros(\n len(positive_logits), dtype=torch.long, device=device\n )\n raw_loss = F.cross_entropy(cat_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * raw_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()",
"allow_multiple": false
}
### Tool output · 2026-09-29T06:06:57.267Z
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.objective == "infonce":
cat_logits = torch.cat(
[positive_logits.unsqueeze(1), negative_logits], dim=1
) / config.temperature
labels = torch.zeros(
len(positive_logits), dtype=torch.long, device=device
)
raw_loss = F.cross_entropy(cat_logits, labels, reduction="none")
loss_sum = (selected_weights * raw_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 48 · agent · 2026-09-29T06:07:01.210Z
### read_file · 2026-09-29T06:07:01.210Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 2050,
"start_line": 1940
}
### Tool output · 2026-09-29T06:07:01.217Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1940-2050 of 2177 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("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
## Step 49 · agent · 2026-09-29T06:07:04.043Z
### read_file · 2026-09-29T06:07:04.043Z
{
"end_line": 2070,
"file_path": "/app/solution/train_current_model.py",
"start_line": 2045
}
### Tool output · 2026-09-29T06:07:04.049Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2045-2070 of 2177 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) ---
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,
## Step 50 · agent · 2026-09-29T06:07:10.132Z
### replace · 2026-09-29T06:07:10.132Z
{
"instruction": "Add CLI arguments for objective and temperature",
"file_path": "/app/solution/train_current_model.py",
"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 \"--objective\",\n choices=(\"bce\", \"infonce\"),\n default=Config.objective,\n )\n parser.add_argument(\n \"--temperature\",\n type=float,\n default=Config.temperature,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )",
"old_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 \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )",
"allow_multiple": false
}
### Tool output · 2026-09-29T06:07:10.148Z
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(
"--objective",
choices=("bce", "infonce"),
default=Config.objective,
)
parser.add_argument(
"--temperature",
type=float,
default=Config.temperature,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
...
## Step 51 · agent · 2026-09-29T06:07:13.096Z
### read_file · 2026-09-29T06:07:13.096Z
{
"start_line": 2045,
"end_line": 2075,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:07:13.103Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2045-2075 of 2187 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2076.
--- FILE CONTENT (truncated) ---
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")
## Step 52 · agent · 2026-09-29T06:07:17.238Z
### replace · 2026-09-29T06:07:17.238Z
{
"file_path": "/app/solution/train_current_model.py",
"instruction": "Pass objective and temperature into Config instantiation",
"allow_multiple": false,
"new_string": " negative_filter_scope=args.negative_filter_scope,\n objective=args.objective,\n temperature=args.temperature,\n target_weighting=args.target_weighting,",
"old_string": " negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,"
}
### Tool output · 2026-09-29T06:07:17.253Z
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,
objective=args.objective,
temperature=args.temperature,
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,
...
## Recorded session usage
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