yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-fixed-r02-3d6fca60
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T05:35:45.528Z
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 · 2026-09-29T05:35:45.575Z
# 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 · 2026-09-29T05:35:51.657Z
### Recorded reasoning summary
Analyzing Project Requirements
My current focus is on thoroughly understanding the project's prompt, all specified requirements, any constraints, and the detailed instructions provided. This foundational step is crucial before proceeding with any improvements.
### Recorded reasoning summary
Deconstructing Project Specifications
I'm now meticulously dissecting the core task, focusing on the provided Python scripts and expected deliverables. My attention is particularly on the exact format and content requirements for the submission file and the `train_current_model.py` script, while also noting all stated constraints.
### update_topic · 2026-09-29T05:35:51.657Z
{
"summary": "Starting initial investigation into the production two-tower model. We will inspect the existing codebase, reproduce the baseline public validation performance, examine the negative sampling and training objective, and generate a baseline submission to ensure a valid artifact is always present.",
"title": "Initial Investigation & Baseline Reproduction",
"strategic_intent": "Initialize investigation into the production two-tower model and establish baseline evaluation."
}
### Tool output · 2026-09-29T05:35:51.666Z
Current topic: "Initial Investigation & Baseline Reproduction"
Topic summary: Starting initial investigation into the production two-tower model. We will inspect the existing codebase, reproduce the baseline public validation performance, examine the negative sampling and training objective, and generate a baseline submission to ensure a valid artifact is always present.
Strategic Intent: Initialize investigation into the production two-tower model and establish baseline evaluation.
## Step 4 · agent · 2026-09-29T05:35:54.736Z
### Recorded reasoning summary
Exploring Task Files
I'm currently reviewing the files within the `/task` directory. My focus is on understanding `train_current_model.py` and `two_tower_model.py`, and how they relate to `evaluate_public.py` and `ranking_evaluation`.
### read_file · 2026-09-29T05:35:54.736Z
{
"start_line": 1,
"file_path": "/task/evaluate_public.py",
"end_line": 150
}
### Tool output · 2026-09-29T05:35:54.751Z
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 · 2026-09-29T05:35:54.736Z
{
"start_line": 1,
"file_path": "/task/ranking_evaluation.py",
"end_line": 150
}
### Tool output · 2026-09-29T05:35:54.751Z
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 · 2026-09-29T05:35:54.736Z
{
"end_line": 150,
"start_line": 1,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:35:54.751Z
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 · 2026-09-29T05:35:54.736Z
{
"end_line": 150,
"start_line": 1,
"file_path": "/task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T05:35:54.751Z
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 5 · agent · 2026-09-29T05:35:56.763Z
### read_file · 2026-09-29T05:35:56.763Z
{
"end_line": 350,
"start_line": 151,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:35:56.767Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-350 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: 351.
--- 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
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)
## Step 6 · agent · 2026-09-29T05:35:58.607Z
### grep_search · 2026-09-29T05:35:58.607Z
{
"dir_path": "/task/current_model",
"pattern": "def "
}
### Tool output · 2026-09-29T05:35:58.624Z
Found 57 matches for pattern "def " in path "/task/current_model":
---
File: train_current_model.py
L82: def read(cls, path: Path) -> "CandidateSet":
L118: def lengths(self) -> np.ndarray:
L122: def target_count(self) -> int:
L136: def selected_count(self) -> int:
L140: def log(message: str) -> None:
L144: def seed_everything(seed: int) -> None:
L150: def model_config(config: Config) -> base.TowerConfig:
L174: def append_state_snapshot(
L190: def causal_activity_features(
L247: def build_sequence_parts(
L314: def final_user_features(
L341: def prepare_data(
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L481: def save_cache(
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L511: def load_or_prepare(
L534: def build_item_correction(
L578: def build_known_positive_filter(
L609: def invalid_full_history_negatives(
L630: def draw_full_history_filtered_negatives(
L724: def draw_training_negatives(
L763: def event_sequence(
L774: def pool_explicit_states(
L798: def user_queries(
L813: def candidate_vectors(
L828: def history_windows(
L845: def corrected_negative_loss(
L867: def retained_target_window_starts(
L888: def chronological_target_partition(
L914: def load_organic_like_outcomes(
L955: def build_target_weights(
L1035: def build_dense_block(
L1111: def score_target_microbatches(
L1198: def release_accelerator_cache(device: torch.device) -> None:
L1203: def run_training_epoch(
L1364: def evaluate_validation_bce(
L1488: def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
L1495: def save_epoch_checkpoint(
L1521: def train_model(
L1782: def inference_queries(
L1861: def rank_candidates(
L1912: def write_ranking(frame: pd.DataFrame, output: Path) -> None:
L1922: def main() -> int:
---
File: two_tower_model.py
L65: def log(message: str) -> None:
L69: def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
L90: def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
L106: def iter_listen_users(
L157: def merge_listen_streams(
L175: def load_explicit_events(
L228: def empty_explicit_events() -> ExplicitEvents:
L238: def pack_active_items(
L251: def load_inference_state(
L287: def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
L332: def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
L339: def encode_items(self, items: torch.Tensor) -> torch.Tensor:
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---
## Step 7 · agent · 2026-09-29T05:36:03.693Z
### Recorded reasoning summary
Exploring Model Training Logic
I'm currently examining the functions within `train_current_model.py`, specifically focusing on `build_item_correction`, `build_known_positive_filter`, `invalid_full_history_negatives`, `draw_full_history_filtered_negatives`, and `draw_training`. My aim is to understand the core mechanics of how the model is being trained and how negative sampling is handled.
### Recorded reasoning summary
Investigating Negative Sampling
I'm delving into the negative sampling logic within `train_current_model.py`, specifically exploring `invalid_full_history_negatives` and `draw_full_history_filtered_negatives`. My focus is on understanding the nuances of how these strategies are implemented and their impact on training, especially concerning the prompt's emphasis on strict CPU budget constraints.
### read_file · 2026-09-29T05:36:03.693Z
{
"file_path": "/task/current_model/train_current_model.py",
"end_line": 720,
"start_line": 530
}
### Tool output · 2026-09-29T05:36:03.698Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 530-720 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 721.
--- FILE CONTENT (truncated) ---
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""Estimate in-batch propensities from the selected training targets."""
counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
for user_row, count in enumerate(target_counts):
if not count:
continue
offset = int(prepared.offsets[user_row])
target_start = int(target_starts[user_row])
counts += np.bincount(
prepared.sequence_items[
offset
+ target_start
+ 1 : offset
+ target_start
+ int(count)
+ 1
].astype(np.int64),
minlength=len(prepared.item_ids),
)
total = float(counts.sum())
probabilities = (counts + 1.0) / (total + len(counts))
raw = np.power(
(1.0 / len(counts)) / probabilities,
config.inbatch_correction_power,
)
normalization = float(np.sum(counts * raw) / max(total, 1.0))
correction = np.clip(
raw / max(normalization, 1.0e-12),
config.inbatch_correction_min,
config.inbatch_correction_max,
).astype(np.float32)
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
log(
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
)
return torch.from_numpy(correction)
def build_known_positive_filter(
prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
"""Pack each user's complete public-training positive-item set."""
width = (len(prepared.item_ids) + 7) // 8
packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
retained = 0
for user_row, target_count in enumerate(training_counts):
if not target_count:
continue
offset = int(prepared.offsets[user_row])
# T adjacent targets consume sequence positions 0 through T. The
# chronological BCE suffix starts at T + 1 and is intentionally absent.
items = np.unique(
prepared.sequence_items[
offset : offset + int(target_count) + 1
].astype(np.int64)
)
byte_columns = items >> 3
bit_values = np.left_shift(
np.uint8(1), (items & 7).astype(np.uint8)
)
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
retained += len(items)
log(
f"full-history false-negative filter: users={len(prepared.user_ids):,} "
f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
)
return packed
def invalid_full_history_negatives(
negatives: np.ndarray,
targets: np.ndarray,
user_rows: np.ndarray,
known_positive_packed: np.ndarray,
liked_items: np.ndarray,
) -> np.ndarray:
"""Return row-level invalid negatives using the complete training prefix."""
byte_columns = negatives >> 3
bit_columns = negatives & 7
known = known_positive_packed[user_rows[:, None], byte_columns]
invalid = ((known >> bit_columns) & 1).astype(bool)
invalid |= negatives == targets[:, None]
invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= (
negatives[:, column, None] == negatives[:, :column]
).any(axis=1)
return invalid
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
## Step 8 · agent · 2026-09-29T05:36:33.573Z
### read_file · 2026-09-29T05:36:33.573Z
{
"end_line": 950,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 721
}
### Tool output · 2026-09-29T05:36:33.579Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 721-950 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) ---
)
def draw_training_negatives(
positive_items: torch.Tensor,
known_history: torch.Tensor,
liked_items: torch.Tensor,
user_rows: np.ndarray,
known_positive_packed: np.ndarray | None,
prepared: SequenceData,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
if config.negative_filter_scope == "full-training-history":
if known_positive_packed is None:
raise RuntimeError("full-history negative filter was not built")
return draw_full_history_filtered_negatives(
positive_items,
user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
config,
item_correction,
generator,
device,
)
return base.draw_filtered_mixed_negatives(
positive_items,
known_history,
liked_items,
len(prepared.item_ids),
config.uniform_negatives,
config.inbatch_negatives,
item_correction,
generator,
device,
)
def event_sequence(
model: base.SequentialTwoTower,
items: torch.Tensor,
features: torch.Tensor,
hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
inputs = torch.cat([model.context_embedding(items), features], dim=-1)
projected = model.event_projection(inputs)
return model.sequence_encoder(projected, hidden)
def pool_explicit_states(
model: base.SequentialTwoTower,
prepared: SequenceData,
state_ids: np.ndarray,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
unique_states, inverse = np.unique(state_ids, return_inverse=True)
liked_items = torch.from_numpy(
prepared.liked_states[unique_states].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.disliked_states[unique_states].astype(np.int64)
).to(device)
liked_vectors = model._mean_context_channel(liked_items)
disliked_vectors = model._mean_context_channel(disliked_items)
inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
counts = prepared.state_counts[state_ids]
return (
liked_vectors[inverse_rows],
disliked_vectors[inverse_rows],
torch.from_numpy(counts.astype(np.float32)).to(device),
)
def user_queries(
model: base.SequentialTwoTower,
sequence_vectors: torch.Tensor,
liked_vectors: torch.Tensor,
disliked_vectors: torch.Tensor,
user_features: torch.Tensor,
) -> torch.Tensor:
return model.user_tower(
torch.cat(
[sequence_vectors, liked_vectors, disliked_vectors, user_features],
dim=1,
)
)
def candidate_vectors(
model: base.SequentialTwoTower,
positives: torch.Tensor,
negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
all_items = torch.cat([positives, negatives.reshape(-1)])
unique_items, inverse = torch.unique(all_items, return_inverse=True)
encoded = model.encode_items(unique_items)
positive_vectors = encoded[inverse[: len(positives)]]
negative_vectors = encoded[inverse[len(positives) :]].reshape(
len(positives), negatives.shape[1], -1
)
return positive_vectors, negative_vectors
def history_windows(
sequence: np.ndarray,
start: int,
count: int,
width: int,
padding_item: int,
) -> np.ndarray:
base_start = max(0, start - width + 1)
segment = sequence[base_start : start + count]
padded = np.concatenate(
[np.full(width - 1, padding_item, dtype=np.uint16), segment]
)
windows = np.lib.stride_tricks.sliding_window_view(padded, width)
offset = start - base_start
return windows[offset : offset + count].copy()
def corrected_negative_loss(
logits: torch.Tensor,
correction: torch.Tensor,
config: Config,
) -> torch.Tensor:
raw = F.softplus(logits)
total = config.uniform_negatives + config.inbatch_negatives
result = torch.zeros(len(logits), device=logits.device)
if config.uniform_negatives:
result += config.uniform_negatives * raw[
:, : config.uniform_negatives
].mean(dim=1)
if config.inbatch_negatives:
selected = slice(config.uniform_negatives, total)
weights = correction[:, selected]
result += config.inbatch_negatives * (
(raw[:, selected] * weights).sum(dim=1)
/ weights.sum(dim=1).clamp_min(1.0e-6)
)
return result / total
def retained_target_window_starts(
total_targets: np.ndarray,
validation_counts: np.ndarray,
training_counts: np.ndarray,
) -> np.ndarray:
"""Return the discarded prefix before the retained chronological suffix."""
training_region_ends = total_targets - validation_counts
window_starts = training_region_ends - training_counts
if (
np.any(window_starts < 0)
or np.any(training_counts < 0)
or np.any(validation_counts < 0)
):
raise RuntimeError("chronological target partition produced negative counts")
if not np.array_equal(
window_starts + training_counts + validation_counts, total_targets
):
raise RuntimeError("chronological target window is not a final contiguous suffix")
return window_starts
def chronological_target_partition(
prepared: SequenceData,
validation_fraction: float,
max_train_targets_per_user: int,
max_validation_targets_per_user: int,
) -> TargetPartition:
"""Select a recent contiguous window with a final out-of-time BCE suffix."""
totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
validation = np.floor(totals * validation_fraction).astype(np.int64)
validation[(totals >= 2) & (validation == 0)] = 1
if max_validation_targets_per_user:
validation = np.minimum(validation, max_validation_targets_per_user)
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 9 · agent · 2026-09-29T05:36:35.481Z
### read_file · 2026-09-29T05:36:35.481Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 951,
"end_line": 1150
}
### Tool output · 2026-09-29T05:36:35.485Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 951-1150 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: 1151.
--- 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]
## Step 10 · agent · 2026-09-29T05:36:38.828Z
### read_file · 2026-09-29T05:36:38.828Z
{
"start_line": 1151,
"file_path": "/task/current_model/train_current_model.py",
"end_line": 1350
}
### Tool output · 2026-09-29T05:36:38.832Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1151-1350 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1351.
--- FILE CONTENT (truncated) ---
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
def run_training_epoch(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
generator: torch.Generator,
device: torch.device,
epoch: int,
) -> dict[str, float]:
model.train()
train_users = np.flatnonzero(target_counts > 0)
sorted_users = train_users[
np.argsort(target_counts[train_users], kind="stable")
]
groups = [
sorted_users[start : start + config.user_batch_size]
for start in range(0, len(sorted_users), config.user_batch_size)
]
group_order = torch.randperm(len(groups), generator=generator).numpy()
negative_count = config.uniform_negatives + config.inbatch_negatives
total_loss = 0.0
total_targets = 0
total_weight = 0.0
total_positive = 0.0
total_negative = 0.0
total_pair_correct = 0.0
batches = 0
started = time.monotonic()
for group_number in group_order:
users = groups[int(group_number)]
group_counts = target_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_counts.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_counts,
np.zeros(len(users), dtype=np.int64),
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0).detach()
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
## Step 11 · agent · 2026-09-29T05:36:40.835Z
### read_file · 2026-09-29T05:36:40.835Z
{
"end_line": 434,
"start_line": 260,
"file_path": "/task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T05:36:40.839Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 260-434 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) ---
keep = mapped >= 0
state = state.loc[keep, ["uid", "state"]].copy()
state["item"] = mapped[keep].astype(np.int32, copy=False)
user_rows = pd.Index(uids)
liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
like_counts = np.zeros(len(uids), dtype=np.int32)
dislike_counts = np.zeros(len(uids), dtype=np.int32)
for value, width, destination, counts in (
("liked", LIKE_ITEMS, liked, like_counts),
("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
):
selected = state[state.state == value]
for uid, rows in selected.groupby("uid", sort=False):
row = int(user_rows.get_indexer([int(uid)])[0])
if row < 0:
continue
items = np.sort(rows.item.unique())
counts[row] = len(items)
retained = items[:width]
destination[row, : len(retained)] = retained.astype(np.uint16)
return liked, disliked, like_counts, dislike_counts
class SequentialTwoTower(nn.Module):
"""Order-sensitive user tower and independent catalog-aware item tower."""
def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
super().__init__()
n_items, n_item_features = item_features.shape
dim = config.embedding_dim
self.n_items = n_items
self.context_embedding = nn.Embedding(
n_items + 1, dim, padding_idx=n_items
)
self.event_projection = nn.Sequential(
nn.Linear(dim + 3, config.hidden_dim),
nn.GELU(),
)
self.sequence_encoder = nn.GRU(
input_size=config.hidden_dim,
hidden_size=dim,
num_layers=1,
batch_first=True,
)
self.user_tower = nn.Sequential(
nn.Linear(dim * 3 + 6, config.user_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_1, config.user_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_2, dim),
)
self.candidate_embedding = nn.Embedding(n_items, dim)
self.register_buffer(
"item_features", torch.from_numpy(item_features), persistent=True
)
self.item_tower = nn.Sequential(
nn.Linear(dim + n_item_features, config.item_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_1, config.item_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_2, dim),
)
nn.init.normal_(self.context_embedding.weight, std=0.02)
nn.init.normal_(self.candidate_embedding.weight, std=0.02)
with torch.no_grad():
self.context_embedding.weight[n_items].zero_()
def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
mask = items.ne(self.n_items)
vectors = self.context_embedding(items)
return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
dim=1, keepdim=True
).clamp_min(1)
def encode_items(self, items: torch.Tensor) -> torch.Tensor:
inputs = torch.cat(
[self.candidate_embedding(items), self.item_features[items]], dim=1
)
return self.item_tower(inputs)
def invalid_negative_mask(
negatives: torch.Tensor,
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
) -> torch.Tensor:
"""Identify targets, known positives, active likes, and row duplicates."""
invalid = negatives.eq(targets[:, None])
invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= negatives[:, column, None].eq(
negatives[:, :column]
).any(dim=1)
return invalid
def draw_filtered_mixed_negatives(
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
n_items: int,
uniform_count: int,
inbatch_count: int,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw uniform and unique-target in-batch negatives."""
batch_size = len(targets)
uniform = torch.randint(
n_items,
(batch_size, uniform_count),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
-1.0,
)
columns = torch.topk(priorities, k=corrected_count, dim=1).indices
inbatch = unique_targets[columns]
if corrected_count < inbatch_count:
filler = torch.randint(
n_items,
(batch_size, inbatch_count - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
:, uniform_count : uniform_count + corrected_count
] = item_correction[inbatch[:, :corrected_count]]
negatives = negatives_cpu.to(device)
correction = correction_cpu.to(device)
for _ in range(32):
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if not bool(invalid.any()):
return negatives, correction
replacements = torch.randint(
n_items,
(int(invalid.sum().detach().cpu()),),
generator=generator,
device="cpu",
).to(device)
negatives[invalid] = replacements
correction[invalid] = 1.0
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if bool(invalid.any()):
raise RuntimeError("mixed negative resampling failed false-negative filter")
return negatives, correction
## Step 12 · agent · 2026-09-29T05:36:43.361Z
### read_file · 2026-09-29T05:36:43.361Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 1520,
"end_line": 1700
}
### Tool output · 2026-09-29T05:36:43.365Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1520-1700 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: 1701.
--- FILE CONTENT (truncated) ---
def train_model(
public: Path,
stage: str,
prepared: SequenceData,
config: Config,
device: torch.device,
checkpoint_dir: Path | None,
resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
training_counts = partition.training_counts
validation_counts = partition.validation_counts
log(
f"chronological BCE window: train={int(training_counts.sum()):,} "
f"validation={int(validation_counts.sum()):,} "
f"selected={partition.selected_count:,} "
f"discarded_old_targets={int(partition.window_starts.sum()):,} "
f"all={prepared.target_count:,}"
)
model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
device
)
optimizer = torch.optim.AdamW(
model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
)
generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
item_correction = build_item_correction(
prepared,
config,
partition.window_starts,
training_counts,
)
target_weights = build_target_weights(
public,
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
"negative_filter_scope", "last-100"
)
stored_target_weighting = stored_config.get(
"target_weighting", "uniform"
)
stored_weight_values = (
stored_config.get("ordinary_target_weight", 1.0),
stored_config.get("organic_strong_target_weight", 1.0),
stored_config.get("organic_like_target_weight", 3.0),
stored_config.get("discovery_horizon_days", 7),
)
current_weight_values = (
config.ordinary_target_weight,
config.organic_strong_target_weight,
config.organic_like_target_weight,
config.discovery_horizon_days,
)
stored_window_values = (
stored_config.get("validation_fraction", 0.10),
stored_config.get("max_train_targets_per_user", 0),
stored_config.get("max_validation_targets_per_user", 0),
)
current_window_values = (
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
objective_changed = (
stored_filter_scope != config.negative_filter_scope
or stored_target_weighting != config.target_weighting
or stored_weight_values != current_weight_values
or stored_window_values != current_window_values
)
model.load_state_dict(payload["model_state"])
if "optimizer_state" in payload and not objective_changed:
optimizer.load_state_dict(payload["optimizer_state"])
for group in optimizer.param_groups:
group["lr"] = config.learning_rate
group["weight_decay"] = config.weight_decay
log(f"restored optimizer state from {resume_checkpoint}")
else:
log(
f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
)
if "generator_state" in payload and not objective_changed:
generator.set_state(payload["generator_state"])
start_epoch = int(payload["epoch"])
if start_epoch >= config.epochs:
raise RuntimeError(
f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
)
prior = {
str(name): float(value)
for name, value in payload.get("diagnostics", {}).items()
}
if objective_changed:
prior.pop("validation_loss", None)
log(
"BCE objective changed: filter "
f"{stored_filter_scope}->{config.negative_filter_scope}, "
"target weighting "
f"{stored_target_weighting}->{config.target_weighting}, "
f"target window {stored_window_values}->{current_window_values}; "
"recomputing the resume checkpoint's validation BCE"
)
if "validation_loss" not in prior:
prior.update(
evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
)
history.append(prior)
best_loss = float(prior["validation_loss"])
best_epoch = start_epoch
best_state = snapshot_model(model)
log(
f"resumed validation-BCE training: epoch={start_epoch} "
f"loss={best_loss:.6f}"
)
for epoch in range(start_epoch + 1, config.epochs + 1):
diagnostics = run_training_epoch(
model,
optimizer,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
item_correction,
known_positive_packed,
## Step 13 · agent · 2026-09-29T05:36:45.751Z
### read_file · 2026-09-29T05:36:45.751Z
{
"end_line": 2000,
"start_line": 1701,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:36:45.758Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1701-2000 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2001.
--- FILE CONTENT (truncated) ---
generator,
device,
epoch,
)
validation = evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
diagnostics.update(validation)
history.append(diagnostics)
current_state = snapshot_model(model)
if checkpoint_dir is not None:
save_epoch_checkpoint(
checkpoint_dir,
epoch,
current_state,
config,
diagnostics,
optimizer,
generator,
)
improved = (
diagnostics["validation_loss"]
< best_loss - config.early_stopping_min_delta
)
if improved:
best_loss = diagnostics["validation_loss"]
best_epoch = epoch
best_state = current_state
epochs_without_improvement = 0
if checkpoint_dir is not None:
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": current_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
checkpoint_dir / "best.pt",
)
else:
epochs_without_improvement += 1
log(
f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
f"validation_loss={diagnostics['validation_loss']:.5f} "
f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
f"train_targets={int(diagnostics['targets']):,} "
f"validation_targets={int(diagnostics['validation_targets']):,} "
f"train_seconds={diagnostics['seconds']:.1f} "
f"validation_seconds={diagnostics['validation_seconds']:.1f}"
)
release_accelerator_cache(device)
if epochs_without_improvement >= config.early_stopping_patience:
log(
f"early stopping after epoch={epoch}; "
f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
)
break
if best_state is None:
raise RuntimeError("training did not produce a valid checkpoint")
model.load_state_dict(best_state)
model.to(device).eval()
log(
f"restored best validation-BCE checkpoint: epoch={best_epoch} "
f"loss={best_loss:.6f}"
)
return model, history
@torch.inference_mode()
def inference_queries(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
device: torch.device,
) -> torch.Tensor:
sequence_rows = prepared.inference_user_rows
lengths = np.where(
sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
)
ordered = np.argsort(lengths, kind="stable")
result = torch.zeros(
len(sequence_rows), config.embedding_dim, dtype=torch.float32
)
for group_start in range(0, len(ordered), config.user_batch_size):
output_rows = ordered[group_start : group_start + config.user_batch_size]
rows = sequence_rows[output_rows]
group_lengths = lengths[output_rows]
hidden = torch.zeros(
1, len(output_rows), config.embedding_dim, device=device
)
for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
steps = min(
config.bptt_steps,
int(group_lengths.max(initial=0)) - start,
)
items = np.full(
(len(output_rows), steps),
len(prepared.item_ids),
dtype=np.uint16,
)
features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
counts = np.zeros(len(output_rows), dtype=np.int32)
for local_row, sequence_row in enumerate(rows):
if sequence_row < 0:
continue
count = min(steps, max(int(group_lengths[local_row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[sequence_row])
selected = slice(offset + start, offset + start + count)
items[local_row, :count] = prepared.sequence_items[selected]
features[local_row, :count] = prepared.event_features[selected]
counts[local_row] = count
outputs, _ = event_sequence(
model,
torch.from_numpy(items.astype(np.int64)).to(device),
torch.from_numpy(features.astype(np.float32)).to(device),
hidden,
)
next_rows: list[torch.Tensor] = []
for local_row, count in enumerate(counts):
next_rows.append(
outputs[local_row, count - 1]
if count
else hidden[0, local_row]
)
hidden = torch.stack(next_rows).unsqueeze(0)
liked_items = torch.from_numpy(
prepared.inference_liked_items[output_rows].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.inference_disliked_items[output_rows].astype(np.int64)
).to(device)
queries = user_queries(
model,
hidden.squeeze(0),
model._mean_context_channel(liked_items),
model._mean_context_channel(disliked_items),
torch.from_numpy(
prepared.inference_user_features[output_rows].astype(np.float32)
).to(device),
)
result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
return result
@torch.inference_mode()
def rank_candidates(
model: base.SequentialTwoTower,
prepared: SequenceData,
eligibility: CandidateSet,
config: Config,
device: torch.device,
ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
model.eval()
queries = inference_queries(model, prepared, config, device)
item_parts: list[torch.Tensor] = []
for start in range(0, len(prepared.item_ids), 2_048):
rows = torch.arange(
start,
min(start + 2_048, len(prepared.item_ids)),
dtype=torch.long,
device=device,
)
item_parts.append(model.encode_items(rows).cpu())
items = torch.cat(item_parts)
rows: list[tuple[int, int, int]] = []
for start in range(0, len(eligibility.uids), ranking_batch_size):
end = min(start + ranking_batch_size, len(eligibility.uids))
scores = queries[start:end] @ items.T
eligible = np.unpackbits(
eligibility.packed[start:end],
axis=1,
count=len(prepared.item_ids),
bitorder="little",
).astype(bool, copy=False)
scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
for local_row, columns in enumerate(selected):
uid = int(eligibility.uids[start + local_row])
rows.extend(
(uid, int(prepared.item_ids[column]), rank)
for rank, column in enumerate(columns, start=1)
)
if end == len(eligibility.uids) or end % 1_024 == 0:
log(f"ranked users={end:,}/{len(eligibility.uids):,}")
query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
diagnostics = {
"query_norm_mean": float(query_norm.mean()),
"query_norm_std": float(query_norm.std()),
"item_norm_mean": float(item_norm.mean()),
"item_norm_std": float(item_norm.std()),
}
return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
def write_ranking(frame: pd.DataFrame, output: Path) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
if output.suffix == ".parquet":
frame.to_parquet(output, index=False, compression="zstd")
elif output.suffix == ".csv":
frame.to_csv(output, index=False)
else:
raise ValueError("--output must end in .parquet or .csv")
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
## Step 14 · agent · 2026-09-29T05:36:47.647Z
### read_file · 2026-09-29T05:36:47.647Z
{
"start_line": 2001,
"file_path": "/task/current_model/train_current_model.py",
"end_line": 2165
}
### Tool output · 2026-09-29T05:36:47.654Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2001-2165 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2166.
--- FILE CONTENT (truncated) ---
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
validation_fraction=args.validation_fraction,
max_train_targets_per_user=args.max_train_targets_per_user,
max_validation_targets_per_user=args.max_validation_targets_per_user,
early_stopping_patience=args.early_stopping_patience,
early_stopping_min_delta=args.early_stopping_min_delta,
seed=args.seed,
)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(1)
seed_everything(config.seed)
device = torch.device(args.device)
if device.type == "mps" and not torch.backends.mps.is_available():
parser.error("MPS was requested but is unavailable")
started = time.monotonic()
prepared, eligibility = load_or_prepare(
public,
args.stage,
config,
args.prepared_cache.resolve() if args.prepared_cache else None,
)
target_partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
if args.prepare_only:
print(
json.dumps(
{
"stage": args.stage,
"sequence_users": len(prepared.user_ids),
"sequence_tokens": len(prepared.sequence_items),
"next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(
target_partition.window_starts.sum()
),
"target_users": len(eligibility.uids),
"runtime_seconds": round(time.monotonic() - started, 3),
"config": asdict(config),
},
indent=2,
)
)
return 0
model, training_history = train_model(
public,
args.stage,
prepared,
config,
device,
args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
)
ranking, embedding_diagnostics = rank_candidates(
model,
prepared,
eligibility,
config,
device,
args.ranking_batch_size,
)
write_ranking(ranking, args.output.resolve())
print(
json.dumps(
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": args.stage,
"output": str(args.output.resolve()),
"rows": len(ranking),
"users": int(ranking.uid.nunique()),
"runtime_seconds": round(time.monotonic() - started, 3),
"public_inputs_only": True,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": (
"every-adjacent-target-within-recent-chronological-window"
),
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": False,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": (
"24-uniform-plus-8-propensity-corrected-inbatch"
),
"false_negative_filter": config.negative_filter_scope,
"target_weighting": config.target_weighting,
},
"config": asdict(config),
"sequence_tokens": len(prepared.sequence_items),
"all_next_item_targets": prepared.target_count,
"selected_training_targets": int(
target_partition.training_counts.sum()
),
"selected_validation_targets": int(
target_partition.validation_counts.sum()
),
"discarded_old_targets": int(target_partition.window_starts.sum()),
"training_history": training_history,
"embedding_diagnostics": embedding_diagnostics,
"torch_version": torch.__version__,
"device": str(device),
},
indent=2,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
## Step 15 · agent · 2026-09-29T05:36:50.918Z
### Recorded reasoning summary
Executing Reproduction Command
I'm initiating the reproduction command as outlined, specifically focusing on the validation stage and directing the output to the designated production validation parquet file. This step is crucial for testing the model's performance under current conditions.
### run_shell_command · 2026-09-29T05:36:50.918Z
{
"command": "python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/dummy.parquet --prepare-only",
"description": "Run prepare-only to inspect dataset size and preparation time"
}
### Tool output · 2026-09-29T05:37:45.345Z
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=15.0s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s
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=35.4s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.6s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.4s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
{
"stage": "validation",
"sequence_users": 9135,
"sequence_tokens": 20056329,
"next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"target_users": 8456,
"runtime_seconds": 50.333,
"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: 1626
## Step 16 · agent · 2026-09-29T05:37:47.559Z
### read_file · 2026-09-29T05:37:47.559Z
{
"end_line": 535,
"start_line": 470,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:37:47.566Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 470-535 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 536.
--- FILE CONTENT (truncated) ---
def cache_metadata(stage: str, config: Config) -> dict[str, object]:
return {
"cache_version": CACHE_VERSION,
"stage": stage,
"positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
"profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
"event_time_feature": "exp-negative-gap-over-60-days",
}
def save_cache(
path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
np.savez(
path,
metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
**prepared.__dict__,
)
log(f"saved public-derived full-sequence cache: {path}")
def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
with np.load(path, allow_pickle=False) as artifact:
actual = json.loads(str(artifact["metadata"].item()))
expected = cache_metadata(stage, config)
if actual != expected:
raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
values = {
field: artifact[field].copy()
for field in SequenceData.__dataclass_fields__
}
prepared = SequenceData(**values)
log(
f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
f"targets={prepared.target_count:,}"
)
return prepared
def load_or_prepare(
public: Path,
stage: str,
config: Config,
cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
candidate_name = (
"validation_eligible_candidates.npz"
if stage == "validation"
else "test_eligible_candidates.npz"
)
eligibility = CandidateSet.read(public / "data" / candidate_name)
if cache is not None and cache.is_file():
prepared = load_cache(cache, stage, config)
if not np.array_equal(prepared.item_ids, eligibility.item_ids):
raise RuntimeError("cached catalog differs from candidate artifact")
return prepared, eligibility
prepared, built_eligibility = prepare_data(public, stage, config)
if cache is not None:
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
## Step 17 · agent · 2026-09-29T05:37:52.532Z
### Recorded reasoning summary
Caching Prepared Data
I'm focused on optimizing performance by implementing caching for the prepared data, aiming to significantly reduce execution time for subsequent runs. My current work ensures that all deliverables and intermediate artifacts are correctly placed within the `/app/solution/` directory as per the requirements.
We will now run the baseline production model on the validation stage, saving the prepared data cache to `/app/solution/val_cache.npz` and the validation ranking to `/app/solution/production_validation.parquet`. This will establish our baseline evaluation score on the public validation set.
### run_shell_command · 2026-09-29T05:37:52.532Z
{
"description": "Run baseline training on validation stage and save cache",
"command": "python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --output /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T05:44:03.995Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=10.0s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.3s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.8s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.7s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.5s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=36.1s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.5s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.3s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/val_cache.npz
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=199.5s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.8 validation_seconds=16.3
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": 366.992,
"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": 0.5398380467434385,
"positive_logit": 0.7427799590910525,
"negative_logit": -0.6222247034852317,
"pair_accuracy": 0.7547733315257186,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 212.804426531,
"validation_loss": 0.4966362155214729,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 16.33529513899998
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497606277466,
"item_norm_mean": 1.7570281028747559,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1658
## Step 18 · agent · 2026-09-29T05:44:10.095Z
### run_shell_command · 2026-09-29T05:44:10.095Z
{
"description": "Evaluate public baseline ranking on validation outcomes",
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T05:44:13.956Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1724
## Step 19 · agent · 2026-09-29T05:44:18.981Z
### Recorded reasoning summary
Analyzing Current Model
I've begun by copying the existing code to my workspace for closer examination. My immediate focus is on understanding the contents of `/task/current_model/` to grasp the existing architecture and identify potential areas for improvement.
### Recorded reasoning summary
Copying Essential Files
I've decided to start by copying `train_current_model.py` and its dependent `two_tower_model.py` into my `/app/solution/` directory. This ensures I have the foundational code in place, and I'm also preparing an initial baseline submission file to `/app`.
### grep_search · 2026-09-29T05:44:18.981Z
{
"pattern": "stage == \"submission\"",
"dir_path": "/task/current_model"
}
### Tool output · 2026-09-29T05:44:19.057Z
No matches found for pattern "stage == "submission"" in path "/task/current_model".
## Step 20 · agent · 2026-09-29T05:44:21.046Z
### grep_search · 2026-09-29T05:44:21.046Z
{
"dir_path": "/task/current_model",
"pattern": "stage"
}
### Tool output · 2026-09-29T05:44:21.059Z
Found 26 matches for pattern "stage" in path "/task/current_model":
---
File: train_current_model.py
L342: public: Path, stage: str, config: Config
L354: if stage == "validation":
L463: f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L474: "stage": stage,
L482: path: Path, prepared: SequenceData, stage: str, config: Config
L487: metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L496: expected = cache_metadata(stage, config)
L513: stage: str,
L519: if stage == "validation"
L524: prepared = load_cache(cache, stage, config)
L528: prepared, built_eligibility = prepare_data(public, stage, config)
L530: save_cache(cache, prepared, stage, config)
L915: public: Path, stage: str, item_ids: np.ndarray
L918: prefixes = ("train",) if stage == "validation" else ("train", "val")
L957: stage: str,
L978: public, stage, prepared.item_ids
L981: base.TRAIN_END if stage == "validation" else base.VALIDATION_END
L1523: stage: str,
L1560: stage,
L1925: "--stage", choices=("validation", "submission"), default="validation"
L2057: args.stage,
L2071: "stage": args.stage,
L2095: args.stage,
L2115: "stage": args.stage,
---
## Step 21 · agent · 2026-09-29T05:44:24.598Z
### Recorded reasoning summary
Testing Submission Mode
My latest thought is that the `--stage submission` flag integrates seamlessly with `train_current_model.py` without requiring modifications. I'm currently examining the conditional logic within `prepare_data` to confirm how it handles data loading for submission versus validation scenarios.
### read_file · 2026-09-29T05:44:24.598Z
{
"start_line": 350,
"end_line": 400,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:44:24.605Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 350-400 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: 401.
--- FILE CONTENT (truncated) ---
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(
## Step 22 · agent · 2026-09-29T05:45:06.923Z
### Recorded reasoning summary
Analyzing Model Parameters
I'm currently examining the `target_weighting` and `discovery-graded` parameters within `train_current_model.py`. My focus is on understanding their role and impact, as well as the implications of the configured negative sampling values.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now digging into how negative examples are chosen and utilized by the training objective. My current focus is on pinpointing weaknesses in the existing negative-sampling strategy, especially concerning the `negative_filter_scope` set to "full-training-history." I'm also considering the implications of `inbatch_correction_min` and `inbatch_correction_max` on this process.
### Recorded reasoning summary
Refining Negative Sampling
I'm now investigating the specifics of `draw_training_negatives` and `draw_full_history_filtered_negatives` to understand precisely how negative examples are selected. My goal is to refine this process for better performance within our CPU constraints, and I'll be validating any adjustments with public backtests.
### Recorded reasoning summary
Exploring Negative Sampling Implementation
I'm now examining the `draw_mixed_negatives` function. My focus is on understanding how uniform and in-batch negatives are generated and combined, especially the logic for `corrected_count` and the initial priority assignments.
### Recorded reasoning summary
Optimizing Negative Sampling
I'm currently exploring how uniform and in-batch negatives are generated and merged within `draw_mixed_negatives`. My focus is on the `corrected_count` logic and initial priority assignments to improve efficiency within CPU limits, planning to validate changes with public backtests.
### Recorded reasoning summary
Revising Invalid Negative Handling
I'm reviewing how invalid in-batch negatives are replaced with uniform draws and how their corrections are reset to 1.0. I'm also examining `build_known_positive_filter` to ensure it correctly handles packed item sets for users during training.
### Recorded reasoning summary
Clarifying Known Positives
I'm reassessing how known positives are constructed in `build_known_positive_filter`. It appears to only consider items up to `target_count + 1` from `sequence_items`. I need to investigate if this accurately captures all relevant user interactions, particularly those outside this immediate sequence and any explicitly disliked items, to ensure robustness.
### Recorded reasoning summary
Investigating Window Boundaries
I'm now scrutinizing how `retained_target_window_starts` calculates window boundaries, especially when a user's sequence (`1,000` items) far exceeds `target_count` (`128`). I need to clarify where the user's actual sequence begins within this calculated window.
### Recorded reasoning summary
Clarifying Sequence Slicing
I'm re-examining how user sequences are sliced for training. It seems the slice `offset : offset + int(target_count) + 1` is being used, which I need to reconcile with how target positions are determined within the training block.
### Recorded reasoning summary
Correcting Sequence Interpretation
I've discovered a critical misinterpretation: `build_known_positive_filter` was slicing only the initial `target_count + 1` items from a user's historical sequence, not the full training history. This seems to stem from an incorrect assumption about sequence indexing.
### Recorded reasoning summary
Correcting Validation History Logic
I've realized my understanding of the validation split was flawed. The training history for validation should encompass all items the user encountered during the training period, not just a limited slice. I am now verifying how `partition.history_training_counts` is actually populated and used to ensure the full history is considered.
### Recorded reasoning summary
Revising History Counts
I'm now examining the `history_training_counts` to confirm it includes the full user history up to the validation split. I need to ensure that disliked items are also accounted for in this history for accurate validation and am continuing to investigate all aspects of negative sampling and the training objective.
### Recorded reasoning summary
Revising Correction Logic
I'm now re-evaluating how `corrected_negative_loss` calculates the loss contribution from uniform and in-batch negatives. My focus is on ensuring the averaging and weighting mechanisms accurately reflect the intended influence of each negative type on the overall loss, particularly in conjunction with `score_target_microbatches`.
### Recorded reasoning summary
Adjusting Loss Calculation
I'm reassessing the loss calculation within `score_target_microbatches`. It appears the averaging of `positive_loss` and `negative_loss` by `2.0` might be incorrect. I need to investigate the derivation behind this specific weighting.
### Recorded reasoning summary
Clarifying Loss Formulation
I'm currently investigating whether the objective uses a simple binary cross-entropy with negative sampling, or if it's more akin to a sampled softmax or InfoNCE loss, as is common in modern retrieval models. My focus is on understanding how a single positive interacts with the ensemble of negatives.
### Recorded reasoning summary
Investigating Loss Formulation
I am investigating the precise loss formulation, determining if it's a standard binary cross-entropy with negative sampling or something more complex like a sampled softmax or InfoNCE, particularly how a single positive interacts with the negative set.
### Recorded reasoning summary
Analyzing Loss Averaging
I'm now questioning the simple averaging of positive and negative losses. I need to understand the underlying theoretical justification for dividing by 2.0 in `score_target_microbatches` and if it accurately reflects the contribution of each to the overall objective.
### Recorded reasoning summary
Revising Averaging Rationale
I'm questioning the simple division by 2.0 for averaging positive and negative losses. I'm investigating the theoretical grounding for this specific averaging method and its implications for the overall objective function's balance.
### Recorded reasoning summary
Revising Positive Item Handling
I'm analyzing how positive items are selected and subsequently used to draw negatives. The current implementation appears to generate a very large set of positive items (up to 6,400 per batch), and I need to scrutinize the efficiency and impact of drawing negatives for each individual positive item.
### Recorded reasoning summary
Examining Candidate Vector Generation
I'm now focused on the `candidate_vectors` function, observing how it concatenates positive and negative item IDs. My current line of inquiry is into the subsequent use of `torch.unique` to create a combined set of items for encoding, which I need to understand thoroughly.
### Recorded reasoning summary
Analyzing Negative Vector Usage
I'm questioning the separate generation and encoding of negative vectors. My current analysis suggests this approach might be inefficient, particularly with large numbers of sampled negatives, and I need to clarify how these distinct vectors are intended to be utilized compared to standard in-batch negative approaches where scores are directly computed.
### Recorded reasoning summary
Clarifying Item Vector Generation
I'm now questioning how item vectors are generated and utilized. My current analysis indicates that the concatenated positive and negative item IDs, followed by `torch.unique`, might lead to redundant encoding. I need to determine if a more efficient method exists for handling these vectors.
### run_shell_command · 2026-09-29T05:45:06.923Z
{
"description": "List directory contents of /task",
"command": "ls -la /task"
}
### Tool output · 2026-09-29T05:45:06.969Z
Output: total 40
dr-xr-xr-x 1 root root 4096 Sep 29 05:32 .
drwxr-xr-x 1 root root 4096 Sep 29 05:35 ..
-r--r--r-- 1 root root 0 Sep 29 05:30 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 05:30 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 05:31 data
-r--r--r-- 1 root root 5774 Sep 29 05:30 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 05:30 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 05:30 requirements.txt
Process Group PGID: 1748
## Step 23 · agent · 2026-09-29T05:45:48.984Z
### run_shell_command · 2026-09-29T05:45:48.984Z
{
"description": "Check git status",
"command": "git status || true"
}
### Tool output · 2026-09-29T05:45:48.999Z
Output: /usr/bin/bash: line 4: git: command not found
Process Group PGID: 1757
## Step 24 · agent · 2026-09-29T05:46:04.059Z
### Recorded reasoning summary
Preparing Code Workspace
I'm starting by ensuring Git is set up. Next, I'll meticulously copy the training script from the current model directory into my editable solution space. This prepares me for the upcoming modifications and experimentation.
### run_shell_command · 2026-09-29T05:46:04.059Z
{
"command": "cp /task/current_model/train_current_model.py /app/solution/ && cp /task/current_model/two_tower_model.py /app/solution/",
"description": "Copy baseline code to /app/solution/"
}
### Tool output · 2026-09-29T05:46:04.107Z
Output: (empty)
Process Group PGID: 1760
## Step 25 · agent · 2026-09-29T05:46:23.874Z
### Recorded reasoning summary
Reviewing Weighting Options
I'm currently examining the `train_current_model.py` script, specifically within the `main()` function, to understand the available `--target-weighting` choices. My focus is on the documented options, which currently include "discovery-graded," "organic-strong," and "uniform."
### Recorded reasoning summary
Investigating Weighting Methods
I'm digging into how "discovery-graded" weighting is implemented in `build_target_weights`. It seems to involve loading "organic-like" outcomes and calculating a "horizon" based on `discovery_horizon_days`. There's also logic for processing user sequences and determining split timestamps, hinting at a more nuanced approach than the default.
### Recorded reasoning summary
Analyzing Discovery Graded Logic
I'm now tracing the logic for "discovery-graded" weighting in the `build_target_weights` function. My focus is on how timestamps and user likes are used to determine novelty and assign weights, especially when encountering repeat items in sequences.
### Recorded reasoning summary
Refining Negative Sampling
I'm scrutinizing how the trainer selects and utilizes negative examples. My recent focus is on the `target_weighting` mechanism, as it appears to influence how positive targets are weighted, which might indirectly impact the sampling of negatives. I need to understand this relationship more thoroughly.
### Recorded reasoning summary
Exploring Negative Sampling
I'm now focusing on how the training objective selects and uses negative examples, as instructed. My current deep dive involves meticulously examining the `draw_full_history_filtered_negatives` function and its parameters.
### Recorded reasoning summary
Examining Negative Drawing
I'm currently analyzing the `draw_full_history_filtered_negatives` function, specifically how it handles uniform and in-batch negative sampling. I'm focusing on the conversion of tensors to NumPy and the use of `torch.randint` to generate uniform negatives, as well as the logic for in-batch negatives when `config.inbatch_negatives` is enabled.
### Recorded reasoning summary
Tracing User Rows
I'm investigating the origin of `user_rows` within `run_training_epoch`, specifically how it's derived from groups and used in `draw_training_negatives`. Understanding this connection is crucial for the subsequent negative sampling logic.
### Recorded reasoning summary
Tracing User Indices
I am now investigating how `flat_user_rows` is populated and what it represents in relation to `users` and `sorted_users`. My current focus is on understanding the indexing into `prepared.user_ids` to confirm that `user_rows` accurately corresponds to user row indices within the prepared data structure.
### Recorded reasoning summary
Checking Liked Items
I'm examining the `invalid` tensor calculation and discovered a crucial dependency on `liked_items`. I'm tracing `liked_items` back to `prepared.liked_states[flat_states]`, which represents user's active likes at a given timestep, a key detail for understanding the logic.
### Recorded reasoning summary
Confirming Liked Item Representation
I've traced `liked_items` back to `prepared.liked_states[flat_states]`, which seems to accurately represent a user's active likes at a specific timestep. My current focus is on confirming how this state is then utilized within the `invalid` tensor calculation to filter out previously liked items.
### grep_search · 2026-09-29T05:46:23.874Z
{
"dir_path": "/app/solution",
"pattern": "def evaluate_validation_bce"
}
### Tool output · 2026-09-29T05:46:23.889Z
Found 1 match for pattern "def evaluate_validation_bce" in path "/app/solution":
---
File: train_current_model.py
L1314- negatives,
L1315- negative_weights,
L1316- block[8].reshape(-1)[flat_scored],
L1317- device,
L1318- backward=True,
L1319- )
L1320- if sequence_gradient is None:
L1321- raise RuntimeError("training block omitted its sequence gradient")
L1322- sequence_vectors.backward(sequence_gradient)
L1323- nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
L1324- optimizer.step()
L1325-
L1326- count = len(positive_items)
L1327- total_loss += statistics["loss_sum"]
L1328- total_targets += count
L1329- total_weight += statistics["weight_sum"]
L1330- total_positive += statistics["positive_sum"]
L1331- total_negative += statistics["negative_sum"]
L1332- total_pair_correct += statistics["pair_correct"]
L1333- batches += 1
L1334- if batches % 25 == 0:
L1335- release_accelerator_cache(device)
L1336- if batches % 250 == 0:
L1337- batch_bce = statistics["loss_sum"] / max(
L1338- statistics["weight_sum"], 1.0e-12
L1339- )
L1340- running_bce = total_loss / max(total_weight, 1.0e-12)
L1341- log(
L1342- f"epoch={epoch}/{config.epochs} batches={batches:,} "
L1343- f"targets={total_targets:,} "
L1344- f"batch_bce={batch_bce:.6f} "
L1345- f"running_bce={running_bce:.6f} "
L1346- f"elapsed={time.monotonic() - started:.1f}s"
L1347- )
L1348-
L1349- return {
L1350- "epoch": float(epoch),
L1351- "loss": total_loss / max(total_weight, 1.0e-12),
L1352- "positive_logit": total_positive / max(total_targets, 1),
L1353- "negative_logit": total_negative / max(total_targets * negative_count, 1),
L1354- "pair_accuracy": total_pair_correct
L1355- / max(total_targets * negative_count, 1),
L1356- "targets": float(total_targets),
L1357- "target_weight_sum": total_weight,
L1358- "batches": float(batches),
L1359- "seconds": time.monotonic() - started,
L1360- }
L1361-
L1362-
L1363- @torch.no_grad()
L1364: def evaluate_validation_bce(
L1365- model: base.SequentialTwoTower,
L1366- prepared: SequenceData,
L1367- config: Config,
L1368- target_weights: np.ndarray,
L1369- window_starts: np.ndarray,
L1370- training_counts: np.ndarray,
L1371- validation_counts: np.ndarray,
L1372- item_correction: torch.Tensor,
L1373- known_positive_packed: np.ndarray | None,
L1374- device: torch.device,
L1375- ) -> dict[str, float]:
L1376- """Measure deterministic BCE on each user's chronological target suffix."""
L1377- model.eval()
L1378- totals = training_counts + validation_counts
L1379- users_with_validation = np.flatnonzero(validation_counts > 0)
L1380- ordered = users_with_validation[
L1381- np.argsort(totals[users_with_validation], kind="stable")
L1382- ]
L1383- groups = [
L1384- ordered[start : start + config.user_batch_size]
L1385- for start in range(0, len(ordered), config.user_batch_size)
L1386- ]
L1387- generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
L1388- total_loss = 0.0
L1389- total_targets = 0
L1390- total_weight = 0.0
L1391- started = time.monotonic()
L1392-
L1393- for group_number, users in enumerate(groups, start=1):
L1394- group_totals = totals[users]
L1395- group_starts = training_counts[users]
L1396- hidden = torch.zeros(
L1397- 1, len(users), config.embedding_dim, device=device
L1398- )
L1399- max_targets = int(group_totals.max())
L1400- for start in range(0, max_targets, config.bptt_steps):
L1401- steps = min(config.bptt_steps, max_targets - start)
L1402- block = build_dense_block(
L1403- prepared,
L1404- target_weights,
L1405- users,
L1406- window_starts[users],
L1407- group_totals,
L1408- group_starts,
L1409- start,
L1410- steps,
L1411- config,
L1412- )
L1413- sequence_outputs, _ = event_sequence(
L1414- model,
---
## Step 26 · agent · 2026-09-29T05:46:36.731Z
### Recorded reasoning summary
Analyzing Model Logic
I'm currently delving into `two_tower_model.py`, specifically comparing the functionalities of `base.draw_filtered_mixed_negatives` and `draw_full_history_filtered_negatives`. My focus is on understanding the `invalid_negative_mask` checks, including how `negatives.eq` plays a role in this filtering process.
### Recorded reasoning summary
Refining Negative Sampling
I'm re-examining the negative sampling strategies, specifically how `base.draw_filtered_mixed_negatives` and `draw_full_history_filtered_negatives` handle exclusions. My current focus is on the differing scopes of "known" items each function considers, and how this impacts the candidate pool during evaluation and testing, aligning with the problem description's exclusion criteria.
### Recorded reasoning summary
Investigating Negative Selection
My current focus is on dissecting the training objective's utilization of negative examples. I'm scrutinizing the code, particularly within `draw_full_history_filtered_negatives`, to understand precisely which negatives are selected and how their exclusion criteria are applied to ensure they align with the problem's specifications.
### Recorded reasoning summary
Clarifying Target Sources
I'm investigating the origin of `unique_targets`. It appears they represent tracks users have actually listened to, implying a bias towards popular items. This contrasts with my current understanding of the overall negative sampling strategy, and I'm examining how this influences the selection of `uniform_negatives` and `inbatch_negatives`.
### grep_search · 2026-09-29T05:46:36.731Z
{
"pattern": "dislike",
"dir_path": "/task"
}
### Tool output · 2026-09-29T05:46:36.747Z
Found 40 matches for pattern "dislike" in path "/task":
---
File: current_model/train_current_model.py
L110: disliked_states: np.ndarray
L114: inference_disliked_items: np.ndarray
L176: active_dislikes: dict[int, int],
L179: disliked_states: list[np.ndarray],
L183: disliked_states.append(
L184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186: state_counts.append((len(active_likes), len(active_dislikes)))
L252: disliked_states: list[np.ndarray],
L279: active_dislikes: dict[int, int] = {}
L293: else active_dislikes
L304: active_dislikes,
L307: disliked_states,
L320: dislike_count: int,
L337: result[5] = np.log1p(dislike_count) / 4.0
L369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383: disliked_states = [
L384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405: disliked_states,
L427: int(dislike_counts[inference_row]),
L440: np.log1p(dislike_counts[missing]) / 4.0
L455: disliked_states=np.stack(disliked_states),
L459: inference_disliked_items=inference_dislikes,
L784: disliked_items = torch.from_numpy(
L785: prepared.disliked_states[unique_states].astype(np.int64)
L788: disliked_vectors = model._mean_context_channel(disliked_items)
L793: disliked_vectors[inverse_rows],
L802: disliked_vectors: torch.Tensor,
L807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159: disliked_vectors,
L1844: disliked_items = torch.from_numpy(
L1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851: model._mean_context_channel(disliked_items),
---
File: current_model/two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184: ("dislikes", 1, 1, 0),
L185: ("undislikes", 1, 0, 1),
L265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281: return liked, disliked, like_counts, dislike_counts
---
## Step 27 · agent · 2026-09-29T05:46:51.902Z
### Recorded reasoning summary
Refining Dislike Usage
I'm realizing dislikes aren't negative examples, but a specific input feature. I'm currently investigating their role within `invalid_full_history_negatives` to ensure correct application.
### Recorded reasoning summary
Investigating Dislike Interaction
My recent focus has been on how disliked items are handled. I've observed that current logic correctly filters out explicitly liked items from being drawn as negatives. However, it appears disliked items are *not* similarly excluded, raising questions about their potential inclusion in negative samples.
### Recorded reasoning summary
Analyzing Negative Sampling Ratios
I'm reviewing the frequency of disliked items within the larger dataset. The current proportion is exceptionally low, prompting me to re-examine the negative sampling strategy and how it aligns with the prompt's directive to investigate and improve its limitations.
### Recorded reasoning summary
Exploring Loss Calculation
I'm examining the `corrected_negative_loss` function more closely, specifically the `result` variable's contribution. It seems the implementation might be directly calculating a weighted average of softplus applied to the negative logits, which needs careful validation against the intended probabilistic interpretation.
### Recorded reasoning summary
Calculating Loss Contributions
I'm dissecting the loss calculation. It appears to be a weighted average of uniform and in-batch negatives, with specific coefficients. I'm now scrutinizing how this translates to binary cross-entropy for positive and negative samples.
### Recorded reasoning summary
Analyzing Gradient Behavior
I'm currently scrutinizing the gradients generated by the loss calculation. It appears the gradient on positive logits is significantly larger, specifically 32 times greater than on individual negative logits, which warrants further investigation into its impact on model learning.
### Recorded reasoning summary
Evaluating Loss Function
I am reassessing the loss function. It seems the calculation might not be a straightforward comparison between positive and negative samples as I initially assumed. I'm now questioning whether the current approach is truly implementing a binary cross-entropy with appropriate balancing, or if another objective function like InfoNCE or BPR is implicitly at play, given the prompt's description.
### grep_search · 2026-09-29T05:46:51.902Z
{
"pattern": "negative",
"dir_path": "/task"
}
### Tool output · 2026-09-29T05:46:51.919Z
Found 100 matches for pattern "negative" in path "/task" (results limited to 100 matches for performance):
---
File: current_model/train_current_model.py
L14: and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
L56: uniform_negatives: int = 24
L57: inbatch_negatives: int = 8
L61: negative_filter_scope: str = "full-training-history"
L165: uniform_negatives=config.uniform_negatives,
L166: inbatch_negatives=config.inbatch_negatives,
L477: "event_time_feature": "exp-negative-gap-over-60-days",
L603: f"full-history false-negative filter: users={len(prepared.user_ids):,} "
L609: def invalid_full_history_negatives(
L610: negatives: np.ndarray,
L616: """Return row-level invalid negatives using the complete training prefix."""
L617: byte_columns = negatives >> 3
L618: bit_columns = negatives & 7
L621: invalid |= negatives == targets[:, None]
L622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
L623: for column in range(1, negatives.shape[1]):
L625: negatives[:, column, None] == negatives[:, :column]
L630: def draw_full_history_filtered_negatives(
L641: """Draw mixed negatives and reject all known public-training positives."""
L647: (batch_size, config.uniform_negatives),
L653: if config.inbatch_negatives:
L656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)
L672: if corrected_count < config.inbatch_negatives:
L675: (batch_size, config.inbatch_negatives - corrected_count),
L681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()
L682: correction = np.ones_like(negatives, dtype=np.float32)
L685: :, config.uniform_negatives : config.uniform_negatives + corrected_count
L688: invalid = invalid_full_history_negatives(
L689: negatives,
L697: torch.from_numpy(negatives).to(device),
L706: negatives[invalid] = replacements
L709: invalid = invalid_full_history_negatives(
L710: negatives,
L717: raise RuntimeError("full-history negative resampling failed")
L719: torch.from_numpy(negatives).to(device),
L724: def draw_training_negatives(
L736: if config.negative_filter_scope == "full-training-history":
L738: raise RuntimeError("full-history negative filter was not built")
L739: return draw_full_history_filtered_negatives(
L750: return base.draw_filtered_mixed_negatives(
L755: config.uniform_negatives,
L756: config.inbatch_negatives,
L816: negatives: torch.Tensor,
L818: all_items = torch.cat([positives, negatives.reshape(-1)])
L822: negative_vectors = encoded[inverse[len(positives) :]].reshape(
L823: len(positives), negatives.shape[1], -1
L825: return positive_vectors, negative_vectors
L845: def corrected_negative_loss(
L851: total = config.uniform_negatives + config.inbatch_negatives
L853: if config.uniform_negatives:
L854: result += config.uniform_negatives * raw[
L855: :, : config.uniform_negatives
L857: if config.inbatch_negatives:
L858: selected = slice(config.uniform_negatives, total)
L860: result += config.inbatch_negatives * (
L880: raise RuntimeError("chronological target partition produced negative counts")
L1119: negatives: torch.Tensor,
L1120: negative_weights: torch.Tensor,
L1134: "negative_sum": 0.0,
L1162: positive_vectors, negative_vectors = candidate_vectors(
L1165: negatives[begin:end],
L1168: negative_logits = torch.einsum(
L1169: "bd,bkd->bk", query_vectors, negative_vectors
L1172: negative_loss = corrected_negative_loss(
L1173: negative_logits, negative_weights[begin:end], config
L1179: selected_weights * (positive_loss + negative_loss) / 2.0
L1190: statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
L1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
L1227: negative_count = config.uniform_negatives + config.inbatch_negatives
L1232: total_negative = 0.0
L1293: negatives, negative_weights = draw_training_negatives(
L1314: negatives,
L1315: negative_weights,
L1331: total_negative += statistics["negative_sum"]
L1353: "negative_logit": total_negative / max(total_targets * negative_count, 1),
L1355: / max(total_targets * negative_count, 1),
L1449: negatives, negative_weights = draw_training_negatives(
L1469: negatives,
L1470: negative_weights,
L1567: if config.negative_filter_scope == "full-training-history"
L1601: "negative_filter_scope", "last-100"
L1629: stored_filter_scope != config.negative_filter_scope
L1660: f"{stored_filter_scope}->{config.negative_filter_scope}, "
L1944: "--negative-filter-scope",
L1946: default=Config.negative_filter_scope,
L2034: negative_filter_scope=args.negative_filter_scope,
L2136: "negative_sampling": (
L2139: "false_negative_filter": config.negative_filter_scope,
---
File: current_model/two_tower_model.py
L40: uniform_negatives: int = 24
L41: inbatch_negatives: int = 8
L346: def invalid_negative_mask(
L347: negatives: torch.Tensor,
L353: invalid = negatives.eq(targets[:, None])
L354: invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
L355: invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
L356: for column in range(1, negatives.shape[1]):
L357: invalid[:, column] |= negatives[:, column, None].eq(
L358: negatives[:, :column]
L363: def draw_filtered_mixed_negatives(
L374: """Draw uniform and unique-target in-batch negatives."""
---
## Step 28 · agent · 2026-09-29T05:47:32.565Z
### Recorded reasoning summary
Analyzing Model Architecture
I am now meticulously scrutinizing specific sections of the `train_current_model.py` and `two_tower_model.py` scripts, paying particular attention to the `build_known_positive_filter` function within the latter. My focus is on understanding its implementation and how it integrates with the broader model.
### Recorded reasoning summary
Refining Feature Encoding
I'm currently delving into the `pack_public_training_positives` function. My focus is on the bitwise operations used to efficiently encode user item sets, aiming to understand the data packing strategy and optimize it.
### Recorded reasoning summary
Investigating Target Counts
I'm currently examining the `target_count` variable within the `train_model` script, specifically its relationship with `partition.history_training_counts`. My investigation is leading me to the `chronological_target_partition` function to understand how these counts are derived and utilized.
### Recorded reasoning summary
Exploring Positive Filter Logic
I am now dissecting the `build_known_positive_filter` function, specifically how it utilizes `prepared.sequence_items` which appear to represent positively completed listens. I'm questioning if this encompasses explicit user likes and if `qualifying` listens are indeed the sole source.
### Recorded reasoning summary
Clarifying Negative Sampling
I am now questioning the definition of "negative" in this context, as it impacts how training data is constructed. My current focus is on understanding if user likes, even those without significant listen time, are adequately captured in the model's learning process, and how this relates to the evaluation metrics.
### Recorded reasoning summary
Revising Evaluation Splits
I'm re-evaluating the candidate exclusion logic for validation and testing. It appears my previous understanding was too narrow; items encountered during training are entirely excluded from validation and test candidate pools, which significantly impacts how model performance is interpreted.
### Recorded reasoning summary
Analyzing Target Creation
I am examining how targets are constructed, specifically that the next item in a sequence is designated as the target. I'm investigating the implications of this, particularly concerning repeated song listens and how they're handled in the training weights.
### Recorded reasoning summary
Confirming Target Weights
I'm confirming that under the `organic-strong` weighting scheme, repeat listens retain their ordinary weight and are not discounted. My previous understanding was incorrect; they remain training examples with standard weighting, not solely a function of the `discovery-graded` logic.
### Recorded reasoning summary
Diagnosing Negative Sampling
I am now deeply investigating how negative examples are selected and utilized by the training objective. My primary focus is on identifying limitations within the existing negative sampling strategy to pinpoint areas for improvement, all while remaining within the fixed CPU budget.
### Recorded reasoning summary
Investigating Negative Sampling Sources
I'm now examining the `draw_full_history_filtered_negatives` function, specifically how it generates both uniform and in-batch negative samples. My current focus is on the logic for selecting in-batch negatives, aiming to understand how the priorities are assigned and how unique targets are handled to avoid self-sampling.
### Recorded reasoning summary
Questioning Negative Sampling Uniformity
I am now questioning if my negative sampling is truly uniform. It appears the `unique_targets` are sampled without regard for their original frequency, meaning both popular and unpopular items have an equal chance of being selected as in-batch negatives. This raises concerns about the `item_correction` logic.
### Recorded reasoning summary
Challenging Sampling Assumptions
I'm realizing my `item_correction` logic assumes in-batch negatives are sampled proportionally to item frequency. However, by de-duplicating targets before sampling, each unique target has an equal chance, invalidating that assumption and its application.
### Recorded reasoning summary
Revising Correction Logic
I'm now examining the `corrected_negative_loss` function. The way `weights` are applied and averaged within this function seems unusual for standard propensity correction, prompting a deeper dive into its specific implementation and theoretical justification.
### Recorded reasoning summary
Revising Loss Calculation
I'm now realizing the `corrected_negative_loss` function applies normalization by the sum of weights, effectively creating a weighted average of negative losses. This deviates from standard propensity correction techniques, prompting me to re-evaluate its specific purpose and impact.
### Recorded reasoning summary
Adjusting Loss Weights
I'm now understanding how the loss on rare items is heavily upweighted while popular items are downweighted due to the `item_correction` logic. This uneven weighting, combined with the ratio of uniform to in-batch negatives, significantly influences the training dynamic, prompting further investigation into potential biases and limitations.
### Recorded reasoning summary
Re-evaluating Invalid Negative Logic
I'm currently reviewing how invalid negatives are identified, specifically the logic in `invalid_full_history_negatives`. I need to understand what constitutes an "invalid" draw and how this impacts the sampling process, particularly its interaction with `known_positive_packed` and `liked_items`.
### Recorded reasoning summary
Revising Invalid Negative Handling
I'm re-evaluating how invalid in-batch negatives are treated. Upon identifying an invalid in-batch negative, it's replaced by a uniform random sample, and its correction factor is reset to 1.0. I need to understand the implications of this replacement and the implications of resetting its correction.
### Recorded reasoning summary
Examining Target Inclusion
I am now questioning the exclusion of future validation targets from the `prepared.sequence_items` used in `pack_public_training_positives`. The comment indicates this suffix is intentionally absent, but I need to understand why, as it feels like a potential oversight.
### Recorded reasoning summary
Identifying Training Contradictions
I'm realizing a critical issue: items intended for validation or testing could be sampled as negatives during training. This penalizes future relevant behavior and contradicts the strict exclusion of training data from evaluation sets. This needs immediate revision.
### run_shell_command · 2026-09-29T05:47:32.565Z
{
"description": "Inspect validation eligible candidates",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\nprint(f\"Total users: {len(uids)}, Total items: {len(item_ids)}\")\ncounts = [np.unpackbits(packed[i], count=len(item_ids), bitorder=\"little\").sum() for i in range(100)]\nprint(f\"Sample eligible items per user: min={min(counts)}, max={max(counts)}, mean={np.mean(counts)}\")\n'"
}
### Tool output · 2026-09-29T05:47:34.180Z
Output: Total users: 8456, Total items: 30000
Sample eligible items per user: min=25547, max=29971, mean=29098.94
Process Group PGID: 1776
## Step 29 · agent · 2026-09-29T05:47:38.877Z
### Recorded reasoning summary
Analyzing Item Eligibility
My focus has shifted to understanding item eligibility, finding that a vast majority (approximately 29,100 out of 30,000) are suitable. Only a small fraction, around 900, previously encountered during training, have been marked as ineligible. I'm now reviewing the scoring from `evaluate_public.py`, noting a `quality_ndcg@10` score of 0.02128.
### run_shell_command · 2026-09-29T05:47:38.877Z
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ndf = pd.read_parquet(\"/app/solution/production_validation.parquet\")\nprint(\"Top 20 most frequently recommended items in baseline:\")\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ntop_rec = df[df[\"rank\"] <= 10][\"item_id\"].value_counts().head(20)\nprint(top_rec)\n\ntop_rec_df = pd.DataFrame({\"item_id\": top_rec.index, \"rec_count\": top_rec.values})\ntop_rec_df = top_rec_df.merge(cat, on=\"item_id\")\nprint(\"\\nCatalog features of top recommended items:\")\nprint(top_rec_df[[\"item_id\", \"rec_count\", \"popularity_rank\", \"positive_listens\", \"organic_positive_listens\"]])\n'",
"description": "Inspect top recommended items in baseline validation ranking"
}
### Tool output · 2026-09-29T05:47:40.514Z
Output: Top 20 most frequently recommended items in baseline:
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
Catalog features of top recommended items:
item_id rec_count ... positive_listens organic_positive_listens
0 6006390 2917 ... 8970 6813
1 8213481 2800 ... 22505 17071
2 6896222 2712 ... 3020 2017
3 8743390 2653 ... 13518 8480
4 5436843 2587 ... 14897 12127
5 2651314 2580 ... 16286 11801
6 8722394 2542 ... 3464 1695
7 3542184 2427 ... 24075 17699
8 5862961 2292 ... 26615 20620
9 906358 2066 ... 22238 17183
10 5927996 1872 ... 14751 10424
11 9378983 1537 ... 24036 18195
12 2185191 1522 ... 13059 8839
13 6901374 1463 ... 25536 17347
14 3033749 1333 ... 16682 12267
15 5252571 1289 ... 14897 11331
16 2408923 1215 ... 7515 4545
17 4423556 1148 ... 6544 2177
18 8351511 1121 ... 14061 9739
19 3375935 1107 ... 5213 3232
[20 rows x 5 columns]
Process Group PGID: 1787
## Step 30 · agent · 2026-09-29T05:48:48.978Z
### Recorded reasoning summary
Analyzing Recommendation Bias
I've noticed a significant trend: a small subset of items is consistently appearing in the top 10 recommendations for a vast majority of users. This suggests a strong bias towards popular items, and I need to investigate the underlying causes.
### Recorded reasoning summary
Investigating Negative Sampling
My recent focus has been on the negative sampling strategy. I'm digging into how both uniform and in-batch negatives are drawn. It appears the uniform selection, picking from 30,000 items, heavily favors less popular tracks due to the long tail distribution. I'm now examining the precise mechanism for in-batch negative selection.
### Recorded reasoning summary
Revising Negative Sampling
I've identified a critical flaw: in-batch negatives are sampled uniformly from unique target items, not weighted by their batch frequency. This negates the intended benefit of using popular items more often as negatives, and I need to explore the `item_correction` function next.
### Recorded reasoning summary
Adjusting Correction Logic
I've found that the `raw` calculation within `item_correction` heavily penalizes popular items, leading to small correction factors. This means unpopular items are disproportionately boosted, and I'm now examining how this impacts the `corrected_negative_loss`.
### Recorded reasoning summary
Evaluating Correction Factors
I've discovered that the current correction logic disproportionately boosts unpopular items. Popular items, despite frequent positive exposure, are barely penalized due to low correction factors. This seems to be a key driver behind the observed recommendation bias.
### Recorded reasoning summary
Quantifying Negative Gradient
My analysis reveals popular items receive minimal negative gradients. Uniform negatives have a minuscule chance of selection, while in-batch negatives are sampled uniformly and downweighted, rendering them ineffective for pushing down popular item embeddings.
### Recorded reasoning summary
Researching Standard Objectives
I'm now exploring established literature and industry practices for two-tower model training objectives. Specifically, I'm looking into In-batch Softmax, Sampled Softmax, and InfoNCE, as well as potential fixes for the current Binary Cross-Entropy negative sampling.
### Recorded reasoning summary
Exploring Alternative Objectives
I'm currently investigating alternative training objectives to address the recommendation bias. My focus is on InfoNCE and various Softmax variants, aiming to refine negative sampling without altering the serving contract or data protocol.
### Recorded reasoning summary
Refining Objective Function
I'm currently delving into how the training objective selects and utilizes negative examples. My focus is on diagnosing limitations within the current negative-sampling strategy and enhancing it to fit within the fixed CPU budget, ensuring all serving and data boundaries remain intact.
### Recorded reasoning summary
Optimizing Negative Sampling
I'm now investigating how the training objective selects and uses negative examples, diagnosing limitations in the current negative-sampling strategy. My aim is to improve this process within the fixed CPU budget, ensuring all serving and data boundaries remain intact.
### Recorded reasoning summary
Revising In-Batch Sampling
I've discovered the in-batch negative sampling incorrectly uses unique targets, destroying the intended popularity bias. I'm now questioning the fixed ratio of in-batch to uniform negatives and exploring if sampling proportionally to batch frequency is feasible within constraints.
### Recorded reasoning summary
Considering Proportional Sampling
I'm now evaluating different combinations of in-batch and uniform negatives, questioning the fixed ratio. I'm also exploring sampling negatives proportionally to their popularity, which seems promising.
### Recorded reasoning summary
Analyzing Correction Factors
I'm examining how the `raw` value is calculated in relation to item probabilities. It appears that popular items, having a probability greater than the inverse of the count of unique items, result in a `raw` value less than one, indicating they are being downweighted.
### Recorded reasoning summary
Investigating Loss Weighting
I'm scrutinizing how `weights` are applied within the `corrected_negative_loss` calculation. It seems unusual for these weights, derived from item correction, to directly multiply the loss value itself, and I need to understand the theoretical justification for this specific weighting scheme in importance-sampled BCE.
### Recorded reasoning summary
Analyzing Loss Weighting
I've discovered that the current loss weighting scheme within `corrected_negative_loss` heavily downweights popular items. It seems the division by `weights.sum(dim=1)` disproportionately assigns loss to rare items, effectively shielding popular items from significant penalty.
### Recorded reasoning summary
Correcting Logit Bias
I'm now comparing my current logit correction approach with established literature, specifically Yi et al. (2019). Their method subtracts `log(p_i)` directly from the logit, which is fundamentally different from my current multiplicative weighting. This difference seems crucial for properly accounting for sampling bias.
### Recorded reasoning summary
Comparing Correction Methods
I am now comparing my current multiplicative weighting scheme for loss correction against the additive method proposed in Yi et al. (2019). Their approach of directly subtracting `log(p_i)` from the logit appears more theoretically sound for bias correction than my current strategy.
### Recorded reasoning summary
Adapting Additive Correction
I'm now implementing the additive logit correction method from Yi et al. (2019). My previous multiplicative approach was incorrectly downweighting popular items, and this new strategy should more accurately address sampling bias, fitting within our CPU constraints.
### Recorded reasoning summary
Adapting Additive Correction
I'm now implementing the additive logit correction method from Yi et al. (2019). My previous multiplicative approach was incorrectly downweighting popular items, and this new strategy should more accurately address sampling bias, fitting within our CPU constraints. I am also investigating the `negative_filter_scope` parameter, questioning the rationale behind its two distinct options.
### Recorded reasoning summary
Investigating Scope Logic
I'm examining the `negative_filter_scope` parameter and questioning why it has two distinct options. My current focus is understanding the logic within `base.draw_filtered_mixed_negatives` and how it filters `known_history` and `liked_items`, contrasting it with `draw_full_history_filtered_negatives`.
### Recorded reasoning summary
Investigating Filter Packing
I'm currently analyzing the `negative_filter_scope` parameter and questioning its two distinct options. My immediate focus is understanding the internal logic of `base.draw_filtered_mixed_negatives`, specifically how it handles filtering based on `known_history` and `liked_items`, and how this contrasts with `draw_full_history_filtered_negatives`. I'm also looking into `known_positive_packed`.
### Recorded reasoning summary
Revising History Filter
I've realized `build_known_positive_filter` is incorrectly using `history_training_counts` from the partition. This should be `partition.training_counts` to accurately reflect the training set for the filter.
### Recorded reasoning summary
Correcting History Filter
I've identified an issue with `build_known_positive_filter`. It's incorrectly referencing `history_training_counts` from the partition when it should be using `partition.training_counts` to ensure the filter accurately reflects the training set for accurate bias correction.
### Recorded reasoning summary
Refining Training Windows
I've identified a critical discrepancy in how training windows are calculated. The current logic, particularly within `retained_target_window_starts`, appears to misinterpret the relationship between total targets, validation counts, and training counts, leading to an incorrect starting point for the training window.
### Recorded reasoning summary
Adjusting Filter Logic
I've found that `build_known_positive_filter` incorrectly uses `history_training_counts` instead of `partition.training_counts`. Furthermore, the validation targets are not filtered by `known_positive_packed` during validation BCE, a deliberate choice I now need to understand.
### Recorded reasoning summary
Examining Validation Targets
I'm questioning why validation targets aren't filtered during validation BCE. While sampled negatives matching the validation target are rejected, other validation targets aren't filtered. During training, items from validation suffixes or disliked items in `train_*` could be sampled as negatives.
### Recorded reasoning summary
Analyzing Validation Relevance
I'm now scrutinizing the validation relevance criteria and the model's ranking process. The target discovery task, specifically identifying organic events within a defined 7-day validation window, seems critical. I'm investigating how `inference_queries` and `rank_candidates` interact to assess performance.
### Recorded reasoning summary
Evaluating Ranking Logic
I'm analyzing the raw dot product used for scoring. The norms of queries and items seem critically important here, and I'm investigating their distribution from baseline training logs to understand potential biases introduced at this stage.
### Recorded reasoning summary
Analyzing Item Norms
I'm examining the item norms from the baseline training logs. The average item norm is 1.76 with a standard deviation of 0.83. I'm now looking at the correlation between item popularity and dot product scores, and I've noticed that the most popular items are consistently ranking highest.
### Recorded reasoning summary
Correlating Item Features
I'm correlating item features with their norms and popularity. It seems features like log-transformed positives and organic listens, along with a popularity rank inverse, are being standardized. I'm now examining how these standardized features feed into the item tower.
### Recorded reasoning summary
Analyzing Feature Engineering
I've identified that the item tower directly uses popularity-related features like log-positives and organic listens. Given the training data heavily features popular items as positives and uniform negatives are rare, this direct encoding is likely amplifying the existing bias.
### Recorded reasoning summary
Analyzing In-Batch Negatives
I've pinpointed the issue: `torch.unique(targets)` means popular and rare items have equal representation in the in-batch negative pool, regardless of their actual frequency. This negates the intended bias correction, and the subsequent `item_correction` further exacerbates the problem by downweighting popular items.
### Recorded reasoning summary
Investigating Gradient Impact
I've realized that Track A, when used as a negative, receives a near-zero gradient due to loss weighting. However, as a positive 50 times, it gets 50 positive gradients, causing its representation to explode and align with nearly every user query. This explains the extreme recommendation counts for popular items.
### run_shell_command · 2026-09-29T05:48:48.978Z
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nlistens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nlikes = pd.read_parquet(\"/task/data/val_likes.parquet\")\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = {int(uid): i for i, uid in enumerate(val_npz[\"uids\"])}\nitem_ids = {int(item): i for i, item in enumerate(val_npz[\"item_ids\"])}\npacked = val_npz[\"packed_eligible\"]\n\ndef is_eligible(uid, item):\n r = uids.get(uid)\n c = item_ids.get(item)\n if r is None or c is None: return False\n return bool((packed[r, c // 8] >> (c % 8)) & 1)\n\nstrong = listens[listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[likes.uid.isin(targets) & (likes.is_organic == 1)][[\"uid\", \"item_id\"]].drop_duplicates()\n\neligible_strong = [row for row in strong.itertuples(index=False) if is_eligible(row.uid, row.item_id)]\neligible_likes = [row for row in organic_likes.itertuples(index=False) if is_eligible(row.uid, row.item_id)]\n\nprint(f\"Eligible strong listens: {len(eligible_strong)}\")\nprint(f\"Eligible organic likes: {len(eligible_likes)}\")\n\nitems_in_truth = pd.Series([r[1] for r in eligible_strong] + [r[1] for r in eligible_likes]).value_counts()\nprint(f\"Unique items in truth: {len(items_in_truth)}\")\nprint(\"Top 10 most common items in truth:\")\nprint(items_in_truth.head(10))\n\ntop_truth_items = pd.DataFrame({\"item_id\": items_in_truth.head(10).index, \"truth_count\": items_in_truth.head(10).values}).merge(cat, on=\"item_id\")\nprint(top_truth_items[[\"item_id\", \"truth_count\", \"popularity_rank\", \"positive_listens\"]])\n'",
"description": "Inspect ground truth distribution in validation window"
}
### Tool output · 2026-09-29T05:48:51.106Z
Output: Eligible strong listens: 35597
Eligible organic likes: 4417
Unique items in truth: 14124
Top 10 most common items in truth:
6399259 203
6252104 122
8250570 96
2055777 90
1265696 83
3134183 62
7579671 54
1005397 52
7419525 50
4948220 47
Name: count, dtype: int64
item_id truth_count popularity_rank positive_listens
0 6399259 203 3329 1276
1 6252104 122 5622 855
2 8250570 96 4263 1059
3 2055777 90 1170 2465
4 1265696 83 7350 681
5 3134183 62 2602 1517
6 7579671 54 2789 1441
7 1005397 52 240 5546
8 7419525 50 11086 476
9 4948220 47 6675 740
Process Group PGID: 1817
## Step 31 · agent · 2026-09-29T05:50:00.514Z
### Recorded reasoning summary
Observing Catalog Popularity
I'm currently analyzing the popularity distribution of ground truth items within the larger catalog. It's striking to note that none of the top-ranked items in the catalog appear within the provided ground truth list.
### Recorded reasoning summary
Investigating Popularity Bias
My current focus is on how the training objective handles negative examples, given the recent realization that top-ranked items are often already encountered during training. This bias seems to be limiting the effectiveness of the baseline, especially since the validation set deliberately excludes these highly popular tracks. I'm examining the current negative-sampling strategy to identify its limitations.
### Recorded reasoning summary
Examining Negative Sampling
I'm now delving into the specifics of how negative examples are selected, paying close attention to `draw_full_history_filtered_negatives` and `corrected_negative_loss`. My goal is to pinpoint weaknesses in the current strategy.
### Recorded reasoning summary
Refining Negative Sampling
I'm now inspecting the code responsible for sampling negative items. Specifically, I'm tracing how `uniform` and `inbatch` negatives are generated, looking to understand how `corrected_count` influences the selection process.
### Recorded reasoning summary
Tracing Negative Sampling Logic
I'm now tracing the code flow for generating negative samples. I'm observing how `corrected_count` influences the selection between `uniform` and `inbatch` negatives, and noting how `filler` is used when `corrected_count` is low. I'm also examining the `invalid_full_history_negatives` function for potential issues.
### Recorded reasoning summary
Identifying Sampling Flaws
I'm now scrutinizing the in-batch negative sampling mechanism. It appears `unique_targets` are sampled uniformly, which deviates from the standard approach of sampling proportional to batch item frequency. This could be a significant weakness in the current strategy.
### Recorded reasoning summary
Analyzing Sampling Inconsistencies
I'm seeing that `torch.unique(targets)` is flattening the batch's popularity distribution, and it's now taking fewer in-batch negatives than uniform ones. It seems uniform negatives rarely hit popular items from the large catalog. Additionally, I'm observing that the propensity correction in `corrected_negative_loss` is unduly penalizing unpopular items while favoring popular ones.
### Recorded reasoning summary
Correcting Loss Penalties
I've identified that the propensity correction within `corrected_negative_loss` is drastically over-penalizing unpopular items and, conversely, excessively favoring popular ones. This imbalance is amplifying the loss for rare items by a factor of 16, significantly distorting the training signal.
### Recorded reasoning summary
Correcting Loss Imbalances
I've uncovered a critical issue where the propensity correction in `corrected_negative_loss` is overly aggressive, reducing the loss contribution of popular items by a factor of 16. This, combined with their frequent positive occurrences, is essentially teaching the model to assign uniformly high positive logits and no negative logits to popular items.
### Recorded reasoning summary
Revising Loss Function
I'm now realizing the current loss function's independence of the logit difference is a major issue. The gradients are severely imbalanced, with negatives contributing significantly less than positives. I'm exploring InfoNCE and BPR as more appropriate alternatives for two-tower retrieval models.
### Recorded reasoning summary
Exploring Alternative Loss Functions
I'm now considering InfoNCE and BPR as potential replacements for the current loss function. The observed gradient imbalance, with negatives contributing far less than positives due to the independence from logit difference, necessitates a shift to loss formulations better suited for two-tower retrieval models.
### Recorded reasoning summary
Testing Loss Function Hypotheses
I am now evaluating InfoNCE and BPR as replacements for the current loss, specifically examining how their gradient structures address the observed imbalances. My focus is on how they handle the logit difference to create more balanced contributions from positive and negative examples within the microbatch context.
### Recorded reasoning summary
Evaluating Loss Function Hypotheses
I'm now testing InfoNCE and BPR to see how their gradient structures address the identified imbalances. My current focus is on how they incorporate the logit difference to ensure more equitable contributions from both positive and negative examples within the microbatch.
### Recorded reasoning summary
Comparing Target Weighting
I'm now comparing the impact of different `target_weighting` options. Specifically, I'm investigating how `discovery-graded`, which prioritizes first-encounter targets, differs from the `organic-strong` weighting used in the baseline, and examining the in-batch negative sampling logic within `draw_full_history_filtered_negatives`.
### Recorded reasoning summary
Analyzing Sampling Bias
I've identified a critical flaw in how in-batch negatives are sampled. They're currently drawn uniformly from unique targets, disregarding actual batch frequency. This means popular items are just as likely to be sampled as rare ones, regardless of their prominence in the batch, undermining the sampling's effectiveness.
### Recorded reasoning summary
Adjusting Correction Logic
I'm now revisiting the `build_item_correction` function. I've realized that the `probabilities` used there refer to the entire training set, not just the current batch. This leads to incorrect raw correction values, where highly popular items are under-corrected and rare items over-corrected, significantly impacting subsequent loss calculations.
### Recorded reasoning summary
Revising Correction Application
I'm now realizing the `build_item_correction` function's calculation `sum(raw * weights) / sum(weights)` is applying the correction to the *average raw loss* of in-batch negatives, not to their individual logits before loss calculation. This is a critical misapplication of the correction mechanism.
### Recorded reasoning summary
Correcting Loss Misapplication
I've discovered that the `build_item_correction` function applies the correction to the average raw loss of in-batch negatives, rather than to their individual logits before loss calculation. This misapplication significantly diminishes the impact of popular items on the gradients, reducing their corrective influence by a factor of 17.
### Recorded reasoning summary
Testing Correction Scenarios
I'm now exploring counterfactuals, specifically what happens if propensity correction is disabled or if in-batch negatives are sampled proportionally. The current correction's impact is so skewed that these tests are crucial. I'm also considering InfoNCE as a baseline loss.
### Recorded reasoning summary
Testing Loss Function Variants
I'm now examining variants of InfoNCE, specifically focusing on how different temperature settings ($\tau$) impact gradient balance and convergence. My aim is to find a sweet spot that effectively penalizes dissimilar items without over-penalizing rare ones.
### Recorded reasoning summary
Comparing Loss Functions
I'm now comparing InfoNCE, BPR, and Margin Ranking Loss. I'm particularly interested in how InfoNCE's temperature parameter affects gradient balance and how BPR directly optimizes pairwise margins. I'm also considering a revised BCE, focusing on fixing its negative sampling and weighting issues.
### Recorded reasoning summary
Analyzing Loss Function Impact
I'm now analyzing how the loss function, particularly the averaging of negatives in BCE, led to an ineffective training signal. This averaging allowed popular items to receive very high logits while rare items received very low logits, creating a misleadingly low training loss and ignoring user preferences.
### Recorded reasoning summary
Testing Loss Function Impact
I'm now analyzing how the loss function, particularly the averaging of negatives in BCE, led to an ineffective training signal. This averaging allowed popular items to receive very high logits while rare items received very low logits, creating a misleadingly low training loss and ignoring user preferences.
### Recorded reasoning summary
Implementing InfoNCE Loss
I'm now implementing InfoNCE, recognizing its elegant simplicity and lack of added parameters or changes to the serving contract. It appears to be a direct and effective replacement for the previous loss formulation.
### Recorded reasoning summary
Exploring Full In-Batch Negatives
I'm now considering utilizing all in-batch items as negatives. With a `user_batch_size` of 64 and up to 6,400 positive items per block, computing a `[6400, 6400]` logits matrix is feasible within memory and computational constraints.
### Recorded reasoning summary
Leveraging Full In-Batch Negatives
I've realized that computing the full `[6400, 6400]` logits matrix for in-batch negatives is feasible, offering a natural frequency distribution of popular items as hard negatives. I can also mask out known positives using `known_positive_packed` and significantly reduce item encoding computation by encoding only B items.
### Recorded reasoning summary
Leveraging In-Batch Negatives
I've decided to switch to an in-batch negative sampling strategy. This is significantly faster, provides thousands of negatives, naturally reflects item popularity, and allows for false negative masking. I can also incorporate a small, shared pool of uniform catalog negatives.
### Recorded reasoning summary
Refining Negative Sampling Strategy
I've decided to adopt an in-batch negative sampling strategy. This approach offers substantial speed advantages, provides a large pool of naturally occurring hard negatives reflecting item popularity, and allows for effective false negative masking. I will also incorporate a small set of uniform catalog negatives.
### Recorded reasoning summary
Investigating Loss Function Effects
I'm now analyzing how the previous BCE loss function's averaging of negatives created an ineffective training signal. Popular items received disproportionately high logits, while rare items received low ones, resulting in a misleadingly low training loss that ignored crucial user preferences.
### Recorded reasoning summary
Testing Loss Function Combinations
I'm now considering testing various combinations of loss functions and sampling strategies. Specifically, I want to evaluate InfoNCE with in-batch negatives and controlled temperature settings, as well as explore `discovery-graded` target weighting alongside these changes. This comprehensive approach aims to isolate the impact of each element.
### Recorded reasoning summary
Evaluating Training Duration
I've realized the training ran for only a single epoch, which was dictated by the validation loss metric and the default `epochs` setting. This limited duration likely prevented deeper learning. I'm now reviewing the validation evaluation logic and default configuration.
### update_topic · 2026-09-29T05:50:00.514Z
{
"summary": "We completed the initial investigation and reproduced the public baseline NDCG@10 of 0.02128. Analysis revealed severe limitations in the baseline negative sampling: (1) in-batch negatives are drawn uniformly from de-duplicated targets rather than true frequency, (2) the propensity correction formula heavily penalizes rare items while shrinking the negative gradient on popular items by 16x, (3) independent BCE with 1:1 positive-to-average-negative balance allows popular items to dominate all recommendations, and (4) false-negative filtering misses relevant negative nuances. We will now design and test targeted improvements to negative selection and the training objective.",
"strategic_intent": "Diagnose negative sampling limitations and test objective improvements on the validation set.",
"title": "Diagnosing and Improving Negative Sampling Objective"
}
### Tool output · 2026-09-29T05:50:02.889Z
Current topic: "Diagnosing and Improving Negative Sampling Objective"
Topic summary: We completed the initial investigation and reproduced the public baseline NDCG@10 of 0.02128. Analysis revealed severe limitations in the baseline negative sampling: (1) in-batch negatives are drawn uniformly from de-duplicated targets rather than true frequency, (2) the propensity correction formula heavily penalizes rare items while shrinking the negative gradient on popular items by 16x, (3) independent BCE with 1:1 positive-to-average-negative balance allows popular items to dominate all recommendations, and (4) false-negative filtering misses relevant negative nuances. We will now design and test targeted improvements to negative selection and the training objective.
Strategic Intent: Diagnose negative sampling limitations and test objective improvements on the validation set.
## Step 32 · agent · 2026-09-29T05:50:26.342Z
### Recorded reasoning summary
Exploring Retrieval Success Factors
I'm currently dissecting the mechanics behind effective two-tower retrieval models, specifically in the context of music discovery. My immediate focus is on understanding the nuances of negative sampling strategies, starting with the `draw_full_history_filtered_neg` approach.
### Recorded reasoning summary
Refining In-Batch Negative Sampling
My focus has shifted to enhancing in-batch negative sampling. Rather than uniform selection from unique targets, I'm exploring weighted sampling strategies. This aims to prioritize more relevant negative examples within the batch for improved model performance.
### Recorded reasoning summary
Optimizing In-Batch Negatives
I'm delving deeper into the optimal strategy for sampling in-batch negatives. The core question is whether to sample directly from all possible targets or proportional to popularity. I've realized that drawing directly from the `targets` tensor with replacement inherently captures popularity, which seems to be the standard definition of in-batch negative sampling. The `unique_targets` step likely serves to prevent redundant priority calculations.
### Recorded reasoning summary
Adjusting In-Batch Negatives
I've discovered that directly sampling indices within the batch, excluding the positive item, naturally yields negatives proportional to their batch frequency. I'm now examining the baseline's propensity correction and loss formulation to understand how it leverages these sampled negatives with the global training probabilities.
### Recorded reasoning summary
Revising Loss Weighting
I'm rethinking how I apply weights in the Binary Cross-Entropy loss. I've realized that downweighting popular items when they appear as negatives might be counterproductive, effectively telling the model to ignore penalizing them, which could lead to over-recommendation of popular items. I'm considering setting all weights to 1.0 to ensure all negatives contribute equally.
### Recorded reasoning summary
Analyzing Gradient Behavior
I'm currently dissecting the gradients of InfoNCE and BCE, specifically observing how they interact with negative sample scores. It appears InfoNCE naturally focuses on harder negatives, while BCE's gradient saturates for high scores, potentially limiting its effectiveness in penalizing them.
**Comparing Loss Function Gradients**
I've been comparing the gradient behavior of InfoNCE versus Binary Cross-Entropy (BCE) with respect to negative item scores. A key insight is that InfoNCE's gradient is directly proportional to the exponentiated score of the negative item, meaning it inherently prioritizes harder negatives. Conversely, BCE's gradient saturates, potentially limiting its ability to strongly penalize highly-scored, incorrect negatives.
### Recorded reasoning summary
Balancing InfoNCE Gradients
I've confirmed that InfoNCE's gradient structure inherently balances positive and negative contributions, preventing popularity bias by ensuring hard negatives, especially popular ones with high scores, receive significant penalization. This contrasts with BCE, where gradients can saturate.
### Recorded reasoning summary
Increasing In-Batch Negatives
I'm exploring the impact of increasing the number of in-batch negatives within InfoNCE. The current setup with more uniform than in-batch negatives seems inefficient. I'm hypothesizing that a higher proportion of in-batch negatives could lead to better NDCG@10 scores and am planning to test this hypothesis directly.
### read_file · 2026-09-29T05:50:26.342Z
{
"end_line": 1190,
"start_line": 1155,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:50:26.349Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1155-1190 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1191.
--- FILE CONTENT (truncated) ---
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())
## Step 33 · agent · 2026-09-29T05:50:34.518Z
### Recorded reasoning summary
Analyzing Logit Calculation
I'm currently examining lines 1168-1180, specifically focusing on the distinct methods for calculating positive and negative logits. The direct summation for positive logits contrasts with the `einsum` approach used for negative logits, and I'm exploring the implications of this difference.
### read_file · 2026-09-29T05:50:34.518Z
{
"start_line": 1410,
"end_line": 1480,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:50:34.525Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1410-1480 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: 1481.
--- FILE CONTENT (truncated) ---
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 {
## Step 34 · agent · 2026-09-29T05:50:53.718Z
### Recorded reasoning summary
Exploring Objective Flexibility
I'm currently investigating how to integrate support for diverse objectives and negative loss configurations within `score_target_microbatches` and `draw_training`. This seems like a promising avenue to enhance model adaptability.
### Recorded reasoning summary
Refining Contrastive Loss
I'm exploring InfoNCE loss implementations, specifically how to handle `positive_logits` and `negative_logits`. I need to clarify the role and selection of the `temperature` parameter, considering it as a configurable option and examining its impact on the logits scaling.
### Recorded reasoning summary
Adapting Negative Sampling
I'm re-evaluating the `unique_targets` logic for in-batch negatives. Instead of using `torch.unique`, I'm considering sampling directly from the `targets_cpu` array to potentially refine negative selection.
### Recorded reasoning summary
Sampling Targets Directly
I've realized a much simpler way to implement in-batch negatives. I can directly sample from `targets_cpu` using `torch.randint`, which naturally accounts for item frequency. Invalid negatives will then be handled by the existing rejection sampling loop.
### run_shell_command · 2026-09-29T05:50:53.718Z
{
"command": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(df.info())\nprint(df.head())\n'",
"description": "Inspect candidate_catalog.parquet columns"
}
### Tool output · 2026-09-29T05:50:55.377Z
Output:
RangeIndex: 30000 entries, 0 to 29999
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 item_id 30000 non-null uint32
1 positive_listens 30000 non-null int64
2 organic_positive_listens 30000 non-null uint64
3 popularity_rank 30000 non-null int32
dtypes: int32(1), int64(1), uint32(1), uint64(1)
memory usage: 703.3 KB
None
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
Process Group PGID: 1843
## Step 35 · agent · 2026-09-29T05:51:05.084Z
### Recorded reasoning summary
Refining Candidate Selection
I'm observing a dramatic shift in `positive_listens` within `candidate_catalog.parquet`, shrinking from over 26,000 to just 1. Simultaneously, `popularity_rank` has expanded its range from 1 to 30,000. This suggests a significant recalibration occurring during data preparation, particularly in how `item_index` is being utilized.
### Recorded reasoning summary
Testing InfoNCE Implementation
I'm confirming that `item_id` directly corresponds to `popularity_rank`, indicating an inherent sort. I'm now preparing to run Experiment 1 to analyze InfoNCE loss on sampled negatives, isolating its effect compared to BCE by focusing on `score_target_microbatches` and the cross-entropy calculation with a temperature of 0.1.
### Recorded reasoning summary
Tracking Loss Metrics
I'm scrutinizing the `statistics` recording, specifically questioning what `loss_sum`, `positive_sum`, `negative_sum`, `pair_correct`, and `weight_sum` should encompass. I'm also concerned about potential InfoNCE loss evaluation during validation, and the implications of `target_weighting` in the configuration.
### Recorded reasoning summary
Investigating Weighting Strategies
I'm re-evaluating `organic-strong` and `discovery-graded` weighting schemes, noting the significant boost `discovery-graded` gave to organic likes. For now, I'll stick with the default `target_weighting` to isolate the impact of InfoNCE and in-batch sampling. I'm also setting up a clean test script with CLI flags to manage these variables.
### read_file · 2026-09-29T05:51:05.084Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 75,
"start_line": 30
}
### Tool output · 2026-09-29T05:51:05.091Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 30-75 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) ---
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)
## Step 36 · agent · 2026-09-29T05:51:43.569Z
### Recorded reasoning summary
Exploring Configuration Options
I'm currently investigating the impact of new `Config` parameters, specifically `loss_type`, `temperature`, and `inbatch_sampling`. My focus is on understanding how these additions, especially `inbatch_sampling` with its `unique` and `frequency` modes, interact with existing features like `uniform_negatives`.
### grep_search · 2026-09-29T05:51:43.569Z
{
"dir_path": "/app/solution",
"pattern": "add_argument"
}
### Tool output · 2026-09-29T05:51:43.583Z
Found 27 matches for pattern "add_argument" in path "/app/solution":
---
File: train_current_model.py
L1924: parser.add_argument(
L1927: parser.add_argument("--output", type=Path, required=True)
L1928: parser.add_argument("--public-dir", type=Path, required=True)
L1929: parser.add_argument("--prepared-cache", type=Path)
L1930: parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
L1931: parser.add_argument("--threads", type=int, default=8)
L1932: parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
L1933: parser.add_argument(
L1936: parser.add_argument("--epochs", type=int, default=Config.epochs)
L1937: parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
L1938: parser.add_argument(
L1943: parser.add_argument(
L1948: parser.add_argument(
L1953: parser.add_argument(
L1958: parser.add_argument(
L1963: parser.add_argument(
L1968: parser.add_argument(
L1973: parser.add_argument(
L1978: parser.add_argument(
L1984: parser.add_argument(
L1990: parser.add_argument(
L1995: parser.add_argument(
L2000: parser.add_argument("--checkpoint-dir", type=Path)
L2001: parser.add_argument("--resume-checkpoint", type=Path)
L2002: parser.add_argument("--seed", type=int, default=Config.seed)
L2003: parser.add_argument("--ranking-batch-size", type=int, default=64)
L2004: parser.add_argument("--prepare-only", action="store_true")
---
## Step 37 · agent · 2026-09-29T05:52:05.829Z
### Recorded reasoning summary
Exploring Argument Configuration
I'm currently investigating how including previously omitted arguments like `uniform_negatives` and `inbatch_negatives` in CLI configurations might impact performance. I'm also looking into the interaction between `draw_full_history_filtered_negatives` and in-batch negative sampling.
### Recorded reasoning summary
Analyzing In-Batch Sampling Logic
I'm dissecting the in-batch negative sampling mechanism when `inbatch_sampling` is set to "frequency." Specifically, I'm examining how `config.inbatch_negatives` influences the selection of sampled indices directly from the batch of targets.
### Recorded reasoning summary
Refining In-Batch Negative Selection
I'm currently focusing on the selection of in-batch negatives, specifically addressing the potential issue where a randomly chosen negative might coincidentally be the user's actual target or an item they've already interacted with. I'm examining the logic for handling these invalid selections.
### Recorded reasoning summary
Testing Negative Replacement Logic
I'm verifying that the current logic correctly handles invalid in-batch negatives by replacing them. I'm also exploring whether these replacements should come from a uniform draw or another in-batch draw, and considering how hard or popularity-based negatives fit into this.
### Recorded reasoning summary
Verifying Item Representation
I'm investigating a specific item's representation by verifying its low probability of being sampled as a negative due to its absence in target batches. I'm planning to test training configurations, starting with InfoNCE loss and frequency-based in-batch negatives, and will then inspect item norms.
### Recorded reasoning summary
Validating Logit Calculations
I'm now focused on validating the computed logits, particularly for InfoNCE loss. I'm scrutinizing how the temperature scaling impacts the final logit values before the cross-entropy calculation.
### Recorded reasoning summary
Validating Code Integration
I'm currently tracing the integration of new code snippets within `train_current_model.py` to ensure seamless execution. My immediate plan is to run a validation experiment using the provided command, focusing on the timing of training and ranking processes to assess performance impacts.
### Recorded reasoning summary
Examining Loss Type Impact
I'm verifying how adding new fields like `loss_type` and `temperature` to `Config` affects downstream functions. My immediate focus is on `score_target_microbatches`, specifically its `pair_accuracy` calculation, and how it interacts with `config.loss_type`.
### read_file · 2026-09-29T05:52:05.829Z
{
"start_line": 645,
"end_line": 685,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:52:05.839Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 645-685 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 686.
--- FILE CONTENT (truncated) ---
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
## Step 38 · agent · 2026-09-29T05:52:13.203Z
### Recorded reasoning summary
Exploring In-batch Sampling
I'm currently focusing on the specific scenario where `config.inbatch_sampling` is set to "frequency." My thought process is delving into how this impacts the sampling of in-batch negatives, particularly how random targets from the current batch are selected.
### Recorded reasoning summary
Refining Frequency Sampling
My current focus is on optimizing the "frequency" in-batch sampling, specifically how random targets are chosen from the current batch. I'm also examining the rejection loop's efficiency in handling invalid negatives and ensuring proper replacement strategies are implemented.
### Recorded reasoning summary
Analyzing Correction Factors
I'm considering the implications of the `correction` factor in BCE loss. If `inbatch_correction_power` is zero, this factor becomes a simple one, which I'm verifying. I'm also pivoting to investigate standard InfoNCE loss before diving deeper into logQ corrections.
### read_file · 2026-09-29T05:52:13.203Z
{
"start_line": 50,
"file_path": "/app/solution/train_current_model.py",
"end_line": 80
}
### Tool output · 2026-09-29T05:52:13.211Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 50-80 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 81.
--- FILE CONTENT (truncated) ---
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
item_ids: np.ndarray
packed: np.ndarray
## Step 39 · agent · 2026-09-29T05:52:17.052Z
### replace · 2026-09-29T05:52:17.052Z
{
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"",
"instruction": "Add loss_type, temperature, and inbatch_sampling to Config",
"new_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n loss_type: str = \"bce\"\n temperature: float = 0.1\n inbatch_sampling: str = \"unique\"\n target_weighting: str = \"organic-strong\""
}
### Tool error · 2026-09-29T05:52:17.062Z
EACCES: permission denied, open '/app/solution/train_current_model.py'
## Step 40 · agent · 2026-09-29T05:52:19.587Z
### run_shell_command · 2026-09-29T05:52:19.587Z
{
"command": "ls -la /app/solution",
"description": "Check file permissions in /app/solution"
}
### Tool output · 2026-09-29T05:52:19.603Z
Output: total 524012
drwxr-xr-x 1 agent agent 4096 Sep 29 05:46 .
drwxr-xr-x 1 agent agent 4096 Sep 29 05:32 ..
-rw-r--r-- 1 agent agent 1244230 Sep 29 05:44 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 535225808 Sep 29 05:38 val_cache.npz
Process Group PGID: 1874
## Step 41 · agent · 2026-09-29T05:52:23.550Z
### Recorded reasoning summary
Fixing Permissions
I've encountered a read-only issue with files copied from the current model directory. My plan is to rectify this by applying write permissions to the files within the solution directory using `chmod u+w`.
### run_shell_command · 2026-09-29T05:52:23.550Z
{
"description": "Grant write permissions to agent user for solution python files",
"command": "chmod u+w /app/solution/*.py"
}
### Tool output · 2026-09-29T05:52:23.594Z
Output: (empty)
Process Group PGID: 1877
## Step 42 · agent · 2026-09-29T05:52:26.043Z
### run_shell_command · 2026-09-29T05:52:26.043Z
{
"command": "ls -la /app/solution",
"description": "Verify permissions in /app/solution"
}
### Tool output · 2026-09-29T05:52:26.059Z
Output: total 524012
drwxr-xr-x 1 agent agent 4096 Sep 29 05:46 .
drwxr-xr-x 1 agent agent 4096 Sep 29 05:32 ..
-rw-r--r-- 1 agent agent 1244230 Sep 29 05:44 production_validation.parquet
-rw-r--r-- 1 agent agent 80058 Sep 29 05:46 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 29 05:46 two_tower_model.py
-rw-r--r-- 1 agent agent 535225808 Sep 29 05:38 val_cache.npz
Process Group PGID: 1880
## Step 43 · agent · 2026-09-29T05:52:29.898Z
### replace · 2026-09-29T05:52:29.898Z
{
"allow_multiple": false,
"new_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n loss_type: str = \"bce\"\n temperature: float = 0.1\n inbatch_sampling: str = \"unique\"\n target_weighting: str = \"organic-strong\"",
"old_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"",
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add loss_type, temperature, and inbatch_sampling to Config"
}
### Tool output · 2026-09-29T05:52:29.924Z
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"
loss_type: str = "bce"
temperature: float = 0.1
inbatch_sampling: str = "unique"
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 44 · agent · 2026-09-29T05:52:33.159Z
### read_file · 2026-09-29T05:52:33.159Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 650,
"end_line": 685
}
### Tool output · 2026-09-29T05:52:33.165Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 650-685 of 2168 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 686.
--- FILE CONTENT (truncated) ---
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
## Step 45 · agent · 2026-09-29T05:52:41.343Z
### Recorded reasoning summary
Exploring Batch Sampling
I'm currently deep in the `draw_full_history_filtered_negatives` function, specifically examining the `frequency` sampling case when `inbatch_sampling` is enabled and the batch size exceeds one. My focus is on understanding the `rand_indices` generation in this scenario.
### Recorded reasoning summary
Refining In-Batch Sampling
I'm dissecting the generation of `rand_indices` when `inbatch_sampling` is active and `frequency` sampling is used with a batch size greater than one. My current focus is on the intricacies of creating these random indices within the `draw_full_history_filtered_negatives` function to ensure correct negative sample selection.
### replace · 2026-09-29T05:52:41.343Z
{
"allow_multiple": false,
"new_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n if config.inbatch_sampling == \"frequency\" and batch_size > 1:\n rand_indices = torch.randint(\n batch_size,\n (batch_size, config.inbatch_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.from_numpy(targets_cpu)[rand_indices]\n corrected_count = config.inbatch_negatives\n else:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)",
"instruction": "Support frequency-based in-batch sampling in draw_full_history_filtered_negatives",
"file_path": "/app/solution/train_current_model.py",
"old_string": " inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)"
}
### Tool output · 2026-09-29T05:52:41.367Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
if config.inbatch_sampling == "frequency" and batch_size > 1:
rand_indices = torch.randint(
batch_size,
(batch_size, config.inbatch_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.from_numpy(targets_cpu)[rand_indices]
corrected_count = config.inbatch_negatives
else:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
...
## Step 46 · agent · 2026-09-29T05:52:44.700Z
### read_file · 2026-09-29T05:52:44.700Z
{
"end_line": 1195,
"file_path": "/app/solution/train_current_model.py",
"start_line": 1165
}
### Tool output · 2026-09-29T05:52:46.044Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1165-1195 of 2178 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1196.
--- FILE CONTENT (truncated) ---
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")
## Step 47 · agent · 2026-09-29T05:52:52.996Z
### Recorded reasoning summary
Refining Microbatch Scoring
I'm currently examining the `score_target_microbatches` function, specifically how positive and negative logits are computed. My focus is on the matrix multiplications and the einsum operation used for calculating the similarity scores, ensuring their efficiency and correctness within the microbatching framework.
### replace · 2026-09-29T05:52:52.996Z
{
"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.loss_type == \"infonce\":\n logits = torch.cat(\n [positive_logits.unsqueeze(1), negative_logits], dim=1\n ) / config.temperature\n target_indices = torch.zeros(\n len(logits), dtype=torch.long, device=device\n )\n loss = F.cross_entropy(logits, target_indices, reduction=\"none\")\n loss_sum = (selected_weights * 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,
"old_string": " positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"instruction": "Support InfoNCE loss in score_target_microbatches"
}
### Tool output · 2026-09-29T05:52:53.014Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
if config.loss_type == "infonce":
logits = torch.cat(
[positive_logits.unsqueeze(1), negative_logits], dim=1
) / config.temperature
target_indices = torch.zeros(
len(logits), dtype=torch.long, device=device
)
loss = F.cross_entropy(logits, target_indices, reduction="none")
loss_sum = (selected_weights * 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 · 2026-09-29T05:52:56.894Z
### read_file · 2026-09-29T05:52:56.894Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1970,
"start_line": 1935
}
### Tool output · 2026-09-29T05:52:56.900Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1935-1970 of 2188 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: 1971.
--- FILE CONTENT (truncated) ---
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,
)
## Step 49 · agent · 2026-09-29T05:52:59.611Z
### read_file · 2026-09-29T05:52:59.611Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1970,
"end_line": 2050
}
### Tool output · 2026-09-29T05:52:59.618Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1970-2050 of 2188 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2051.
--- FILE CONTENT (truncated) ---
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
## Step 50 · agent · 2026-09-29T05:53:02.486Z
### read_file · 2026-09-29T05:53:02.486Z
{
"start_line": 2050,
"end_line": 2090,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:53:02.492Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2050-2090 of 2188 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2091.
--- FILE CONTENT (truncated) ---
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:
## Step 51 · agent · 2026-09-29T05:53:08.399Z
### Recorded reasoning summary
Implementing CLI Arguments
I'm integrating CLI arguments for loss type, temperature, and in-batch sampling into the parser, ensuring they are correctly passed to the `Config` object for flexible experimentation.
### replace · 2026-09-29T05:53:08.399Z
{
"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 \"--loss-type\",\n choices=(\"bce\", \"infonce\"),\n default=Config.loss_type,\n )\n parser.add_argument(\n \"--temperature\",\n type=float,\n default=Config.temperature,\n )\n parser.add_argument(\n \"--inbatch-sampling\",\n choices=(\"unique\", \"frequency\"),\n default=Config.inbatch_sampling,\n )\n parser.add_argument(\n \"--uniform-negatives\",\n type=int,\n default=Config.uniform_negatives,\n )\n parser.add_argument(\n \"--inbatch-negatives\",\n type=int,\n default=Config.inbatch_negatives,\n )\n parser.add_argument(\n \"--inbatch-correction-power\",\n type=float,\n default=Config.inbatch_correction_power,\n )",
"old_string": " parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )",
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add CLI arguments for loss_type, temperature, inbatch_sampling, uniform_negatives, inbatch_negatives, inbatch_correction_power"
}
### Tool output · 2026-09-29T05:53:08.415Z
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(
"--loss-type",
choices=("bce", "infonce"),
default=Config.loss_type,
)
parser.add_argument(
"--temperature",
type=float,
default=Config.temperature,
)
parser.add_argument(
"--inbatch-sampling",
choices=("unique", "frequency"),
default=Config.inbatch_sampling,
)
parser.add_argument(
"--uniform-negatives",
type=int,
default=Config.uniform_negatives,
)
parser.add_argument(
"--inbatch-negatives",
type=int,
default=Config.inbatch_negatives,
)
parser.add_argument(
"--inbatch-correction-power",
type=float,
default=Config.inbatch_correction_power,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
...
## Step 52 · agent · 2026-09-29T05:53:11.567Z
### read_file · 2026-09-29T05:53:11.567Z
{
"start_line": 2040,
"file_path": "/app/solution/train_current_model.py",
"end_line": 2090
}
### Tool output · 2026-09-29T05:53:11.574Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2040-2090 of 2218 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2091.
--- FILE CONTENT (truncated) ---
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,
## Step 53 · agent · 2026-09-29T05:53:16.350Z
### read_file · 2026-09-29T05:53:16.350Z
{
"end_line": 2115,
"start_line": 2085,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:53:16.356Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 2085-2115 of 2218 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: 2116.
--- FILE CONTENT (truncated) ---
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,
## Step 54 · agent · 2026-09-29T05:53:21.000Z
### replace · 2026-09-29T05:53:21.000Z
{
"allow_multiple": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,",
"new_string": " learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n inbatch_correction_power=args.inbatch_correction_power,\n negative_filter_scope=args.negative_filter_scope,\n loss_type=args.loss_type,\n temperature=args.temperature,\n inbatch_sampling=args.inbatch_sampling,\n target_weighting=args.target_weighting,",
"instruction": "Pass loss_type, temperature, inbatch_sampling, uniform_negatives, inbatch_negatives, inbatch_correction_power to Config"
}
### Tool output · 2026-09-29T05:53:21.017Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
uniform_negatives=args.uniform_negatives,
inbatch_negatives=args.inbatch_negatives,
inbatch_correction_power=args.inbatch_correction_power,
negative_filter_scope=args.negative_filter_scope,
loss_type=args.loss_type,
temperature=args.temperature,
inbatch_sampling=args.inbatch_sampling,
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,
...
## Step 55 · agent · 2026-09-29T05:53:48.512Z
### read_file · 2026-09-29T05:53:48.512Z
{
"end_line": 180,
"start_line": 150,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:53:48.523Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 150-180 of 2224 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: 181.
--- FILE CONTENT (truncated) ---
torch.manual_seed(seed)
def model_config(config: Config) -> base.TowerConfig:
"""Map the dense trainer configuration onto the unchanged tower."""
return base.TowerConfig(
sequence_length=config.bptt_steps,
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim,
user_hidden_1=config.user_hidden_1,
user_hidden_2=config.user_hidden_2,
item_hidden_1=config.item_hidden_1,
item_hidden_2=config.item_hidden_2,
dropout=config.dropout,
epochs=config.epochs,
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
gradient_clip=config.gradient_clip,
uniform_negatives=config.uniform_negatives,
inbatch_negatives=config.inbatch_negatives,
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
seed=config.seed,
)
def append_state_snapshot(
active_likes: dict[int, int],
active_dislikes: dict[int, int],
n_items: int,
## Step 56 · agent · 2026-09-29T05:53:51.828Z
### Recorded reasoning summary
Testing Configuration Propagation
I'm verifying that `model_config` correctly passes `uniform_negatives` and `inbatch_negatives` from the main `config`. My next step is a syntax check with `--prepare-only` to confirm smooth execution and catch any parsing errors early.
### run_shell_command · 2026-09-29T05:53:51.828Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --output /app/solution/dummy.parquet \\\n --prepare-only",
"description": "Verify syntax and cache loading with prepare-only"
}
### Tool output · 2026-09-29T05:53:56.750Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
{
"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": 0.569,
"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",
"loss_type": "bce",
"temperature": 0.1,
"inbatch_sampling": "unique",
"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: 1889
## Step 57 · agent · 2026-09-29T05:54:23.950Z
### Recorded reasoning summary
Generating Initial Submission
My thoughts are now focused on generating the initial valid submission file. After a swift cache load and clean config parsing, I'm preparing to save the best valid submission, adhering to the core mandate for this attempt.
### Recorded reasoning summary
Testing Submission Strategies
My current thinking is exploring submission strategies, specifically if generating a baseline submission now via `--stage submission` is feasible, though it would take about six minutes. I'm weighing this against running an initial validation experiment first, particularly to see if early improvements in NDCG@10 are possible with InfoNCE variants.
### Recorded reasoning summary
Refining Loss Calculation
I'm currently examining the loss calculation, specifically the difference in normalization between the baseline BCE and the InfoNCE approach. The absence of a division by 2.0 in the InfoNCE loss calculation, coupled with its inherent logarithmic nature, necessitates careful consideration of the resulting loss values and their impact on training dynamics.
### Recorded reasoning summary
Analyzing Gradient Magnitudes
I'm currently analyzing the gradient magnitudes resulting from the InfoNCE loss. With a temperature ($\tau$) of 0.1, the scaling factor becomes 10, which is then constrained by the gradient clip of 2.0. I'm investigating typical temperatures for two-tower retrieval with raw dot products to contextualize these values.
### Recorded reasoning summary
Investigating Temperature Effects
I'm currently exploring the impact of the temperature parameter ($\tau$) on softmax saturation. With a difference of 13.6 and $\tau=0.1$, softmax will quickly saturate. I'm testing $\tau=0.2$ and $\tau=0.5$, considering that for non-L2-normalized embeddings, temperatures typically range from 0.2 to 1.0. I need to verify if embedding normalization is permissible.
We will run training on the validation stage using the InfoNCE objective with temperature 0.2 to test if contrastive ranking improves over the baseline BCE loss.
### run_shell_command · 2026-09-29T05:54:23.950Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --output /app/solution/val_infonce_t02.parquet \\\n --loss-type infonce \\\n --temperature 0.2",
"description": "Run validation with InfoNCE loss at tau=0.2"
}
### Tool output · 2026-09-29T05:59:41.765Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=2.834704 running_bce=2.844623 elapsed=198.3s
epoch=1/1 train_loss=2.83319 validation_loss=2.60024 pair_accuracy=0.770 train_targets=1,052,953 validation_targets=130,103 train_seconds=211.5 validation_seconds=16.1
restored best validation-BCE checkpoint: epoch=1 loss=2.600241
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/val_infonce_t02.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 313.347,
"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",
"loss_type": "infonce",
"temperature": 0.2,
"inbatch_sampling": "unique",
"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": 2.833192685520754,
"positive_logit": 0.14591490125550566,
"negative_logit": -0.1324996562509899,
"pair_accuracy": 0.7695968801551446,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 211.50993273500012,
"validation_loss": 2.6002409476253083,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 16.07279114500011
}
],
"embedding_diagnostics": {
"query_norm_mean": 0.8764076232910156,
"query_norm_std": 0.26191794872283936,
"item_norm_mean": 0.8596771359443665,
"item_norm_std": 0.2651780843734741
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1913
## Step 58 · agent · 2026-09-29T06:01:22.619Z
### run_shell_command · 2026-09-29T06:01:22.619Z
{
"command": "python /task/evaluate_public.py /app/solution/val_infonce_t02.parquet",
"description": "Evaluate InfoNCE ranking on validation outcomes"
}
### Tool output · 2026-09-29T06:01:26.002Z
Output: {
"model": "/app/solution/val_infonce_t02.parquet",
"scores": {
"quality_ndcg@10": 0.017,
"quality_users": 4263
}
}
Process Group PGID: 1980
## Step 59 · agent · 2026-09-29T06:05:11.356Z
### Recorded reasoning summary
Analyzing InfoNCE Performance
My current focus is on understanding why InfoNCE at $\tau = 0.2$ resulted in a lower NDCG@10. Preliminary diagnostics reveal a dramatic shrinkage in embedding norms, from 0.876 for queries to 0.859 for items. This suggests the temperature was likely too low.
### Recorded reasoning summary
Investigating BCE Sampling
The recent exploration into BCE revealed that setting `inbatch_correction_power` to 0.0 effectively negates any in-batch negative downweighting, treating all sampled negatives equally regardless of their frequency. This contrasts sharply with the power of 0.5, which significantly amplified weights for rare items.
### Recorded reasoning summary
Examining Discovery Graded Weights
I'm now investigating how `discovery-graded` target weighting interacts with BCE sampling. If popular items aren't downweighted when sampled as negatives, and `discovery-graded` is also active, it might lead to popular items receiving disproportionately high penalties.
### Recorded reasoning summary
Evaluating Discovery Target Weights
I'm currently examining the `discovery-graded` target weighting mechanism. It appears that popular items, if not downweighted as negatives during BCE sampling, could face excessively high penalties when this weighting is also active, leading to an imbalance in their impact on model training.
We will test the impact of setting `--target-weighting discovery-graded` on the validation score.
### run_shell_command · 2026-09-29T06:05:11.356Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --output /app/solution/val_disc_graded.parquet \\\n --target-weighting discovery-graded",
"description": "Run validation with target-weighting discovery-graded"
}
### Tool output · 2026-09-29T06:10:37.040Z
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=197.7s
epoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.8 validation_seconds=16.0
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_disc_graded.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 321.064,
"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",
"loss_type": "bce",
"temperature": 0.1,
"inbatch_sampling": "unique",
"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": 0.5421154219142149,
"positive_logit": 0.7774311359353417,
"negative_logit": -0.6407561918183821,
"pair_accuracy": 0.7602795423917307,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 210.8115309349996,
"validation_loss": 0.5048305735890953,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 16.039344856000298
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7521849870681763,
"query_norm_std": 0.8638167381286621,
"item_norm_mean": 1.8543914556503296,
"item_norm_std": 0.8459439277648926
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 2018
## Step 60 · agent · 2026-09-29T06:22:12.320Z
### run_shell_command · 2026-09-29T06:22:12.320Z
{
"description": "Evaluate discovery-graded ranking on validation outcomes",
"command": "python /task/evaluate_public.py /app/solution/val_disc_graded.parquet"
}
### Tool output · 2026-09-29T06:22:15.745Z
Output: {
"model": "/app/solution/val_disc_graded.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 2150
## Step 61 · agent · 2026-09-29T06:22:44.691Z
### Recorded reasoning summary
Improving NDCG Performance
My analysis shows a promising, though modest, improvement in NDCG@10 with the `--target-weighting discovery-graded` flag. However, I believe a significant leap forward is attainable by addressing the negative sampling strategy and its corresponding objective function, as per the prompt's directive. I'm now turning my attention to the prompt itself to refine this approach.
### Recorded reasoning summary
Refining Negative Sampling
I'm digging into the negative sampling, specifically `draw_full_history_filtered_negatives` and `corrected_negative_loss`. My initial thought is to investigate the impact of setting `inbatch_correction_power` to `0.0`, which would disable propensity downweighting for in-batch negatives.
### Recorded reasoning summary
Exploring In-Batch Sampling
I'm considering a shift in negative sampling, focusing on increasing the proportion of in-batch negatives. The hypothesis is that these locally relevant items offer a stronger training signal than the largely irrelevant uniform negatives, especially when sampled proportionally to frequency.
### Recorded reasoning summary
Investigating Correction Weights
I'm re-examining `corrected_negative_loss`, particularly the line `weights = correction[:, selected]`. This suggests the 'correction' tensor directly influences the weighting of in-batch negatives, which is a crucial detail for understanding their impact.
### Recorded reasoning summary
Simplifying Loss Calculation
I've realized that with `inbatch_correction_power` at 0.0, the `corrected_negative_loss` simplifies to the mean of raw in-batch negative logits. This means the overall loss becomes a simple weighted average of uniform and in-batch negative losses. I'm now questioning the `(positive_loss + negative_loss) / 2.0` averaging in the loss summation.
### Recorded reasoning summary
Revising Loss Averaging
I'm re-evaluating the loss averaging. The current method seems to underweight the impact of negatives significantly, potentially pushing item embeddings in the wrong direction despite their negative occurrences. I'm considering how to adjust this to reflect actual occurrences more accurately.
### Recorded reasoning summary
Rebalancing Gradients
I've identified a critical imbalance in gradient calculations. The negative loss is divided by the number of negatives (32), while the positive loss is not. This disproportionately amplifies positive gradients, leading to unstable learning, especially for popular items. I'm exploring ways to equalize these gradient contributions.
### Recorded reasoning summary
Revising Negative Loss
I'm shifting my focus to the aggregation of negative logits, moving from a mean to a sum. This aligns with an unbiased estimator of the catalog sum and avoids the gradient imbalance I identified. I'm also exploring the Pairwise Ranking Loss (BPR) as a potential alternative due to its inherent gradient symmetry.
### Recorded reasoning summary
Analyzing BPR Gradients
I've discovered that BPR loss offers perfect gradient balance between positives and negatives, addressing the imbalance I previously observed. This symmetry seems highly promising for stable learning and potentially improving item embedding directions.
### Recorded reasoning summary
Implementing BPR Loss
I've confirmed that implementing BPR loss is computationally efficient, taking under a millisecond per batch. This approach directly trains the model to rank positives above negatives, elegantly handling popular negative items with large gradients and eliminating the previous positive/negative gradient imbalance.
### Recorded reasoning summary
Combining BPR with Sampling
I'm now considering a hybrid approach, integrating BPR loss with frequency-based in-batch negative sampling. My thinking is that this combination will allow us to leverage the gradient symmetry of BPR while also utilizing the more locally relevant, frequency-aware signal from in-batch negatives.
### read_file · 2026-09-29T06:22:44.691Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 1200,
"start_line": 1170
}
### Tool output · 2026-09-29T06:22:46.366Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1170-1200 of 2224 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) ---
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
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
if config.loss_type == "infonce":
logits = torch.cat(
[positive_logits.unsqueeze(1), negative_logits], dim=1
) / config.temperature
target_indices = torch.zeros(
len(logits), dtype=torch.long, device=device
)
loss = F.cross_entropy(logits, target_indices, reduction="none")
loss_sum = (selected_weights * loss).sum()
else:
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
## Step 62 · agent · 2026-09-29T06:22:55.471Z
### replace · 2026-09-29T06:22:55.471Z
{
"old_string": " selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_type == \"infonce\":\n logits = torch.cat(\n [positive_logits.unsqueeze(1), negative_logits], dim=1\n ) / config.temperature\n target_indices = torch.zeros(\n len(logits), dtype=torch.long, device=device\n )\n loss = F.cross_entropy(logits, target_indices, reduction=\"none\")\n loss_sum = (selected_weights * 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()",
"new_string": " selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_type == \"bpr\":\n diff = positive_logits.unsqueeze(1) - negative_logits\n loss = F.softplus(-diff).mean(dim=1)\n loss_sum = (selected_weights * loss).sum()\n elif config.loss_type == \"infonce\":\n logits = torch.cat(\n [positive_logits.unsqueeze(1), negative_logits], dim=1\n ) / config.temperature\n target_indices = torch.zeros(\n len(logits), dtype=torch.long, device=device\n )\n loss = F.cross_entropy(logits, target_indices, reduction=\"none\")\n loss_sum = (selected_weights * 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()",
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add BPR pairwise loss to score_target_microbatches",
"allow_multiple": false
}
### Tool output · 2026-09-29T06:22:55.488Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
"bd,bkd->bk", query_vectors, negative_vectors
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
if config.loss_type == "bpr":
diff = positive_logits.unsqueeze(1) - negative_logits
loss = F.softplus(-diff).mean(dim=1)
loss_sum = (selected_weights * loss).sum()
elif config.loss_type == "infonce":
logits = torch.cat(
[positive_logits.unsqueeze(1), negative_logits], dim=1
) / config.temperature
target_indices = torch.zeros(
len(logits), dtype=torch.long, device=device
...
## Step 63 · agent · 2026-09-29T06:23:10.562Z
### read_file · 2026-09-29T06:23:10.562Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1960,
"end_line": 1980
}
### Tool output · 2026-09-29T06:23:10.570Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1960-1980 of 2228 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: 1981.
--- FILE CONTENT (truncated) ---
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(
"--loss-type",
choices=("bce", "infonce"),
default=Config.loss_type,
)
parser.add_argument(
## Step 64 · agent · 2026-09-29T06:23:14.821Z
### replace · 2026-09-29T06:23:14.821Z
{
"instruction": "Add bpr to choices for loss-type",
"file_path": "/app/solution/train_current_model.py",
"allow_multiple": false,
"new_string": " parser.add_argument(\n \"--loss-type\",\n choices=(\"bce\", \"infonce\", \"bpr\"),\n default=Config.loss_type,\n )",
"old_string": " parser.add_argument(\n \"--loss-type\",\n choices=(\"bce\", \"infonce\"),\n default=Config.loss_type,\n )"
}
### Tool output · 2026-09-29T06:23:14.840Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--loss-type",
choices=("bce", "infonce", "bpr"),
default=Config.loss_type,
)
parser.add_argument(
"--temperature",
type=float,
...
## Step 65 · agent · 2026-09-29T06:23:19.470Z
We will test the BPR pairwise loss with frequency-based in-batch negative sampling (16 uniform and 16 frequency-sampled in-batch negatives) and discovery-graded target weighting on the validation stage.
### run_shell_command · 2026-09-29T06:23:19.470Z
{
"description": "Run validation with BPR pairwise loss and frequency in-batch negatives",
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/val_cache.npz \\\n --output /app/solution/val_bpr_freq.parquet \\\n --loss-type bpr \\\n --inbatch-sampling frequency \\\n --uniform-negatives 16 \\\n --inbatch-negatives 16 \\\n --target-weighting discovery-graded"
}
### Tool output · 2026-09-29T06:28:32.850Z
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=0.451739 running_bce=0.512197 elapsed=171.1s
epoch=1/1 train_loss=0.50676 validation_loss=0.40838 pair_accuracy=0.740 train_targets=1,052,953 validation_targets=130,103 train_seconds=187.1 validation_seconds=28.2
restored best validation-BCE checkpoint: epoch=1 loss=0.408383
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_bpr_freq.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 308.905,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 16,
"inbatch_negatives": 16,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"loss_type": "bpr",
"temperature": 0.1,
"inbatch_sampling": "frequency",
"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": 0.5067620258667893,
"positive_logit": 0.7532000539626512,
"negative_logit": -0.33714526996958005,
"pair_accuracy": 0.740087668917796,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 187.05931897200026,
"validation_loss": 0.40838349439827754,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 28.182732993999707
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.477579116821289,
"query_norm_std": 0.48478859663009644,
"item_norm_mean": 1.9223848581314087,
"item_norm_std": 0.8443576097488403
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 2176
## Step 66 · agent · 2026-09-29T06:29:51.793Z
### run_shell_command · 2026-09-29T06:29:51.793Z
{
"command": "python /task/evaluate_public.py /app/solution/val_bpr_freq.parquet",
"description": "Evaluate BPR ranking on validation outcomes"
}
### Tool output · 2026-09-29T06:29:55.212Z
Output: {
"model": "/app/solution/val_bpr_freq.parquet",
"scores": {
"quality_ndcg@10": 0.010182,
"quality_users": 4263
}
}
Process Group PGID: 2244
## Step 67 · agent · 2026-09-29T06:30:21.205Z
### Recorded reasoning summary
Analyzing BPR performance
I'm digging into why BPR underperformed, specifically noting its NDCG@10 of 0.010182. My current focus is on the `negative_logit` values, comparing BPR's -0.33 against the baseline BCE's -0.64. It seems BPR's approach, where negative loss relies solely on $s^+ - s^-$, didn't sufficiently penalize negative logits.
### Recorded reasoning summary
Improving BCE training
My focus has shifted to BCE, analyzing why it performs best and how it can be further improved. I'm examining the impact of `--target-weighting discovery-graded`, which yielded a slight score increase from 0.02128 to 0.023522. I'm now delving into the specifics of BCE's handling of negative samples.
### Recorded reasoning summary
Investigating loss function
I'm examining the `corrected_negative_loss` function, specifically the part where it calculates the sum of losses. I've noticed a key difference in how negative samples are drawn between `draw_full_history_filtered_negatives` and `two_tower_model.py`'s `draw_filtered_mixed_negatives`, which might be affecting the `negative-filter-scope` and overall training.
### Recorded reasoning summary
Exploring negative sampling scope
I'm investigating the impact of the `negative_filter_scope` parameter. The switch from `"full-training-history"` to `"last-100"` in `train_current_model.py` appears to be a critical difference that might explain performance variations. I'm now examining the detailed implications of this change.
### Recorded reasoning summary
Comparing negative sampling logic
I'm comparing the negative sampling mechanisms between `two_tower_model.py` and `train_current_model.py`. It appears `two_tower_model.py` filters based on `context_items` (last 100), while `train_current_model.py` uses `known_positive_packed` from the full history. This difference in filtering scope is likely impacting performance.
### Recorded reasoning summary
Refining negative sampling strategies
I'm now focusing on the exact implementation of `build_known_positive_filter` and how `known_positive_packed` is constructed. The logic of packing positive items, particularly the exclusion of chronological BCE suffixes, seems critical to understanding the differences in negative sampling across models.
### Recorded reasoning summary
Revising positive filter logic
I'm re-examining `build_known_positive_filter`. It seems `prepared.sequence_items`, containing only items played >= 50%, might be missing crucial positive signals like liked or disliked tracks. This exclusion could be inadvertently allowing disliked songs to be sampled as negatives.
### Recorded reasoning summary
Analyzing BCE suffix absence
I'm investigating why the chronological BCE suffix is intentionally absent from the positive filter during training. This exclusion of validation targets seems counterintuitive and could be negatively impacting how I'm learning to predict future positive interactions.
### Recorded reasoning summary
Detecting positive sample leakage
I've discovered a critical issue: validation window songs can indeed be sampled as negatives during training because they are excluded from the positive filter. This is problematic, as the evaluation metric directly considers these same validation songs. I need to address this leakage.
### Recorded reasoning summary
Addressing validation leakage
I've identified a critical flaw where validation window songs are inadvertently used as negatives during training due to their exclusion from the positive filter. This leakage needs immediate correction to ensure accurate evaluation.
### Recorded reasoning summary
Correcting validation leakage
I've found that validation window songs are being treated as negatives during training. This happens because they're excluded from the positive filter. This leakage is a serious issue, so I'm prioritizing its correction to ensure reliable evaluations.
We will test setting inbatch_correction_power to 0.0 to see if removing the propensity downweighting of popular negative items improves the ranking.
## Recorded session usage
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