yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-fixed-r01-3d6fca60
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T05:36:38.143Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- /task
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
Showing up to 200 items (files + folders).
/task/
├───.keep
├───evaluate_public.py
├───ranking_evaluation.py
├───requirements.txt
├───current_model/
│ ├───train_current_model.py
│ └───two_tower_model.py
└───data/
├───candidate_catalog.parquet
├───target_users.parquet
├───test_eligible_candidates.npz
├───test_preference_state.parquet
├───train_dislikes.parquet
├───train_likes.parquet
├───train_listens.parquet
├───train_undislikes.parquet
├───train_unlikes.parquet
├───val_dislikes.parquet
├───val_likes.parquet
├───val_listens.parquet
├───val_undislikes.parquet
├───val_unlikes.parquet
├───validation_eligible_candidates.npz
└───validation_preference_state.parquet
## Step 2 · user · 2026-09-29T05:36:38.187Z
# Improve a production music-discovery two-tower
You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team's next launch gate.
Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.
The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.
## Production Context
The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.
The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.
## What You Have
All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` window. For the final submission, that
validation window becomes observed history, and another 30-minute gap precedes
the hidden seven-day outcome window.
The package includes:
- `/task/current_model/train_current_model.py`: the production training and
ranking script. It can reproduce the public validation ranking and produce a
hidden-test submission after refitting.
- `/task/current_model/two_tower_model.py`: model primitives used by the
production script.
- `/task/evaluate_public.py`: the public validation evaluator for development.
- `/task/data/validation_eligible_candidates.npz` and
`/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for
the validation and hidden-test cutoffs.
- `/task/data/validation_preference_state.parquet` and
`/task/data/test_preference_state.parquet`: active explicit preference state
at the corresponding cutoffs.
- `/task/requirements.txt`: the exact offline Python distribution inventory.
You can reproduce the production public backtest with:
```sh
python /task/current_model/train_current_model.py \
--stage validation \
--public-dir /task \
--output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet
```
## Data
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action came
through a recommendation surface. A listen is a conventional positive at 50%
completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. For model development and selection, train only from `train_*` and score the
subsequent `val_*` outcomes.
2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
Validation candidates exclude all `train_*` encounters. Test candidates exclude
all `train_*` and `val_*` encounters. Future test interactions are hidden and
used only by the separate Harbor verifier.
## Discovery Objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.
## Deliverable
Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.
The ranking must:
- include the columns `uid`, `item_id`, and `rank`; only these columns are
evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
candidate set.
Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model-provider
connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
the deadline.
## Step 3 · agent · 2026-09-29T05:36:43.180Z
### Recorded reasoning summary
Improving Music Discovery Model
I'm currently focused on enhancing our production music-discovery two-tower model. My immediate priority is to adhere strictly to our Core Mandates and operational guidelines, particularly concerning security, integrity, and ensuring all topic updates are correctly called.
### Recorded reasoning summary
Refining Discovery Pipeline
I'm currently examining the specific submission constraints and deliverables for the production music-discovery model. My focus is on ensuring the final output, a ranked list of items per user, precisely matches all required formats and data specifications within the allocated time.
### update_topic · 2026-09-29T05:36:43.180Z
{
"title": "Initial Inspection and Baseline Replication",
"summary": "Starting the investigation of the production music-discovery two-tower model. In this first phase, I will inspect the codebase in `/task`, examine how training and negative sampling are implemented, run the baseline validation backtest to establish the initial NDCG@10 score, and verify the evaluation mechanics.",
"strategic_intent": "Initialize investigation of the production two-tower model, inspect files, and reproduce baseline validation."
}
### Tool output · 2026-09-29T05:36:43.189Z
Current topic: "Initial Inspection and Baseline Replication"
Topic summary: Starting the investigation of the production music-discovery two-tower model. In this first phase, I will inspect the codebase in `/task`, examine how training and negative sampling are implemented, run the baseline validation backtest to establish the initial NDCG@10 score, and verify the evaluation mechanics.
Strategic Intent: Initialize investigation of the production two-tower model, inspect files, and reproduce baseline validation.
## Step 4 · agent · 2026-09-29T05:36:46.299Z
### Recorded reasoning summary
Examining Task Directory
I'm currently reviewing the contents of the `/task` directory. My immediate focus is on understanding the scripts related to evaluation (`evaluate_public.py`, `ranking_evaluation.py`) and the training procedures for the current model (`current_model/train_current_model.py`, `current_model/two_tower`).
### list_directory · 2026-09-29T05:36:46.299Z
{
"dir_path": "/task"
}
### Tool output · 2026-09-29T05:36:46.305Z
Directory listing for /task:
[DIR] current_model
[DIR] data
.keep (0 bytes)
evaluate_public.py (5774 bytes)
ranking_evaluation.py (8265 bytes)
requirements.txt (1295 bytes)
### list_directory · 2026-09-29T05:36:46.299Z
{
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T05:36:46.305Z
Directory /app/solution is empty.
## Step 5 · agent · 2026-09-29T05:36:48.199Z
### read_file · 2026-09-29T05:36:48.199Z
{
"file_path": "/task/evaluate_public.py",
"end_line": 150,
"start_line": 1
}
### Tool output · 2026-09-29T05:36:48.208Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py "
)
### read_file · 2026-09-29T05:36:48.199Z
{
"end_line": 100,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 1
}
### Tool output · 2026-09-29T05:36:48.208Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-100 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: 101.
--- 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
## Step 6 · agent · 2026-09-29T05:36:50.169Z
### read_file · 2026-09-29T05:36:50.169Z
{
"file_path": "/task/current_model/two_tower_model.py",
"start_line": 1,
"end_line": 150
}
### Tool output · 2026-09-29T05:36:50.173Z
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 7 · agent · 2026-09-29T05:36:51.838Z
### read_file · 2026-09-29T05:36:51.838Z
{
"end_line": 300,
"start_line": 151,
"file_path": "/task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T05:36:51.841Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-300 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: 301.
--- FILE CONTENT (truncated) ---
pending = []
pending.append(part)
if pending_uid is not None:
yield pending_uid, _combine_listen_chunks(pending)
def merge_listen_streams(
paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
"""Merge uid-sorted public listen files without materializing them."""
streams = [iter(iter_listen_users(path, item_index)) for path in paths]
heads: list[tuple[int, ListenEvents] | None] = [
next(stream, None) for stream in streams
]
while any(head is not None for head in heads):
uid = min(head[0] for head in heads if head is not None)
parts: list[ListenEvents] = []
for position, head in enumerate(heads):
if head is not None and head[0] == uid:
parts.append(head[1])
heads[position] = next(streams[position], None)
yield uid, _combine_listen_chunks(parts)
def load_explicit_events(
data: Path,
prefixes: tuple[str, ...],
item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
"""Load public explicit actions as chronological state changes."""
specifications = (
("likes", 0, 1, 0),
("unlikes", 0, 0, 1),
("dislikes", 1, 1, 0),
("undislikes", 1, 0, 1),
)
parts: list[pd.DataFrame] = []
for family, channel, active, tie_priority in specifications:
for prefix in prefixes:
frame = pd.read_parquet(
data / f"{prefix}_{family}.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
mapped = item_index.get_indexer(
frame.item_id.to_numpy(dtype=np.int64, copy=False)
)
keep = mapped >= 0
if not np.any(keep):
continue
retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
retained["item"] = mapped[keep].astype(np.int32, copy=False)
retained["channel"] = np.int8(channel)
retained["active"] = np.int8(active)
retained["tie_priority"] = np.int8(tie_priority)
retained["organic_like"] = np.int8(
family == "likes"
) * retained.is_organic.astype(np.int8)
parts.append(retained.drop(columns="is_organic"))
if not parts:
return {}
events = pd.concat(parts, ignore_index=True)
events = events.sort_values(
["uid", "timestamp", "tie_priority"], kind="mergesort"
).reset_index(drop=True)
result: dict[int, ExplicitEvents] = {}
for uid, rows in events.groupby("uid", sort=False):
result[int(uid)] = ExplicitEvents(
timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
items=rows.item.to_numpy(dtype=np.int32, copy=True),
channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
active=rows.active.to_numpy(dtype=np.int8, copy=True),
organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
)
log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
return result
def empty_explicit_events() -> ExplicitEvents:
return ExplicitEvents(
timestamps=np.empty(0, dtype=np.int64),
items=np.empty(0, dtype=np.int32),
channel=np.empty(0, dtype=np.int8),
active=np.empty(0, dtype=np.int8),
organic_like=np.empty(0, dtype=np.int8),
)
def pack_active_items(
active: dict[int, int], width: int, padding_item: int
) -> np.ndarray:
packed = np.full(width, padding_item, dtype=np.uint16)
if not active:
return packed
ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
packed[: len(ordered)] = np.fromiter(
(item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
)
return packed
def load_inference_state(
path: Path,
item_index: pd.Index,
uids: np.ndarray,
n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Pack the task-provided active explicit state."""
state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
keep = mapped >= 0
state = state.loc[keep, ["uid", "state"]].copy()
state["item"] = mapped[keep].astype(np.int32, copy=False)
user_rows = pd.Index(uids)
liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
like_counts = np.zeros(len(uids), dtype=np.int32)
dislike_counts = np.zeros(len(uids), dtype=np.int32)
for value, width, destination, counts in (
("liked", LIKE_ITEMS, liked, like_counts),
("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
):
selected = state[state.state == value]
for uid, rows in selected.groupby("uid", sort=False):
row = int(user_rows.get_indexer([int(uid)])[0])
if row < 0:
continue
items = np.sort(rows.item.unique())
counts[row] = len(items)
retained = items[:width]
destination[row, : len(retained)] = retained.astype(np.uint16)
return liked, disliked, like_counts, dislike_counts
class SequentialTwoTower(nn.Module):
"""Order-sensitive user tower and independent catalog-aware item tower."""
def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
super().__init__()
n_items, n_item_features = item_features.shape
dim = config.embedding_dim
self.n_items = n_items
self.context_embedding = nn.Embedding(
n_items + 1, dim, padding_idx=n_items
)
self.event_projection = nn.Sequential(
nn.Linear(dim + 3, config.hidden_dim),
nn.GELU(),
)
self.sequence_encoder = nn.GRU(
input_size=config.hidden_dim,
## Step 8 · agent · 2026-09-29T05:36:59.775Z
### read_file · 2026-09-29T05:36:59.775Z
{
"file_path": "/task/current_model/two_tower_model.py",
"start_line": 301,
"end_line": 434
}
### Tool output · 2026-09-29T05:36:59.779Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 301-434 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) ---
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 9 · agent · 2026-09-29T05:37:05.797Z
### read_file · 2026-09-29T05:37:05.797Z
{
"end_line": 300,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 101
}
### Tool output · 2026-09-29T05:37:05.802Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 101-300 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 301.
--- FILE CONTENT (truncated) ---
item_features: np.ndarray
user_ids: np.ndarray
offsets: np.ndarray
sequence_timestamps: np.ndarray
sequence_items: np.ndarray
event_features: np.ndarray
activity_features: np.ndarray
state_ids: np.ndarray
liked_states: np.ndarray
disliked_states: np.ndarray
state_counts: np.ndarray
inference_user_rows: np.ndarray
inference_liked_items: np.ndarray
inference_disliked_items: np.ndarray
inference_user_features: np.ndarray
@property
def lengths(self) -> np.ndarray:
return np.diff(self.offsets)
@property
def target_count(self) -> int:
return int(np.maximum(self.lengths - 1, 0).sum())
@dataclass(frozen=True)
class TargetPartition:
"""A contiguous recent target window and its chronological BCE split."""
window_starts: np.ndarray
training_counts: np.ndarray
validation_counts: np.ndarray
history_training_counts: np.ndarray
@property
def selected_count(self) -> int:
return int(self.training_counts.sum() + self.validation_counts.sum())
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def model_config(config: Config) -> base.TowerConfig:
"""Map the dense trainer configuration onto the unchanged tower."""
return base.TowerConfig(
sequence_length=config.bptt_steps,
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim,
user_hidden_1=config.user_hidden_1,
user_hidden_2=config.user_hidden_2,
item_hidden_1=config.item_hidden_1,
item_hidden_2=config.item_hidden_2,
dropout=config.dropout,
epochs=config.epochs,
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
gradient_clip=config.gradient_clip,
uniform_negatives=config.uniform_negatives,
inbatch_negatives=config.inbatch_negatives,
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
seed=config.seed,
)
def append_state_snapshot(
active_likes: dict[int, int],
active_dislikes: dict[int, int],
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> int:
liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))
disliked_states.append(
base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
)
state_counts.append((len(active_likes), len(active_dislikes)))
return len(liked_states) - 1
def causal_activity_features(
timestamps: np.ndarray,
items: np.ndarray,
organic: np.ndarray,
completion: np.ndarray,
) -> np.ndarray:
"""Compute the first four reviewed user features at every event cutoff."""
length = len(items)
result = np.zeros((length, 4), dtype=np.float16)
if not length:
return result
left_rows = np.searchsorted(
timestamps,
timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
rows = np.arange(length, dtype=np.int64)
counts = rows - left_rows + 1
organic_sum = np.cumsum(organic.astype(np.float64))
completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
before_organic = np.where(
left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
)
before_completion = np.where(
left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
)
unique_counts = np.empty(length, dtype=np.int32)
frequencies: dict[int, int] = {}
left = 0
for right, raw_item in enumerate(items):
item = int(raw_item)
frequencies[item] = frequencies.get(item, 0) + 1
required_left = int(left_rows[right])
while left < required_left:
old_item = int(items[left])
remaining = frequencies[old_item] - 1
if remaining:
frequencies[old_item] = remaining
else:
del frequencies[old_item]
left += 1
unique_counts[right] = len(frequencies)
result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
result[:, 2] = (
(organic_sum - before_organic) / np.maximum(counts, 1)
).astype(np.float16)
result[:, 3] = (
(completion_sum - before_completion)
/ np.maximum(counts, 1)
/ 100.0
).astype(np.float16)
return result
def build_sequence_parts(
listens: base.ListenEvents,
explicit: base.ExplicitEvents,
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
items = listens.items[qualifying].astype(np.uint16, copy=False)
organic = listens.organic[qualifying].astype(np.float32, copy=False)
completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
if not len(items):
return None
event_features = np.zeros((len(items), 3), dtype=np.float16)
event_features[:, 0] = organic.astype(np.float16)
event_features[:, 1] = (
np.minimum(completion, 100.0) / 100.0
).astype(np.float16)
if len(items) > 1:
gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
event_features[1:, 2] = np.exp(
-gaps.astype(np.float64) / (60.0 * base.DAY)
).astype(np.float16)
activity = causal_activity_features(
timestamps, items, organic, completion
)
states = np.zeros(len(items), dtype=np.uint32)
active_likes: dict[int, int] = {}
active_dislikes: dict[int, int] = {}
explicit_position = 0
current_state = 0
for row, timestamp in enumerate(timestamps):
changed = False
while (
explicit_position < len(explicit.timestamps)
and int(explicit.timestamps[explicit_position]) < int(timestamp)
):
item = int(explicit.items[explicit_position])
event_timestamp = int(explicit.timestamps[explicit_position])
destination = (
active_likes
if int(explicit.channel[explicit_position]) == 0
else active_dislikes
)
if int(explicit.active[explicit_position]) == 1:
destination[item] = event_timestamp
else:
destination.pop(item, None)
explicit_position += 1
changed = True
## Step 10 · agent · 2026-09-29T05:37:07.999Z
### read_file · 2026-09-29T05:37:07.999Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 301,
"end_line": 600
}
### Tool output · 2026-09-29T05:37:08.003Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 301-600 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 601.
--- FILE CONTENT (truncated) ---
if changed:
current_state = append_state_snapshot(
active_likes,
active_dislikes,
n_items,
liked_states,
disliked_states,
state_counts,
)
states[row] = np.uint32(current_state)
return timestamps, items.copy(), event_features, activity, states
def final_user_features(
timestamps: np.ndarray,
items: np.ndarray,
event_features: np.ndarray,
history_end: int,
like_count: int,
dislike_count: int,
) -> np.ndarray:
start = int(
np.searchsorted(
timestamps,
history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
side="left",
)
)
result = np.zeros(6, dtype=np.float32)
recent_items = items[start:]
if len(recent_items):
result[0] = np.log1p(len(recent_items)) / 8.0
result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
result[2] = float(np.mean(event_features[start:, 0]))
result[3] = float(np.mean(event_features[start:, 1]))
result[4] = np.log1p(like_count) / 6.0
result[5] = np.log1p(dislike_count) / 4.0
return result
def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
if n_items >= np.iinfo(np.uint16).max:
raise RuntimeError("uint16 item storage is too small for this catalog")
if stage == "validation":
prefixes = ("train",)
history_end = base.TRAIN_END
candidate_name = "validation_eligible_candidates.npz"
state_name = "validation_preference_state.parquet"
else:
prefixes = ("train", "val")
history_end = base.VALIDATION_END
candidate_name = "test_eligible_candidates.npz"
state_name = "test_preference_state.parquet"
eligibility = CandidateSet.read(data / candidate_name)
if not np.array_equal(eligibility.item_ids, item_ids):
raise RuntimeError("catalog and candidate artifact item order differ")
explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
inference_likes, inference_dislikes, like_counts, dislike_counts = (
base.load_inference_state(
data / state_name,
item_index,
eligibility.uids,
n_items,
)
)
inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
# Snapshot zero is the shared empty explicit state.
liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
disliked_states = [
np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
]
state_counts: list[tuple[int, int]] = [(0, 0)]
user_ids: list[int] = []
offsets = [0]
timestamp_parts: list[np.ndarray] = []
item_parts: list[np.ndarray] = []
event_parts: list[np.ndarray] = []
activity_parts: list[np.ndarray] = []
state_parts: list[np.ndarray] = []
listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
started = time.monotonic()
for seen_users, (uid, listens) in enumerate(
base.merge_listen_streams(listen_paths, item_index), start=1
):
parts = build_sequence_parts(
listens,
explicit_by_user.get(uid, base.empty_explicit_events()),
n_items,
liked_states,
disliked_states,
state_counts,
)
if parts is None:
continue
sequence_row = len(user_ids)
user_ids.append(uid)
timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
item_parts.append(parts[1])
event_parts.append(parts[2])
activity_parts.append(parts[3])
state_parts.append(parts[4])
offsets.append(offsets[-1] + len(parts[1]))
inference_row = inference_rows.get(uid)
if inference_row is not None:
inference_sequence_rows[inference_row] = sequence_row
inference_user_features[inference_row] = final_user_features(
parts[0],
parts[1],
parts[2],
history_end,
int(like_counts[inference_row]),
int(dislike_counts[inference_row]),
)
if seen_users % 1_000 == 0:
log(
f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
f"states={len(liked_states):,} "
f"elapsed={time.monotonic() - started:.1f}s"
)
missing = np.flatnonzero(inference_sequence_rows < 0)
if len(missing):
inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
inference_user_features[missing, 5] = (
np.log1p(dislike_counts[missing]) / 4.0
)
log(f"target users without qualifying catalog sequence={len(missing):,}")
prepared = SequenceData(
item_ids=item_ids,
item_features=base.build_item_features(catalog),
user_ids=np.asarray(user_ids, dtype=np.int64),
offsets=np.asarray(offsets, dtype=np.int64),
sequence_timestamps=np.concatenate(timestamp_parts),
sequence_items=np.concatenate(item_parts),
event_features=np.concatenate(event_parts),
activity_features=np.concatenate(activity_parts),
state_ids=np.concatenate(state_parts),
liked_states=np.stack(liked_states),
disliked_states=np.stack(disliked_states),
state_counts=np.asarray(state_counts, dtype=np.int32),
inference_user_rows=inference_sequence_rows,
inference_liked_items=inference_likes,
inference_disliked_items=inference_dislikes,
inference_user_features=inference_user_features,
)
log(
f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
f"tokens={len(prepared.sequence_items):,} "
f"next_item_targets={prepared.target_count:,} "
f"states={len(prepared.liked_states):,}"
)
return prepared, eligibility
def cache_metadata(stage: str, config: Config) -> dict[str, object]:
return {
"cache_version": CACHE_VERSION,
"stage": stage,
"positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
"profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
"event_time_feature": "exp-negative-gap-over-60-days",
}
def save_cache(
path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
np.savez(
path,
metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
**prepared.__dict__,
)
log(f"saved public-derived full-sequence cache: {path}")
def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
with np.load(path, allow_pickle=False) as artifact:
actual = json.loads(str(artifact["metadata"].item()))
expected = cache_metadata(stage, config)
if actual != expected:
raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
values = {
field: artifact[field].copy()
for field in SequenceData.__dataclass_fields__
}
prepared = SequenceData(**values)
log(
f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
f"targets={prepared.target_count:,}"
)
return prepared
def load_or_prepare(
public: Path,
stage: str,
config: Config,
cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
candidate_name = (
"validation_eligible_candidates.npz"
if stage == "validation"
else "test_eligible_candidates.npz"
)
eligibility = CandidateSet.read(public / "data" / candidate_name)
if cache is not None and cache.is_file():
prepared = load_cache(cache, stage, config)
if not np.array_equal(prepared.item_ids, eligibility.item_ids):
raise RuntimeError("cached catalog differs from candidate artifact")
return prepared, eligibility
prepared, built_eligibility = prepare_data(public, stage, config)
if cache is not None:
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""Estimate in-batch propensities from the selected training targets."""
counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
for user_row, count in enumerate(target_counts):
if not count:
continue
offset = int(prepared.offsets[user_row])
target_start = int(target_starts[user_row])
counts += np.bincount(
prepared.sequence_items[
offset
+ target_start
+ 1 : offset
+ target_start
+ int(count)
+ 1
].astype(np.int64),
minlength=len(prepared.item_ids),
)
total = float(counts.sum())
probabilities = (counts + 1.0) / (total + len(counts))
raw = np.power(
(1.0 / len(counts)) / probabilities,
config.inbatch_correction_power,
)
normalization = float(np.sum(counts * raw) / max(total, 1.0))
correction = np.clip(
raw / max(normalization, 1.0e-12),
config.inbatch_correction_min,
config.inbatch_correction_max,
).astype(np.float32)
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
log(
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
)
return torch.from_numpy(correction)
def build_known_positive_filter(
prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
"""Pack each user's complete public-training positive-item set."""
width = (len(prepared.item_ids) + 7) // 8
packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
retained = 0
for user_row, target_count in enumerate(training_counts):
if not target_count:
continue
offset = int(prepared.offsets[user_row])
# T adjacent targets consume sequence positions 0 through T. The
# chronological BCE suffix starts at T + 1 and is intentionally absent.
items = np.unique(
prepared.sequence_items[
offset : offset + int(target_count) + 1
].astype(np.int64)
)
byte_columns = items >> 3
bit_values = np.left_shift(
np.uint8(1), (items & 7).astype(np.uint8)
)
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
## Step 11 · agent · 2026-09-29T05:37:09.850Z
### read_file · 2026-09-29T05:37:09.850Z
{
"file_path": "/task/current_model/train_current_model.py",
"start_line": 601,
"end_line": 900
}
### Tool output · 2026-09-29T05:37:09.854Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 601-900 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: 901.
--- FILE CONTENT (truncated) ---
retained += len(items)
log(
f"full-history false-negative filter: users={len(prepared.user_ids):,} "
f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
)
return packed
def invalid_full_history_negatives(
negatives: np.ndarray,
targets: np.ndarray,
user_rows: np.ndarray,
known_positive_packed: np.ndarray,
liked_items: np.ndarray,
) -> np.ndarray:
"""Return row-level invalid negatives using the complete training prefix."""
byte_columns = negatives >> 3
bit_columns = negatives & 7
known = known_positive_packed[user_rows[:, None], byte_columns]
invalid = ((known >> bit_columns) & 1).astype(bool)
invalid |= negatives == targets[:, None]
invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= (
negatives[:, column, None] == negatives[:, :column]
).any(axis=1)
return invalid
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
def draw_training_negatives(
positive_items: torch.Tensor,
known_history: torch.Tensor,
liked_items: torch.Tensor,
user_rows: np.ndarray,
known_positive_packed: np.ndarray | None,
prepared: SequenceData,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
if config.negative_filter_scope == "full-training-history":
if known_positive_packed is None:
raise RuntimeError("full-history negative filter was not built")
return draw_full_history_filtered_negatives(
positive_items,
user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
config,
item_correction,
generator,
device,
)
return base.draw_filtered_mixed_negatives(
positive_items,
known_history,
liked_items,
len(prepared.item_ids),
config.uniform_negatives,
config.inbatch_negatives,
item_correction,
generator,
device,
)
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)
## Step 12 · agent · 2026-09-29T05:37:11.631Z
### read_file · 2026-09-29T05:37:11.631Z
{
"start_line": 901,
"end_line": 1200,
"file_path": "/task/current_model/train_current_model.py"
}
### Tool output · 2026-09-29T05:37:11.635Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 901-1200 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1201.
--- FILE CONTENT (truncated) ---
history_training = totals - validation
training = history_training.copy()
if max_train_targets_per_user:
training = np.minimum(training, max_train_targets_per_user)
window_starts = retained_target_window_starts(totals, validation, training)
return TargetPartition(
window_starts=window_starts,
training_counts=training,
validation_counts=validation,
history_training_counts=history_training,
)
def load_organic_like_outcomes(
public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
"""Load mapped organic-like timestamps from public fitting history."""
prefixes = ("train",) if stage == "validation" else ("train", "val")
frames = [
pd.read_parquet(
public / "data" / f"{prefix}_likes.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
for prefix in prefixes
]
likes = pd.concat(frames, ignore_index=True)
likes = likes[likes.is_organic == 1].copy()
mapped = pd.Index(item_ids).get_indexer(
likes.item_id.to_numpy(dtype=np.int64)
)
likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
nested: dict[int, dict[int, list[int]]] = {}
for raw_uid, raw_timestamp, raw_item in likes.itertuples(
index=False, name=None
):
nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
int(raw_timestamp)
)
result = {
uid: {
item: np.asarray(timestamps, dtype=np.int64)
for item, timestamps in items.items()
}
for uid, items in nested.items()
}
log(
f"loaded organic-like outcomes: rows={len(likes):,} "
f"users={len(result):,}"
)
return result
def build_target_weights(
public: Path,
stage: str,
prepared: SequenceData,
training_counts: np.ndarray,
config: Config,
) -> np.ndarray:
"""Build nonzero per-token weights without dropping adjacent targets."""
if config.target_weighting == "uniform":
weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
else:
weights = np.full(
len(prepared.sequence_items),
config.ordinary_target_weight,
dtype=np.float32,
)
organic_strong = (
(prepared.event_features[:, 0] > 0.5)
& (prepared.event_features[:, 1] >= 0.8)
)
weights[organic_strong] = config.organic_strong_target_weight
if config.target_weighting == "discovery-graded":
like_outcomes = load_organic_like_outcomes(
public, stage, prepared.item_ids
)
history_end = (
base.TRAIN_END if stage == "validation" else base.VALIDATION_END
)
horizon = config.discovery_horizon_days * base.DAY
grade_three = 0
for user_row, uid in enumerate(prepared.user_ids):
start = int(prepared.offsets[user_row])
end = int(prepared.offsets[user_row + 1])
if end - start < 2:
continue
last_training_target = start + int(training_counts[user_row])
first_validation_target = last_training_target + 1
split_timestamp = (
int(prepared.sequence_timestamps[first_validation_target])
if first_validation_target < end
else history_end
)
user_likes = like_outcomes.get(int(uid), {})
seen = {int(prepared.sequence_items[start])}
for position in range(start + 1, end):
item = int(prepared.sequence_items[position])
novel = item not in seen
seen.add(item)
if not novel:
# Repeats remain training examples, but they cannot be
# eligible discovery outcomes at the corresponding
# cutoff and therefore keep only the ordinary weight.
weights[position] = config.ordinary_target_weight
continue
timestamps = user_likes.get(item)
if timestamps is None:
continue
timestamp = int(prepared.sequence_timestamps[position])
upper = min(
timestamp + horizon,
split_timestamp
if position <= last_training_target
else history_end,
)
outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
weights[position] = config.organic_like_target_weight
grade_three += 1
log(
f"grade-3 first-encounter targets={grade_three:,} "
f"horizon_days={config.discovery_horizon_days}"
)
log(
f"target weighting={config.target_weighting} "
f"min={weights.min():.3f} mean={weights.mean():.3f} "
f"max={weights.max():.3f}"
)
return weights
def build_dense_block(
prepared: SequenceData,
target_weights: np.ndarray,
users: np.ndarray,
window_starts: np.ndarray,
process_counts: np.ndarray,
score_starts: np.ndarray,
start: int,
steps: int,
config: Config,
) -> tuple[np.ndarray, ...]:
"""Build one causal sequence block and mark the target rows to score."""
batch_size = len(users)
padding_item = len(prepared.item_ids)
input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
targets = np.zeros((batch_size, steps), dtype=np.uint16)
weights = np.ones((batch_size, steps), dtype=np.float32)
scored = np.zeros((batch_size, steps), dtype=bool)
histories = np.full(
(batch_size, steps, config.bptt_steps),
padding_item,
dtype=np.uint16,
)
processed = np.zeros(batch_size, dtype=np.int32)
lengths = prepared.lengths
for row, user_row in enumerate(users):
count = min(steps, max(int(process_counts[row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[user_row])
window_start = int(window_starts[row])
absolute_start = window_start + start
positions = slice(
offset + absolute_start,
offset + absolute_start + count,
)
target_positions = slice(
offset + absolute_start + 1,
offset + absolute_start + count + 1,
)
input_items[row, :count] = prepared.sequence_items[positions]
event_features[row, :count] = prepared.event_features[positions]
activity_features[row, :count] = prepared.activity_features[positions]
state_ids[row, :count] = prepared.state_ids[positions]
targets[row, :count] = prepared.sequence_items[target_positions]
weights[row, :count] = target_weights[target_positions]
score_from = min(count, max(int(score_starts[row]) - start, 0))
scored[row, score_from:count] = True
processed[row] = count
user_sequence = prepared.sequence_items[
offset : offset + int(lengths[user_row])
]
histories[row, :count] = history_windows(
user_sequence,
absolute_start,
count,
config.bptt_steps,
padding_item,
)
return (
input_items,
event_features,
activity_features,
state_ids,
targets,
scored,
histories,
processed,
weights,
)
def score_target_microbatches(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
sequence_vectors: torch.Tensor,
flat_states: np.ndarray,
flat_activity: np.ndarray,
positive_items: torch.Tensor,
negatives: torch.Tensor,
negative_weights: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
"""Compute one logical block's exact BCE in bounded-memory slices."""
target_count = len(positive_items)
total_weight = float(sample_weights.sum())
if total_weight <= 0:
raise RuntimeError("target block has no positive sample weight")
sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
statistics = {
"loss_sum": 0.0,
"positive_sum": 0.0,
"negative_sum": 0.0,
"pair_correct": 0.0,
"weight_sum": 0.0,
}
for begin in range(0, target_count, config.loss_microbatch_size):
end = min(begin + config.loss_microbatch_size, target_count)
selected_states = flat_states[begin:end]
if backward:
sequence_input = (
sequence_vectors[begin:end].detach().requires_grad_(True)
)
else:
sequence_input = sequence_vectors[begin:end]
liked_vectors, disliked_vectors, _ = pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = candidate_vectors(
model,
positive_items[begin:end],
negatives[begin:end],
)
positive_logits = (query_vectors * positive_vectors).sum(dim=1)
negative_logits = torch.einsum(
"bd,bkd->bk", query_vectors, negative_vectors
)
positive_loss = F.softplus(-positive_logits)
negative_loss = corrected_negative_loss(
negative_logits, negative_weights[begin:end], config
)
selected_weights = torch.from_numpy(
sample_weights[begin:end].astype(np.float32, copy=False)
).to(device)
loss_sum = (
selected_weights * (positive_loss + negative_loss) / 2.0
).sum()
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("dense next-item training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def release_accelerator_cache(device: torch.device) -> None:
if device.type == "mps":
torch.mps.empty_cache()
## Step 13 · agent · 2026-09-29T05:37:13.675Z
### read_file · 2026-09-29T05:37:13.675Z
{
"file_path": "/task/current_model/train_current_model.py",
"end_line": 1500,
"start_line": 1201
}
### Tool output · 2026-09-29T05:37:13.678Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1201-1500 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: 1501.
--- FILE CONTENT (truncated) ---
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),
"loss": total_loss / max(total_weight, 1.0e-12),
"positive_logit": total_positive / max(total_targets, 1),
"negative_logit": total_negative / max(total_targets * negative_count, 1),
"pair_accuracy": total_pair_correct
/ max(total_targets * negative_count, 1),
"targets": float(total_targets),
"target_weight_sum": total_weight,
"batches": float(batches),
"seconds": time.monotonic() - started,
}
@torch.no_grad()
def evaluate_validation_bce(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
np.argsort(totals[users_with_validation], kind="stable")
]
groups = [
ordered[start : start + config.user_batch_size]
for start in range(0, len(ordered), config.user_batch_size)
]
generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
total_loss = 0.0
total_targets = 0
total_weight = 0.0
started = time.monotonic()
for group_number, users in enumerate(groups, start=1):
group_totals = totals[users]
group_starts = training_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_totals.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_totals,
group_starts,
start,
steps,
config,
)
sequence_outputs, _ = event_sequence(
model,
torch.from_numpy(block[0].astype(np.int64)).to(device),
torch.from_numpy(block[1].astype(np.float32)).to(device),
hidden,
)
hidden = torch.stack(
[
sequence_outputs[row, count - 1]
if count
else hidden[0, row]
for row, count in enumerate(block[7])
]
).unsqueeze(0)
flat_scored = block[5].reshape(-1)
if not np.any(flat_scored):
continue
mask = torch.from_numpy(flat_scored).to(device)
sequence_vectors = sequence_outputs.reshape(
-1, config.embedding_dim
)[mask]
flat_states = block[3].reshape(-1)[flat_scored]
positive_items = torch.from_numpy(
block[4].reshape(-1)[flat_scored].astype(np.int64)
).to(device)
known_history = torch.from_numpy(
block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
np.int64
)
).to(device)
liked_items = torch.from_numpy(
prepared.liked_states[flat_states].astype(np.int64)
).to(device)
flat_user_rows = np.broadcast_to(
users[:, None], block[5].shape
).reshape(-1)[flat_scored]
negatives, negative_weights = draw_training_negatives(
positive_items,
known_history,
liked_items,
flat_user_rows,
known_positive_packed,
prepared,
config,
item_correction,
generator,
device,
)
statistics, _ = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=False,
)
total_loss += statistics["loss_sum"]
total_targets += len(positive_items)
total_weight += statistics["weight_sum"]
if group_number % 25 == 0:
release_accelerator_cache(device)
return {
"validation_loss": total_loss / max(total_weight, 1.0e-12),
"validation_targets": float(total_targets),
"validation_target_weight_sum": total_weight,
"validation_seconds": time.monotonic() - started,
}
def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
return {
name: value.detach().cpu().clone()
for name, value in model.state_dict().items()
}
def save_epoch_checkpoint(
checkpoint_dir: Path,
epoch: int,
model_state: dict[str, torch.Tensor],
config: Config,
diagnostics: dict[str, float],
## Step 14 · agent · 2026-09-29T05:37:15.290Z
### read_file · 2026-09-29T05:37:15.290Z
{
"file_path": "/task/current_model/train_current_model.py",
"end_line": 1800,
"start_line": 1501
}
### Tool output · 2026-09-29T05:37:15.294Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1501-1800 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: 1801.
--- FILE CONTENT (truncated) ---
optimizer: torch.optim.Optimizer,
generator: torch.Generator,
) -> Path:
checkpoint_dir.mkdir(parents=True, exist_ok=True)
path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": model_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
path,
)
log(f"saved epoch checkpoint: {path}")
return path
def train_model(
public: Path,
stage: str,
prepared: SequenceData,
config: Config,
device: torch.device,
checkpoint_dir: Path | None,
resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
partition = chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
training_counts = partition.training_counts
validation_counts = partition.validation_counts
log(
f"chronological BCE window: train={int(training_counts.sum()):,} "
f"validation={int(validation_counts.sum()):,} "
f"selected={partition.selected_count:,} "
f"discarded_old_targets={int(partition.window_starts.sum()):,} "
f"all={prepared.target_count:,}"
)
model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
device
)
optimizer = torch.optim.AdamW(
model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
)
generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
item_correction = build_item_correction(
prepared,
config,
partition.window_starts,
training_counts,
)
target_weights = build_target_weights(
public,
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
"negative_filter_scope", "last-100"
)
stored_target_weighting = stored_config.get(
"target_weighting", "uniform"
)
stored_weight_values = (
stored_config.get("ordinary_target_weight", 1.0),
stored_config.get("organic_strong_target_weight", 1.0),
stored_config.get("organic_like_target_weight", 3.0),
stored_config.get("discovery_horizon_days", 7),
)
current_weight_values = (
config.ordinary_target_weight,
config.organic_strong_target_weight,
config.organic_like_target_weight,
config.discovery_horizon_days,
)
stored_window_values = (
stored_config.get("validation_fraction", 0.10),
stored_config.get("max_train_targets_per_user", 0),
stored_config.get("max_validation_targets_per_user", 0),
)
current_window_values = (
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
objective_changed = (
stored_filter_scope != config.negative_filter_scope
or stored_target_weighting != config.target_weighting
or stored_weight_values != current_weight_values
or stored_window_values != current_window_values
)
model.load_state_dict(payload["model_state"])
if "optimizer_state" in payload and not objective_changed:
optimizer.load_state_dict(payload["optimizer_state"])
for group in optimizer.param_groups:
group["lr"] = config.learning_rate
group["weight_decay"] = config.weight_decay
log(f"restored optimizer state from {resume_checkpoint}")
else:
log(
f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
)
if "generator_state" in payload and not objective_changed:
generator.set_state(payload["generator_state"])
start_epoch = int(payload["epoch"])
if start_epoch >= config.epochs:
raise RuntimeError(
f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
)
prior = {
str(name): float(value)
for name, value in payload.get("diagnostics", {}).items()
}
if objective_changed:
prior.pop("validation_loss", None)
log(
"BCE objective changed: filter "
f"{stored_filter_scope}->{config.negative_filter_scope}, "
"target weighting "
f"{stored_target_weighting}->{config.target_weighting}, "
f"target window {stored_window_values}->{current_window_values}; "
"recomputing the resume checkpoint's validation BCE"
)
if "validation_loss" not in prior:
prior.update(
evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
)
history.append(prior)
best_loss = float(prior["validation_loss"])
best_epoch = start_epoch
best_state = snapshot_model(model)
log(
f"resumed validation-BCE training: epoch={start_epoch} "
f"loss={best_loss:.6f}"
)
for epoch in range(start_epoch + 1, config.epochs + 1):
diagnostics = run_training_epoch(
model,
optimizer,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
item_correction,
known_positive_packed,
generator,
device,
epoch,
)
validation = evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
diagnostics.update(validation)
history.append(diagnostics)
current_state = snapshot_model(model)
if checkpoint_dir is not None:
save_epoch_checkpoint(
checkpoint_dir,
epoch,
current_state,
config,
diagnostics,
optimizer,
generator,
)
improved = (
diagnostics["validation_loss"]
< best_loss - config.early_stopping_min_delta
)
if improved:
best_loss = diagnostics["validation_loss"]
best_epoch = epoch
best_state = current_state
epochs_without_improvement = 0
if checkpoint_dir is not None:
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": current_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
checkpoint_dir / "best.pt",
)
else:
epochs_without_improvement += 1
log(
f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
f"validation_loss={diagnostics['validation_loss']:.5f} "
f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
f"train_targets={int(diagnostics['targets']):,} "
f"validation_targets={int(diagnostics['validation_targets']):,} "
f"train_seconds={diagnostics['seconds']:.1f} "
f"validation_seconds={diagnostics['validation_seconds']:.1f}"
)
release_accelerator_cache(device)
if epochs_without_improvement >= config.early_stopping_patience:
log(
f"early stopping after epoch={epoch}; "
f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
)
break
if best_state is None:
raise RuntimeError("training did not produce a valid checkpoint")
model.load_state_dict(best_state)
model.to(device).eval()
log(
f"restored best validation-BCE checkpoint: epoch={best_epoch} "
f"loss={best_loss:.6f}"
)
return model, history
@torch.inference_mode()
def inference_queries(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
device: torch.device,
) -> torch.Tensor:
sequence_rows = prepared.inference_user_rows
lengths = np.where(
sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
)
ordered = np.argsort(lengths, kind="stable")
result = torch.zeros(
len(sequence_rows), config.embedding_dim, dtype=torch.float32
)
for group_start in range(0, len(ordered), config.user_batch_size):
output_rows = ordered[group_start : group_start + config.user_batch_size]
rows = sequence_rows[output_rows]
group_lengths = lengths[output_rows]
hidden = torch.zeros(
## Step 15 · agent · 2026-09-29T05:37:17.262Z
### read_file · 2026-09-29T05:37:17.262Z
{
"file_path": "/task/current_model/train_current_model.py",
"end_line": 2165,
"start_line": 1801
}
### Tool output · 2026-09-29T05:37:17.265Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1801-2165 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) ---
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)
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 16 · agent · 2026-09-29T05:37:19.309Z
### read_file · 2026-09-29T05:37:19.309Z
{
"end_line": 150,
"start_line": 1,
"file_path": "/task/ranking_evaluation.py"
}
### Tool output · 2026-09-29T05:37:19.312Z
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"
## Step 17 · agent · 2026-09-29T05:37:21.249Z
### read_file · 2026-09-29T05:37:21.249Z
{
"start_line": 151,
"end_line": 232,
"file_path": "/task/ranking_evaluation.py"
}
### Tool output · 2026-09-29T05:37:21.252Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-232 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.
--- FILE CONTENT (truncated) ---
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
## Step 18 · agent · 2026-09-29T05:37:23.534Z
### run_shell_command · 2026-09-29T05:37:23.534Z
{
"command": "lscpu | grep \"Model name\\|CPU(s):\" ; free -h",
"description": "Check available CPU cores and memory"
}
### Tool output · 2026-09-29T05:37:23.566Z
Output: CPU(s): 8
Model name: AMD EPYC 7R13 Processor
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1618
## Step 19 · agent · 2026-09-29T05:37:25.289Z
### run_shell_command · 2026-09-29T05:37:25.289Z
{
"description": "Check total system memory",
"command": "cat /proc/meminfo | grep MemTotal"
}
### Tool output · 2026-09-29T05:37:25.304Z
Output: MemTotal: 15988968 kB
Process Group PGID: 1623
## Step 20 · agent · 2026-09-29T05:37:28.423Z
### run_shell_command · 2026-09-29T05:37:28.423Z
{
"description": "Check PyTorch version and default thread count",
"command": "python3 -c \"import torch; print('PyTorch:', torch.__version__, 'threads:', torch.get_num_threads())\""
}
### Tool output · 2026-09-29T05:37:31.334Z
Output: PyTorch: 2.8.0+cpu threads: 4
Process Group PGID: 1627
## Step 21 · agent · 2026-09-29T05:37:32.965Z
### read_file · 2026-09-29T05:37:32.965Z
{
"end_line": 50,
"start_line": 1,
"file_path": "/task/requirements.txt"
}
### Tool output · 2026-09-29T05:37:32.968Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-50 of 62 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: 51.
--- FILE CONTENT (truncated) ---
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
## Step 22 · agent · 2026-09-29T05:37:34.820Z
### run_shell_command · 2026-09-29T05:37:34.820Z
{
"command": "ls -lh /task/data/",
"description": "List files and sizes in /task/data"
}
### Tool output · 2026-09-29T05:37:34.835Z
Output: total 340M
-r--r--r-- 1 root root 342K Sep 29 05:31 candidate_catalog.parquet
-r--r--r-- 1 root root 83K Sep 29 05:31 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 05:31 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 05:32 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 05:31 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 05:31 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 05:31 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 05:31 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 05:31 train_unlikes.parquet
-r--r--r-- 1 root root 36K Sep 29 05:31 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 05:31 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 05:31 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 05:31 val_undislikes.parquet
-r--r--r-- 1 root root 71K Sep 29 05:31 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 05:31 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 05:32 validation_preference_state.parquet
Process Group PGID: 1637
## Step 23 · agent · 2026-09-29T05:37:41.147Z
### Recorded reasoning summary
Analyzing Data Sizes
My focus has shifted to understanding the scale of our datasets. I've noted that `train_listens.parquet` is a substantial 305MB, while `val_listens.parquet` is considerably smaller at 9.5MB. This size disparity is a key observation as I proceed.
### Recorded reasoning summary
Optimizing Caching Strategy
I'm now exploring the `--prepared-cache` option in `train_current_model.py`. By specifying `/app/solution/validation_cache.npz`, I believe I can significantly speed up validation runs by avoiding repeated parsing of the large training parquet file. I'm assessing the preparation time to quantify this efficiency gain.
### run_shell_command · 2026-09-29T05:37:41.147Z
{
"description": "Copy initial production model files to /app/solution/",
"command": "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/"
}
### Tool output · 2026-09-29T05:37:41.164Z
Output: (empty)
Process Group PGID: 1640
## Step 24 · agent · 2026-09-29T05:37:45.290Z
### Recorded reasoning summary
Running Baseline Validation
I'm currently executing the baseline validation script to replicate the backtest. My focus is on capturing the duration and performance score from this initial run.
### Recorded reasoning summary
Confirming Cache Functionality
I'm verifying how the `--prepared-cache` flag interacts with training. My investigation shows it exclusively handles the saving and loading of `SequenceData`, and doesn't influence the training process itself. I've also confirmed its interaction within `load_or_prepare`.
### run_shell_command · 2026-09-29T05:37:45.290Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet \\\n --prepared-cache /app/solution/validation_cache.npz",
"description": "Run baseline validation training and cache prepared sequence data"
}
### Tool output · 2026-09-29T05:43:45.780Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.7s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.8s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.1s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=24.9s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.5s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.0s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.2s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=44.8s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=196.9s
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=210.0 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/production_validation.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 356.16,
"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": 0.5398380482450718,
"positive_logit": 0.7427799576364758,
"negative_logit": -0.6222247063961966,
"pair_accuracy": 0.7547733315257186,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 209.99864311700003,
"validation_loss": 0.4966362188872719,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.44746553799996
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497010231018,
"item_norm_mean": 1.7570279836654663,
"item_norm_std": 0.8279485106468201
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1643
## Step 25 · agent · 2026-09-29T05:43:53.810Z
### run_shell_command · 2026-09-29T05:43:53.810Z
{
"description": "Score the baseline validation ranking with evaluate_public.py",
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T05:43:57.181Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1705
## Step 26 · agent · 2026-09-29T05:44:03.464Z
### Recorded reasoning summary
Saving Baseline Ranking
I'm ensuring our initial performance metric, quality_ndcg@10 of 0.02128 across 4263 users, is securely saved. I'm also confirming the submission format aligns with the deliverable's `/app/solution/submission.parquet` requirement.
### Recorded reasoning summary
Testing Submission Generation
I'm considering the implications of generating a baseline submission now. While the prompt advises keeping the best valid submission, I'm weighing this against the potential benefit of understanding submission generation mechanics and timing before investing precious time.
### Recorded reasoning summary
Investigating Negative Sampling
I'm now digging into how the training objective handles negative examples. My immediate concern is potential CPU thread contention if background training processes run concurrently with my research. I'll be carefully examining the prompt's emphasis on negative sampling limitations and improvements within the fixed CPU budget.
### update_topic · 2026-09-29T05:44:03.464Z
{
"strategic_intent": "Diagnose current negative-sampling strategy and training objective to identify quality bottlenecks.",
"summary": "I have reproduced the baseline validation backtest, achieving quality_ndcg@10 of 0.02128 across 4,263 scored users, and cached the prepared sequence dataset. I am now transitioning to the research and diagnosis phase, focusing specifically on how the training objective selects and uses negative examples, the loss function, the candidate pool, false negative filtering, and in-batch vs uniform sampling.",
"title": "Diagnosing Negative Sampling Strategy"
}
### Tool output · 2026-09-29T05:44:03.467Z
Current topic: "Diagnosing Negative Sampling Strategy"
Topic summary: I have reproduced the baseline validation backtest, achieving quality_ndcg@10 of 0.02128 across 4,263 scored users, and cached the prepared sequence dataset. I am now transitioning to the research and diagnosis phase, focusing specifically on how the training objective selects and uses negative examples, the loss function, the candidate pool, false negative filtering, and in-batch vs uniform sampling.
Strategic Intent: Diagnose current negative-sampling strategy and training objective to identify quality bottlenecks.
## Step 27 · agent · 2026-09-29T05:44:45.534Z
### Recorded reasoning summary
Exploring Negative Sampling Strategies
I'm currently dissecting how the model handles negative sampling, specifically examining the configurations for uniform and in-batch negatives, and considering the impact of the `inbatch_correction_power` parameter on the training objective.
### Recorded reasoning summary
Refining Negative Sample Filtering
I'm now focusing on the nuances of `draw_full_history_filtered_negatives`, particularly how the `negative_filter_scope` and `discovery_horizon_days` settings influence the selection and weighting of negative samples, aiming to optimize relevance and avoid known positives effectively.
### Recorded reasoning summary
Generating In-Batch Negatives
I'm working on the logic for in-batch negatives. I'm ensuring that if there aren't enough unique targets within a batch to fulfill the `inbatch_negatives` count, the remaining slots are filled with uniformly sampled items.
### Recorded reasoning summary
Adjusting Negative Sample Generation
I'm refining the generation of in-batch negatives, ensuring that fallback mechanisms fill insufficient unique targets. Additionally, I'm implementing a robust loop to filter out invalid negative samples, iterating until a satisfactory set is achieved, and then applying item-specific corrections.
### Recorded reasoning summary
Evaluating In-Batch Negative Logic
I'm closely examining the in-batch negative sampling mechanism. Specifically, I'm trying to understand the definition and scope of a "block" in this context, as it directly impacts how `targets` are used for generating these negatives.
### Recorded reasoning summary
Investigating Dense Block and User Grouping
I'm currently dissecting the `build_dense_block` function and its interaction with user grouping logic. I'm trying to clarify how `users` are consolidated into `groups` and the implications this has on the scale of targets generated within a single block, which appears to be quite substantial.
### Recorded reasoning summary
Clarifying In-Batch Sampling
I'm re-evaluating the in-batch negative sampling strategy. It appears the current implementation samples uniformly from unique batch targets, rather than based on item popularity or batch frequency, which is the standard for two-tower models. This distinction is critical.
### Recorded reasoning summary
Applying In-Batch Popularity Corrections
I'm now focusing on the `build_item_correction` function. I'm analyzing how it calculates item propensities from training targets, applies a power correction based on popularity, and normalizes these corrections to mitigate bias in the negative sampling objective.
### Recorded reasoning summary
Correcting Loss Calculations
I'm realizing a significant flaw in how the `corrected_negative_loss` is calculated. The normalization of `weights` by their sum within the in-batch negative component appears to negate the intended effect of the popularity corrections, which needs immediate revision.
### Recorded reasoning summary
Revising Loss Normalization
I've identified that the normalization of weights within the `corrected_negative_loss` was counterproductive. My current focus is on rectifying this to properly leverage the popularity corrections and ensure the in-batch negative component accurately reflects sampling biases.
### Recorded reasoning summary
Revising Unique Target Sampling
I'm realizing that `torch.unique(targets)` treats all unique items equally, regardless of their actual frequency. This means the current sampling of in-batch negatives from unique targets is uniform, not reflecting actual popularity, which is a critical departure from expected behavior.
### Recorded reasoning summary
Revising In-Batch Sampling Logic
I'm realizing a fundamental issue: in-batch negatives are sampled uniformly from unique targets, ignoring popularity. This, combined with how item corrections were applied, disproportionately penalizes rare items, potentially leading to the model favoring only popular items, which contradicts the discovery objective.
### Recorded reasoning summary
Revising In-Batch Sampling Logic
I'm realizing a fundamental issue: in-batch negatives are sampled uniformly from unique targets, ignoring popularity. This, combined with how item corrections were applied, disproportionately penalizes rare items, potentially leading to the model favoring only popular items, which contradicts the discovery objective.
### Recorded reasoning summary
Revising Uniform Sampling Logic
I'm focusing on how `torch.unique` treats all items equally. This means my in-batch negative sampling, by drawing from unique targets, is currently uniform and doesn't reflect actual item popularity. This needs to be re-evaluated.
### Recorded reasoning summary
Correcting In-Batch Sampling Bias
I've realized that sampling in-batch negatives from unique targets, as my current `torch.unique` approach does, treats all items equally. This is incorrect; in-batch negatives should be sampled proportionally to their frequency within the batch to effectively penalize popular items and align with the standard two-tower model approach.
### Recorded reasoning summary
Revising Logit Calculations
I'm examining the theoretical underpinnings of using `logits - log(p_j)` and its connection to PMI. I'm also scrutinizing the specific implementation of the Binary Cross Entropy loss, particularly how `softplus` is applied to both positive and negative logit calculations, and how the `correction` term is integrated into the negative loss.
### Recorded reasoning summary
Revising Correction Application
I'm analyzing how `correction` is applied to the in-batch negative loss calculation. It appears `weights = correction[:, selected]` is incorrectly using item propensities directly as weights, rather than their normalized, inverse probabilities, which is crucial for accurate loss adjustment.
### Recorded reasoning summary
Applying Inverse Probability Corrections
I'm now focused on calculating the `correction` term by deriving item propensities from target counts. I'm applying a power transformation based on popularity and then normalizing these values to generate inverse probabilities that will be used to adjust the in-batch negative loss, mitigating bias.
### Recorded reasoning summary
Correcting Popularity Bias
I'm identifying that my `raw` calculation for in-batch corrections is inversely related to item probability. This means rare items get large `raw` values, while popular items get small ones. This seems to be counteracting the goal of penalizing popular items.
### Recorded reasoning summary
Analyzing Negative Sampling Sources
I'm currently investigating the source of my uniform negatives, confirming they're sampled from the entire catalog of 30,000 items. I also want to inspect the composition of `unique_targets` from a batch to understand the distribution of items present.
### Recorded reasoning summary
Analyzing Discovery Retrieval
I'm now examining the sources of uniform negatives and the composition of unique targets from batches to understand item distributions. I'm also starting to look into the `candidate_catalog.parquet` for popularity distribution and digging into the definition of "discovery" and "organic" behavior to clarify retrieval and validation criteria.
### grep_search · 2026-09-29T05:44:45.534Z
{
"dir_path": "/task/current_model",
"pattern": "dislike"
}
### Tool output · 2026-09-29T05:44:45.555Z
Found 40 matches for pattern "dislike" in path "/task/current_model":
---
File: train_current_model.py
L110: disliked_states: np.ndarray
L114: inference_disliked_items: np.ndarray
L176: active_dislikes: dict[int, int],
L179: disliked_states: list[np.ndarray],
L183: disliked_states.append(
L184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186: state_counts.append((len(active_likes), len(active_dislikes)))
L252: disliked_states: list[np.ndarray],
L279: active_dislikes: dict[int, int] = {}
L293: else active_dislikes
L304: active_dislikes,
L307: disliked_states,
L320: dislike_count: int,
L337: result[5] = np.log1p(dislike_count) / 4.0
L369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383: disliked_states = [
L384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405: disliked_states,
L427: int(dislike_counts[inference_row]),
L440: np.log1p(dislike_counts[missing]) / 4.0
L455: disliked_states=np.stack(disliked_states),
L459: inference_disliked_items=inference_dislikes,
L784: disliked_items = torch.from_numpy(
L785: prepared.disliked_states[unique_states].astype(np.int64)
L788: disliked_vectors = model._mean_context_channel(disliked_items)
L793: disliked_vectors[inverse_rows],
L802: disliked_vectors: torch.Tensor,
L807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159: disliked_vectors,
L1844: disliked_items = torch.from_numpy(
L1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851: model._mean_context_channel(disliked_items),
---
File: two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184: ("dislikes", 1, 1, 0),
L185: ("undislikes", 1, 0, 1),
L265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281: return liked, disliked, like_counts, dislike_counts
---
## Step 28 · agent · 2026-09-29T05:44:57.879Z
### Recorded reasoning summary
Clarifying Negatives
I'm processing the distinction between user context features and negative training examples. My current focus is on understanding the precise role and definition of "negatives" during the training phase, particularly in light of `invalid_full_history_negatives`.
### Recorded reasoning summary
Refining Exclusion Logic
I'm digging into the `invalid_full_history_negatives` function. It seems to be excluding known positives from the training prefix and the target item itself, which is a crucial step in defining valid negative samples for training.
### Recorded reasoning summary
Analyzing Target Weighting
I'm now examining `target_weighting` and its impact. My attention is on how `"organic-strong"` influences training and what other options exist for this parameter within the configuration, specifically within `train_current_model.py`.
### Recorded reasoning summary
Splitting Data Sequences
I'm currently analyzing how the data sequences are being split. My focus is on the logic that determines the boundary between training and validation sets for each user, particularly the `last_training_target` and `first_validation_target` indices. The handling of sequences shorter than two items and the `split_timestamp` calculation are also key points of investigation.
### Recorded reasoning summary
Revising Weighting Strategy
I'm re-evaluating the target weighting. It appears the baseline used "organic-strong" rather than the expected "discovery-graded," impacting how I interpret the weight adjustments and the `organic_like_target_weight` logic.
### Recorded reasoning summary
Investigating Negative Sampling
I'm focusing on the core of the user's request: how negative examples are selected and used. My analysis is now centered on diagnosing limitations within the current negative-sampling strategy, preparing to identify areas for improvement under the CPU budget constraints.
### run_shell_command · 2026-09-29T05:44:57.879Z
{
"description": "Inspect candidate catalog structure and popularity distribution",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport torch\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"Catalog shape:\", catalog.shape)\nprint(\"Catalog columns:\", catalog.columns.tolist())\nprint(catalog.head(5))\nprint(\"Popularity stats:\")\nprint(catalog[[\"positive_listens\", \"organic_positive_listens\", \"popularity_rank\"]].describe())\n'"
}
### Tool output · 2026-09-29T05:45:01.857Z
Output: Catalog shape: (30000, 4)
Catalog columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
Popularity stats:
positive_listens organic_positive_listens popularity_rank
count 30000.000000 30000.000000 30000.000000
mean 668.544300 313.926433 15000.500000
std 1053.389553 636.039902 8660.398374
min 163.000000 0.000000 1.000000
25% 228.000000 93.000000 7500.750000
50% 354.000000 162.000000 15000.500000
75% 669.250000 309.000000 22500.250000
max 26615.000000 20620.000000 30000.000000
Process Group PGID: 1733
## Step 29 · agent · 2026-09-29T05:45:05.047Z
### Recorded reasoning summary
Inspecting Baseline Predictions
I'm currently focused on validating the baseline's performance. My next step involves a thorough inspection of the predictions made by this baseline model, specifically by examining the `/app/solution/production_validation.parquet` file.
### run_shell_command · 2026-09-29T05:45:05.047Z
{
"description": "Analyze the baseline model's predictions and popularity bias",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\npred = pd.read_parquet(\"/app/solution/production_validation.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nprint(\"Predictions shape:\", pred.shape)\nprint(\"Unique users:\", pred[\"uid\"].nunique())\nprint(\"Unique items predicted:\", pred[\"item_id\"].nunique())\n\npred_with_pop = pred.join(catalog, on=\"item_id\")\nprint(\"Top 10 predicted items and their frequency in top 10:\")\ntop10_preds = pred[pred[\"rank\"] <= 10]\nprint(top10_preds[\"item_id\"].value_counts().head(20))\nprint(\"Popularity rank of top 10 predicted items:\")\nprint(top10_preds.join(catalog, on=\"item_id\")[\"popularity_rank\"].describe())\nprint(\"Popularity rank of all predicted items:\")\nprint(pred_with_pop[\"popularity_rank\"].describe())\n'"
}
### Tool output · 2026-09-29T05:45:06.708Z
Output: Predictions shape: (845600, 3)
Unique users: 8456
Unique items predicted: 2575
Top 10 predicted items and their frequency in top 10:
item_id
6006390 2917
8213481 2800
6896222 2712
8743390 2653
5436843 2587
2651314 2580
8722394 2542
3542184 2427
5862961 2292
906358 2066
5927996 1872
9378983 1537
2185191 1522
6901374 1463
3033749 1333
5252571 1289
2408923 1215
4423556 1148
8351511 1121
3375935 1107
Name: count, dtype: int64
Popularity rank of top 10 predicted items:
count 84560.000000
mean 2597.053903
std 5715.185504
min 1.000000
25% 14.000000
50% 111.000000
75% 2124.000000
max 29868.000000
Name: popularity_rank, dtype: float64
Popularity rank of all predicted items:
count 845600.000000
mean 2991.803713
std 5853.839336
min 1.000000
25% 102.000000
50% 413.000000
75% 2627.000000
max 29992.000000
Name: popularity_rank, dtype: float64
Process Group PGID: 1750
## Step 30 · agent · 2026-09-29T05:45:50.608Z
### Recorded reasoning summary
Observing Predicted Item Performance
I'm focusing on the performance of top predicted items. Notably, `item_id 6006390` is appearing in the top 10 for a significant portion of users, around 35%. Its median popularity rank among these top predictions is also quite good, sitting at 111 out of 30,000.
### run_shell_command · 2026-09-29T05:45:50.608Z
{
"description": "Analyze ground truth validation relevance distribution and popularity",
"command": "python3 -c '\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\n\ntruth_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n truth_items.append({\"uid\": uid, \"item_id\": item, \"grade\": grade})\ndf_truth = pd.DataFrame(truth_items).join(catalog, on=\"item_id\")\nprint(\"Total relevant events in validation:\", len(df_truth))\nprint(\"Unique users with relevant events:\", df_truth[\"uid\"].nunique())\nprint(\"Unique items in relevant events:\", df_truth[\"item_id\"].nunique())\nprint(\"Grade distribution:\", df_truth[\"grade\"].value_counts().to_dict())\nprint(\"Popularity rank of relevant validation items:\")\nprint(df_truth[\"popularity_rank\"].describe())\nprint(\"Positive listens of relevant validation items:\")\nprint(df_truth[\"positive_listens\"].describe())\n'"
}
### Tool output · 2026-09-29T05:45:52.092Z
Output: Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1776
## Step 31 · agent · 2026-09-29T05:46:06.138Z
### run_shell_command · 2026-09-29T05:46:06.138Z
{
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/task\")\nimport pandas as pd\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\n\ntruth_items = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n truth_items.append({\"uid\": uid, \"item_id\": item, \"grade\": grade})\ndf_truth = pd.DataFrame(truth_items).join(catalog, on=\"item_id\")\nprint(\"Total relevant events in validation:\", len(df_truth))\nprint(\"Unique users with relevant events:\", df_truth[\"uid\"].nunique())\nprint(\"Unique items in relevant events:\", df_truth[\"item_id\"].nunique())\nprint(\"Grade distribution:\", df_truth[\"grade\"].value_counts().to_dict())\nprint(\"Popularity rank of relevant validation items:\")\nprint(df_truth[\"popularity_rank\"].describe())\nprint(\"Positive listens of relevant validation items:\")\nprint(df_truth[\"positive_listens\"].describe())\n'",
"description": "Analyze ground truth validation relevance distribution and popularity"
}
### Tool output · 2026-09-29T05:46:10.180Z
Output: Total relevant events in validation: 37769
Unique users with relevant events: 4263
Unique items in relevant events: 14124
Grade distribution: {1: 33352, 3: 4417}
Popularity rank of relevant validation items:
count 37769.000000
mean 8463.087744
std 8117.953262
min 1.000000
25% 1733.000000
50% 5622.000000
75% 13374.000000
max 30000.000000
Name: popularity_rank, dtype: float64
Positive listens of relevant validation items:
count 37769.000000
mean 1761.124652
std 2645.924872
min 163.000000
25% 398.000000
50% 855.000000
75% 1963.000000
max 26615.000000
Name: positive_listens, dtype: float64
Process Group PGID: 1787
## Step 32 · agent · 2026-09-29T05:46:39.567Z
### Recorded reasoning summary
Analyzing Validation Data
My current focus is on dissecting the validation data. I've observed that a substantial 14,124 unique items are marked as relevant. Interestingly, the median popularity rank for these relevant items is 5,622, with the mean rank slightly higher at 8,463. This provides a valuable benchmark for evaluating baseline predictions.
### Recorded reasoning summary
Investigating Bias Causes
I'm digging into why my baseline is so biased. It's predicting only a tiny fraction of items, and those are heavily skewed towards the most popular ones. The negative sampling strategy seems like the prime suspect; popular items get swamped with positive signals, overwhelming the rarer negative ones.
### Recorded reasoning summary
Refining Negative Sampling
I'm investigating how popular items are over-predicted. The issue appears to stem from how negative sampling is handled. I'm examining the `in_batch_negatives` and `uniform_negatives` strategies, specifically the impact of popularity-based sampling and logQ correction on removing popularity bias and retrieving items based on affinity rather than global popularity.
### Recorded reasoning summary
Testing Sampling Strategies
I'm currently testing the effectiveness of `in_batch_negatives` and `uniform_negatives` sampling strategies. My goal is to see how popularity-based sampling and logQ correction impact the removal of popularity bias, ultimately allowing retrieval based on affinity rather than just global popularity.
### Recorded reasoning summary
Adjusting Negative Sampling
I'm now implementing a refined negative sampling approach. The code snippet suggests I'm focusing on how to combine uniform negatives with in-batch negatives, applying a `correction` factor to account for popularity bias during this process.
### Recorded reasoning summary
Analyzing In-Batch Sampling
I'm dissecting how `in_batch_negatives` function. It appears distinct items, regardless of their frequency in the batch, have an equal chance of being selected. This uniformity, contrary to intuition, might be a key factor in the observed biases.
### Recorded reasoning summary
Investigating Source Code
I'm tracing the origin of `item_correction`. My investigation led me to the `build_item_correction` function, which seems to calculate in-batch propensities by counting item occurrences within the target sequences and then deriving probabilities.
### Recorded reasoning summary
Evaluating Correction Impact
I'm analyzing the `raw` calculation and its interaction with clipping. Popular items result in values less than 1, clipped low, while rare items yield high values, clipped high. I need to examine how this `correction` term is ultimately applied within the loss function to understand its bias-mitigation effect.
### Recorded reasoning summary
Adjusting Loss Penalties
I'm seeing how the `correction` term in the loss function modifies penalties. Popular items get their negative penalties reduced, while rare items have theirs amplified, aiming to balance their influence in the training.
### Recorded reasoning summary
Revising Correction Logic
I'm questioning the mathematical basis of the `raw` calculation for importance sampling correction. The current approach seems to invert the intended bias mitigation, potentially reintroducing the very popularity bias it aims to remove by converting in-batch negatives back to a uniform loss.
### Recorded reasoning summary
Questioning Correction Mechanism
I'm re-examining the `raw` calculation's impact on importance sampling correction. My current hypothesis is that it might be counterproductively amplifying popularity bias, not mitigating it, by effectively transforming in-batch negatives into a uniform loss scenario. I need to verify this.
### Recorded reasoning summary
Questioning Correction Mechanism
I'm re-examining the `raw` calculation's impact on importance sampling correction. My current hypothesis is that it might be counterproductively amplifying popularity bias, not mitigating it, by effectively transforming in-batch negatives into a uniform loss scenario. I need to verify this.
### Recorded reasoning summary
Verifying Implementation Details
I'm now examining `train_current_model.py` and `two_tower_model.py` closely. Specifically, I'm checking for the presence of `draw_filtered_mixed_negatives` within `two_tower_model.py` to understand its implementation in drawing negatives.
### Recorded reasoning summary
Tracing Negative Sampling
I'm tracing the code flow for negative sampling. It seems `train_current_model.py` uses `draw_full_history_filtered_negatives` when `negative_filter_scope` is "full-training-history", which in turn utilizes `build_known_positive_filter` to prepare the data.
### Recorded reasoning summary
Tracing Data Preparation
I am scrutinizing the data preparation steps for negative sampling. The code appears to use `np.unique` on sequence items, but I need to confirm if this effectively captures all items from the user's full training history as a filter.
### read_file · 2026-09-29T05:46:39.567Z
{
"start_line": 880,
"file_path": "/task/current_model/train_current_model.py",
"end_line": 925
}
### Tool output · 2026-09-29T05:46:39.574Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 880-925 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: 926.
--- FILE CONTENT (truncated) ---
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
]
## Step 33 · agent · 2026-09-29T05:47:19.267Z
### Recorded reasoning summary
Analyzing Filter Logic
I'm currently examining the `build_known_positive_filter` function and its conditional application based on `config.negative_filter_scope`. My focus is on understanding how this filter is constructed and when it's invoked, specifically looking at the `full-training-history` scenario.
### Recorded reasoning summary
Investigating User Indices
I'm now questioning the role of `user_rows` within `invalid_full_history_negatives`, as its origin in `run_training_epoch` seems to derive from `users` which are simply indices. This raises concerns about the correctness of how user information is being passed and utilized in the context of negative sampling.
### Recorded reasoning summary
Revising Training Targets
I'm analyzing how `targets` are constructed in `build_dense_block`. The slicing logic, particularly `target_positions`, suggests a focus on predicting the next item in a sequence, which differs from my inference-time understanding where all past encounters are excluded. This discrepancy needs resolution.
### run_shell_command · 2026-09-29T05:47:19.267Z
{
"command": "python3 -c '\nimport numpy as np\n\ncache = np.load(\"/app/solution/validation_cache.npz\")\nsequence_items = cache[\"sequence_items\"]\noffsets = cache[\"offsets\"]\nuser_ids = cache[\"user_ids\"]\n\ntotal_targets = 0\nrepeat_targets = 0\n\nfor i in range(min(1000, len(offsets) - 1)):\n start = offsets[i]\n end = offsets[i + 1]\n items = sequence_items[start:end]\n seen = set()\n for item in items:\n if item in seen:\n repeat_targets += 1\n else:\n seen.add(item)\n total_targets += 1\n\nprint(f\"Sample 1000 users: total listens = {total_targets}, repeat listens = {repeat_targets} ({repeat_targets/total_targets*100:.1f}%)\")\n'",
"description": "Check proportion of repeat listens in user sequences"
}
### Tool output · 2026-09-29T05:47:20.085Z
Output: Sample 1000 users: total listens = 2105262, repeat listens = 1510561 (71.8%)
Process Group PGID: 1813
## Step 34 · agent · 2026-09-29T05:47:48.819Z
### Recorded reasoning summary
Exploring Discovery Grading Logic
I'm currently diving into the logic for "discovery-graded" target weighting. Specifically, I'm examining the process of loading organic like outcomes and setting the history end point, with initial focus on the user iteration loop for grading.
### Recorded reasoning summary
Refining Split Timestamp Logic
I'm working through the data segmentation, specifically determining the split timestamp for validation data. My current focus is on handling cases where there are fewer than two training examples, ensuring the `split_timestamp` correctly defaults to `history_end` when necessary, while also accurately identifying unique items within the sequence for novelty checks.
### Recorded reasoning summary
Investigating Negative Example Selection
I'm currently deep-diving into the core of our training objective, specifically how negative examples are selected and then subsequently utilized. My immediate focus is on understanding the `draw_full_history_filtered_negatives` function and the implications of different weighting strategies for repeated items within this selection process.
### Recorded reasoning summary
Implementing In-Batch Negatives
I'm working on the practical implementation of in-batch negative sampling. My current efforts are focused on generating random priorities, masking out true targets, and selecting the top-k unique target indices to form the in-batch negative set, with a fallback for filler generation.
### Recorded reasoning summary
Revising In-Batch Sampling
I've identified an issue in the in-batch negative selection process. Taking `torch.unique` on the targets discards crucial frequency information, leading to uniform sampling rather than proportional selection based on item popularity within the batch. This needs correction to reflect true in-batch dynamics.
### Recorded reasoning summary
Correcting Negative Sampling Proportions
I'm revising how uniform negatives are sampled. Currently, all catalog items have equal probability of selection, irrespective of their overall popularity. This creates a severe imbalance where high-listen-count items receive disproportionately more positive than negative training signals, impacting their representation.
### Recorded reasoning summary
Addressing Popularity Bias
I'm analyzing how popular items are overwhelmingly favored due to the current training setup. My immediate focus is on understanding how positive and negative logits are calculated and the role of `softplus` and `corrected_negative_loss` in the observed bias, particularly the disproportionate impact on tail tracks.
### Recorded reasoning summary
Analyzing Loss Function Biases
I'm examining the loss calculation, specifically `corrected_negative_loss`. My current focus is on understanding the implications of `F.softplus` on logits and how the combination of uniform and in-batch negatives, along with the `correction` tensor, might be introducing or exacerbating biases towards popular items.
### Recorded reasoning summary
Reversing Correction Logic
I'm analyzing the `correction` term used for in-batch negatives. It appears to be inversely proportional to an item's popularity, meaning popular items get a small correction (low weight) and tail items get a large correction (high weight). This seems to be the exact opposite of the desired outcome.
### Recorded reasoning summary
Correcting Uniform Sampling Approach
I'm realizing the attempt to recover a uniform distribution over catalog items is fundamentally flawed. This uniform approach, by design, amplifies popularity bias due to the significant variance in item popularity. The goal is not a uniform expectation, but rather to balance popularity and affinity.
### Recorded reasoning summary
Analyzing Logit Adjustments
I'm re-evaluating how sampling bias correction impacts logits. By subtracting $\log P(i)$ during training, the resulting score $s_{ui}$ effectively represents personalized affinity $\text{PMI}(u, i)$, becoming independent of global popularity at inference time. This is a critical insight for unbiased ranking.
### Recorded reasoning summary
Testing Popularity Sampling
I'm now exploring popularity-based negative sampling, considering probabilities proportional to an item's popularity raised to an exponent. I want to analyze the impact of this versus uniform sampling and investigate combinations of both uniform and popularity-based negatives, as well as in-batch negatives sampled by target frequency.
### Recorded reasoning summary
Revising Invalid Negative Replacement
I'm noticing a critical issue in how invalid negatives are handled. When an invalid negative is identified, it's currently replaced with a random item, and its correction weight is reset to 1.0. This approach effectively discards the information from the invalid draw and treats it as a standard uniform negative, potentially undermining more nuanced sampling strategies.
### run_shell_command · 2026-09-29T05:47:48.819Z
{
"description": "Check candidate eligibility stats for validation",
"command": "python3 -c '\nimport numpy as np\n\nnpz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"keys:\", npz.files)\nuids = npz[\"uids\"]\nitem_ids = npz[\"item_ids\"]\npacked = npz[\"packed_eligible\"]\nprint(f\"uids: {len(uids)}, item_ids: {len(item_ids)}, packed shape: {packed.shape}\")\n\n# For a few users, check how many items are eligible\nfor r in range(5):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n print(f\"User {uids[r]}: eligible items = {mask.sum()} / {len(item_ids)}\")\n'"
}
### Tool output · 2026-09-29T05:47:49.330Z
Output: keys: ['uids', 'item_ids', 'packed_eligible']
uids: 8456, item_ids: 30000, packed shape: (8456, 3750)
User 100: eligible items = 29318 / 30000
User 200: eligible items = 29832 / 30000
User 300: eligible items = 29931 / 30000
User 600: eligible items = 29320 / 30000
User 700: eligible items = 28347 / 30000
Process Group PGID: 1829
## Step 35 · agent · 2026-09-29T05:47:59.470Z
### Recorded reasoning summary
Analyzing Candidate Eligibility
My focus has shifted to understanding how baseline rankings are currently computed. I'm reviewing the `rank_candidates` function, particularly how it handles the vast majority of items (over 29,000) that are eligible for users, excluding only those already seen in training history.
### Recorded reasoning summary
Refining Ranking Logic
I'm currently examining the `rank_candidates` function, specifically how it processes eligible items. My attention is on the batch processing of item encodings and the subsequent scoring mechanism against user queries.
### Recorded reasoning summary
Calculating Top Scores
I'm digging into the score calculation, noting it's a direct dot product between user and item vectors. This ensures fast, precomputed serving. I'm confirming the masking logic correctly excludes ineligible items before selecting the top N.
### Recorded reasoning summary
Investigating Negative Sampling
My current thoughts are centered on the prompt's explicit instruction to focus on negative sampling strategies within the training objective. I need to carefully investigate how negative examples are selected and used, diagnose any limitations, and then strategize improvements under the given CPU constraints.
### run_shell_command · 2026-09-29T05:47:59.470Z
{
"command": "ls -la /app/solution/",
"description": "List contents of /app/solution/"
}
### Tool output · 2026-09-29T05:47:59.487Z
Output: total 524012
drwxr-xr-x 1 agent agent 4096 Sep 29 05:43 .
drwxr-xr-x 1 agent agent 4096 Sep 29 05:33 ..
-rw-r--r-- 1 agent agent 1244228 Sep 29 05:43 production_validation.parquet
-r--r--r-- 1 agent agent 80058 Sep 29 05:37 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 05:37 two_tower_model.py
-rw-r--r-- 1 agent agent 535225808 Sep 29 05:38 validation_cache.npz
Process Group PGID: 1839
## Step 36 · agent · 2026-09-29T05:48:03.450Z
### Recorded reasoning summary
Confirming Data Availability
The `production_validation.parquet` file has been located. However, I've realized it only contains predicted ranks, not the raw scores needed for further analysis. I'm now investigating the training process and the `validation_cache.npz` file.
### run_shell_command · 2026-09-29T05:48:03.450Z
{
"description": "Check cache load time",
"command": "python3 -c '\nimport time\nt0 = time.time()\nimport sys\nsys.path.insert(0, \"/app/solution\")\nfrom train_current_model import load_cache, Config\nfrom pathlib import Path\ncfg = Config()\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", cfg)\nprint(\"Loaded cache in\", round(time.time() - t0, 2), \"s\")\n'"
}
### Tool output · 2026-09-29T05:48:07.689Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
Loaded cache in 3.94 s
Process Group PGID: 1842
## Step 37 · agent · 2026-09-29T05:49:49.641Z
### Recorded reasoning summary
Analyzing Training Time
I've confirmed the cache loads in under four seconds. My current focus is on understanding the training duration. The baseline run shows an initial train loss of 0.53984 with a validation loss of 0.49664, and a pair accuracy of 0.755 across over a million training targets and 130,000 validation targets for the first epoch.
### Recorded reasoning summary
Optimizing Training Duration
I've pinpointed that a full training epoch, including evaluation and ranking, takes under five minutes. My current efforts are concentrated on dissecting the 210-second training component within `run_training_epoch`, specifically by profiling the `build_dense_block` and `draw_training_negatives` functions to identify potential bottlenecks.
### Recorded reasoning summary
Investigating Negative Sampling
I'm diving into the negative sampling logic. Specifically, I've observed a nested loop structure within the `draw_training_negatives` function that's computationally intensive. My aim is to optimize this section, which involves repeated checks and potential regeneration of invalid negatives.
### Recorded reasoning summary
Refining Negative Sampling
I'm now meticulously dissecting the trainer's objective function and its approach to negative example selection. My primary focus is on diagnosing and enhancing the current negative sampling strategy, ensuring it fits within the fixed CPU budget and aligns with the public backtest validation.
### Recorded reasoning summary
Refining Negative Sampling Strategy
I'm now meticulously dissecting the trainer's objective function and its approach to negative example selection. My primary focus is on diagnosing and enhancing the current negative sampling strategy, ensuring it fits within the fixed CPU budget and aligns with the public backtest validation. I've observed that in-batch negatives aren't sampled proportionally to frequency and want to explore broader popularity-based sampling.
### Recorded reasoning summary
Exploring Explicit Negatives
I'm now considering explicit negative signals, specifically user dislikes. I need to determine if and how these explicit negatives can be incorporated into the sampling strategy to improve training and prevent recommending disliked content during candidate generation.
### Recorded reasoning summary
Incorporating Dislikes
I'm now analyzing how user dislikes, encompassing all train encounters, are excluded from candidate eligibility. My core question is whether the model learns from these dislikes during training. I'm investigating explicit dislikes as potential negative examples versus unencountered items as candidate negatives.
### Recorded reasoning summary
Investigating Target Counts
I'm examining the `target_count` variable in the `pack_positive_items` function. My concern is that it might be incorrectly including an extra item in the positive set, potentially skewing the training data and impacting downstream accuracy.
### Recorded reasoning summary
Examining Offsets
I'm scrutinizing how user data offsets are handled, particularly within `pack_positive_items`. My current hypothesis is that the `target_count` might be incorrectly incrementing beyond the actual user sequence length, leading to an overestimation of positive examples in training.
### Recorded reasoning summary
Analyzing Loss Function
I'm scrutinizing the `corrected_negative_loss` function. My focus is on how it utilizes both uniform and in-batch negatives, particularly the weighting applied to in-batch negatives via the `correction` tensor, and how this integrates with the overall loss calculation.
### Recorded reasoning summary
Adjusting Loss Weights
I'm re-evaluating the `corrected_negative_loss` by analyzing how weights are applied to in-batch negatives. It appears tail negatives are disproportionately penalized compared to popular ones, and I'm investigating why this `correction` mechanism is implemented this way.
### Recorded reasoning summary
Revising Correction Logic
I've identified that the `correction` term, intended to account for sampling bias, is inversely proportional to the square root of item popularity. This disproportionately penalizes rare items, causing the model to favor popular items during prediction, which I believe is the source of the observed recommendation bias.
### Recorded reasoning summary
Correcting Popularity Bias
I've determined the correction term subtracts a value inversely proportional to the square root of item popularity. This over-penalizes rare items, causing my model to favor popular ones. I'm now investigating how this aligns with standard approaches like subtracting log probabilities.
### Recorded reasoning summary
Analyzing BCE Loss
I've realized that subtracting $\log(p_j)$ in sampled softmax creates an unbiased estimator of the full catalog softmax. My current focus is on how this concept translates to Binary Cross-Entropy (BCE) loss, specifically analyzing the gradient for $s(u, j)$ when sampling from $Q(j)$ and how it differs from the sampled softmax approach.
### Recorded reasoning summary
Decomposing Score Components
I've realized that my current BCE loss setup, with uniform negative sampling, results in the model's score implicitly learning both user-item affinity and item popularity. Given the vast variance in item popularity, this popularity term currently dominates, explaining why recommendations are overly skewed towards universally popular items. I'm now exploring how to adjust the negative sampling strategy to mitigate this.
### Recorded reasoning summary
Learning Personalized Affinity
I've discovered that by sampling negatives proportional to popularity and setting the derivative of the loss to zero, the global item popularity term cancels out entirely. This means the learned score $s(u, j)$ effectively represents the Pointwise Mutual Information (PMI), capturing how much more a user likes an item than the average user, leading to personalized rankings rather than popularity-based ones.
### Recorded reasoning summary
Confirming Popularity Independence
I've confirmed that sampling negatives proportionally to their popularity effectively cancels out the global item popularity term in the loss function. This means the learned score now truly reflects personalized user-item affinity rather than just broad popularity, allowing for better discovery of niche items.
### Recorded reasoning summary
Testing Popularity Sampling
I'm benchmarking popularity-based negative sampling using `torch.multinomial` on CPU. My experiments aim to assess its speed and feasibility for 30,000 items, exploring catalog popularity distributions with varying alpha values and direct in-batch negative sampling.
### run_shell_command · 2026-09-29T05:49:49.641Z
{
"command": "python3 -c '\nimport torch\nimport time\n\nn_items = 30000\nweights = torch.ones(n_items)\nbatch_size = 6400\nnum_neg = 32\n\nt0 = time.time()\nfor _ in range(10):\n samples = torch.multinomial(weights, num_neg, replacement=True)\nprint(\"Multinomial (1D, batch=32):\", round(time.time() - t0, 4), \"s\")\n\n# Or sampling (batch_size, num_neg) from a 1D distribution\nt0 = time.time()\nfor _ in range(10):\n # Flattened draw\n draws = torch.multinomial(weights, batch_size * num_neg, replacement=True).reshape(batch_size, num_neg)\nprint(\"Multinomial (flattened 6400*32):\", round(time.time() - t0, 4), \"s\")\n'",
"description": "Benchmark torch.multinomial for popularity negative sampling"
}
### Tool output · 2026-09-29T05:49:52.783Z
Output: Multinomial (1D, batch=32): 0.0005 s
Multinomial (flattened 6400*32): 0.2311 s
Process Group PGID: 1859
## Step 38 · agent · 2026-09-29T05:49:57.897Z
### Recorded reasoning summary
Optimizing Drawing Performance
My latest findings indicate a remarkable 0.23-second execution for 10 batches of 200,000 draws. I'm now exploring alternative sampling methods, including NumPy's `np.random.choice`, alias methods, and rejection sampling, to further enhance this efficiency. My current focus is on leveraging pre-populated item ID arrays for potential speed-ups.
### run_shell_command · 2026-09-29T05:49:57.897Z
{
"description": "Benchmark table lookup sampling",
"command": "python3 -c '\nimport torch\nimport time\n\ntable = torch.randint(30000, (1_000_000,))\nbatch_size = 6400\nnum_neg = 32\n\nt0 = time.time()\nfor _ in range(10):\n idx = torch.randint(len(table), (batch_size, num_neg))\n samples = table[idx]\nprint(\"Table lookup sampling (10 iterations):\", round(time.time() - t0, 4), \"s\")\n'"
}
### Tool output · 2026-09-29T05:50:00.821Z
Output: Table lookup sampling (10 iterations): 0.0196 s
Process Group PGID: 1875
## Step 39 · agent · 2026-09-29T05:50:36.514Z
### Recorded reasoning summary
Analyzing Negative Sampling Factors
The table lookup overhead is remarkably low at 1.9 ms per batch, essentially negligible. My focus now shifts to other elements influencing negative sampling and the training objective, specifically examining target weighting and its configuration.
### Recorded reasoning summary
Examining Target Weighting Strategies
My attention has moved to understanding "discovery-graded" target weighting. I'm digging into the `train_current_model.py` script, specifically lines 981-1025, to analyze how user interactions and timestamps influence this weighting method during training and validation.
### Recorded reasoning summary
Investigating Target Weighting
I'm currently scrutinizing the "discovery-graded" target weighting. My analysis focuses on how user interactions and timestamps, as seen in `train_current_model.py` lines 981-1025, dynamically adjust weights for novel items during training and validation.
### Recorded reasoning summary
Refining Target Weighting Logic
I'm dissecting how "discovery-graded" weighting operates. It assigns 0.25 for repeats, 1.0 for novel organic listens, and a significant 3.0 for novel listens followed by an organic like within the defined horizon. I'm now clarifying the "organic-strong" effect.
### Recorded reasoning summary
Clarifying Organic-Strong Weights
I've identified that repeat listens weighted 1.0 via "organic-strong" conditions, despite 71.8% of listens being repeats and repeats being unrecommendable at test time. This seems counterproductive to the objective of predicting novel tracks, and grade 3 listens weren't weighted 3x as expected.
### Recorded reasoning summary
Exploring Negative Sampling Methods
I'm now systematically investigating alternative negative sampling strategies. My current focus is on exploring popularity-biased sampling, considering different $\alpha$ values, and evaluating combinations of uniform and popularity-weighted negatives. I'm also looking into implementing true in-batch negative sampling.
### Recorded reasoning summary
Refining Negative Sampling
I'm realizing that uniform negatives might not be sufficient. I need to consider sampling from items the user *could* encounter at test time, especially popular ones they haven't seen, to train the model to avoid them effectively.
### Recorded reasoning summary
Testing Loss Functions
I am now exploring different loss functions. I'm considering removing inverse-popularity weighting from BCE, applying logQ correction for sampled softmax, or investigating BPR. The core question is how these changes affect the model's ability to predict novel tracks effectively.
### Recorded reasoning summary
Investigating BCE Implementation
I'm examining the baseline's use of binary cross-entropy, particularly the `F.softplus` application. I need to verify this implementation against the script's actual logic for `positive_loss` and `negative_loss`, including the `corrected_negative_loss` function.
**Developing Experimentation Framework**
I'm working on creating a streamlined experiment script to facilitate validation and NDCG@10 evaluation. The goal is to significantly reduce iteration time, aiming for quick turnarounds to test hypotheses efficiently within a limited timeframe.
### Recorded reasoning summary
Profiling Training Bottlenecks
I'm digging into the 210-second training time. I've identified that `build_dense_block` and `draw_full_history_filtered_negatives` within each batch, utilizing Python loops and NumPy, might be significant bottlenecks. I'm also questioning the `threads` default and its impact.
### Recorded reasoning summary
Optimizing Negative Filtering
I'm seeing significant computational cost in how negatives are filtered. The nested loops and large array comparisons, especially involving `liked_items`, are creating an $O(B \times K^2)$ or $O(B \times K \times L)$ complexity where $K$ is the number of negatives and $L$ is the number of liked items. This needs a much more efficient approach.
### run_shell_command · 2026-09-29T05:50:36.514Z
{
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nimport time\nimport torch\nimport numpy as np\nfrom pathlib import Path\nfrom train_current_model import Config, load_cache, chronological_target_partition, build_target_weights, build_item_correction, build_known_positive_filter, model_config, build_dense_block, event_sequence, draw_training_negatives, score_target_microbatches\nimport two_tower_model as base\n\ntorch.set_num_threads(8)\nconfig = Config()\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", config)\npartition = chronological_target_partition(prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user)\nmodel = base.SequentialTwoTower(prepared.item_features, model_config(config))\ndevice = torch.device(\"cpu\")\n\ntrain_users = np.flatnonzero(partition.training_counts > 0)\nsorted_users = train_users[np.argsort(partition.training_counts[train_users], kind=\"stable\")]\ngroups = [sorted_users[start : start + config.user_batch_size] for start in range(0, len(sorted_users), config.user_batch_size)]\n\nknown_positive_packed = build_known_positive_filter(prepared, partition.history_training_counts)\ntarget_weights = build_target_weights(Path(\"/task\"), \"validation\", prepared, partition.history_training_counts, config)\nitem_correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10000)\n\nusers = groups[0]\ngroup_counts = partition.training_counts[users]\nhidden = torch.zeros(1, len(users), config.embedding_dim)\n\nt0 = time.time()\nblock = build_dense_block(prepared, target_weights, users, partition.window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), 0, min(100, int(group_counts.max())), config)\nt_block = time.time() - t0\n\nt0 = time.time()\nsequence_outputs, _ = event_sequence(model, torch.from_numpy(block[0].astype(np.int64)), torch.from_numpy(block[1].astype(np.float32)), hidden)\nt_gru = time.time() - t0\n\nflat_scored = block[5].reshape(-1)\nmask = torch.from_numpy(flat_scored)\nsequence_vectors = sequence_outputs.reshape(-1, config.embedding_dim)[mask]\nflat_states = block[3].reshape(-1)[flat_scored]\npositive_items = torch.from_numpy(block[4].reshape(-1)[flat_scored].astype(np.int64))\nknown_history = torch.from_numpy(block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(np.int64))\nliked_items = torch.from_numpy(prepared.liked_states[flat_states].astype(np.int64))\nflat_user_rows = np.broadcast_to(users[:, None], block[5].shape).reshape(-1)[flat_scored]\n\nt0 = time.time()\nnegatives, negative_weights = draw_training_negatives(positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device)\nt_neg = time.time() - t0\n\nt0 = time.time()\nstats, seq_grad = 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)\nt_score = time.time() - t0\n\nprint(f\"Time breakdown: block={t_block:.3f}s, gru={t_gru:.3f}s, neg={t_neg:.3f}s, score+bwd={t_score:.3f}s\")\n'",
"description": "Profile step-by-step time breakdown for one training step"
}
### Tool output · 2026-09-29T05:50:41.758Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
Time breakdown: block=0.002s, gru=0.003s, neg=0.002s, score+bwd=0.037s
Process Group PGID: 1888
## Step 40 · agent · 2026-09-29T05:51:05.828Z
### Recorded reasoning summary
Analyzing Performance Bottlenecks
I'm currently dissecting the timing breakdown. The neural network computations, encompassing candidate encoding, user MLP, loss calculation, and backpropagation, are consuming roughly 80% of the total time, which aligns with expected PyTorch behavior. This indicates that the core model processing is indeed the primary area of focus for optimization.
### Recorded reasoning summary
Exploring Negative Sampling
I'm currently investigating the impact of negative sampling strategies. My focus is on adjusting `inbatch_correction_power` and understanding the `corrected_negative_loss` function's mechanics, particularly how it handles the interaction between uniform and in-batch negatives, to see how these changes affect NDCG@10 performance.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm digging into the in-batch negative sampling. It seems my understanding of the `correction` term was incomplete. If `inbatch_correction_power` is zero, the `correction` and `raw` weights become uniformly one. I'm now scrutinizing the sampling process within `draw_full_history_filtered_negatives` to ensure it’s correctly handling unique targets and priorities, as this could be another source of error.
### Recorded reasoning summary
Understanding In-Batch Negatives
I'm clarifying why `unique_targets` are essential for in-batch negative sampling. My current focus is on the standard two-tower approach where positive items within a batch implicitly serve as negatives for other queries, an elegant design I'm exploring further.
### Recorded reasoning summary
Investigating Sampling Strategy
I'm re-examining how negatives are sampled. The current method selects 8 in-batch negatives randomly from unique targets and adds 24 uniform negatives per target, resulting in 32 encoded negatives. I'm now considering the impact of sampling negatives based on catalog popularity.
### Recorded reasoning summary
Adjusting Loss Calculation
I'm revisiting the LogQ correction mechanism for sampled softmax loss. My current focus is on how subtracting $\log q_j$ from logits, where $q_j$ is the candidate proposal distribution, effectively re-weights negative samples in relation to their popularity, potentially mitigating the issue of highly popular items becoming overly frequent negatives.
### Recorded reasoning summary
Deriving Logit Behavior
I'm realizing that the LogQ correction, when subtracted from logits, is effectively performing importance weighting for the denominator. This transforms the model's objective towards learning a score proportional to $\text{PMI}(u, i)$, suggesting a deeper connection between sampled softmax and intrinsic user-item association.
### Recorded reasoning summary
Testing Loss Functions
I am exploring alternative loss functions like BCE or margin loss, alongside the current softmax approach, to understand their impact on ranking performance. My immediate next step is to set up a quick, standalone experiment to test these on validation data without external processes, evaluating their speed and efficacy.
### run_shell_command · 2026-09-29T05:51:05.828Z
{
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\nimport evaluate_public\nfrom pathlib import Path\nimport pandas as pd\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Test scoring baseline\ndf = pd.read_parquet(\"/app/solution/production_validation.parquet\")\nranked = evaluate_public.ranked_items(df)\nres = evaluate_public.score(ranked, graded)\nprint(\"Scored baseline:\", res)\n'",
"description": "Test evaluation function directly in Python"
}
### Tool output · 2026-09-29T05:51:08.256Z
Output: Scored baseline: {'quality_ndcg@10': 0.02128, 'quality_users': 4263}
Process Group PGID: 1919
## Step 41 · agent · 2026-09-29T05:51:51.359Z
### Recorded reasoning summary
Testing Scoring Speed
The direct Python scoring function is impressively fast, completing in just 0.5 seconds. My next steps involve a deep dive into how modifying the negative sampling strategy and the choice of loss function impact this performance. I'm particularly keen to test specific hypotheses regarding negative sampling.
### Recorded reasoning summary
Exploring Negative Sampling Power
I'm investigating the impact of `inbatch_correction_power` within `build_item_correction`. Specifically, I'm considering how a negative exponent might alter the correction weights. My current focus is on understanding the mathematical implications and potential performance shifts this could introduce.
### Recorded reasoning summary
Developing Experiment Framework
I am now focused on building a modular script to rigorously test various negative sampling strategies and loss functions. This will allow me to systematically evaluate their impact on key metrics like NDCG@10, moving beyond theoretical considerations to empirical validation.
### Recorded reasoning summary
Evaluating Sampling and Loss
I'm now setting up experiments to compare different negative sampling methods, including uniform, popularity-based, and in-batch approaches. Alongside this, I'm analyzing the effects of various loss functions, such as BCE with different weighting schemes and sampled softmax variations. My goal is to empirically validate which combinations yield the best performance.
### update_topic · 2026-09-29T05:51:51.359Z
{
"title": "Executing Validation Experiments",
"summary": "I have diagnosed key limitations in the production model: severe popularity bias driven by uniform/inverted negative weighting, lack of proper in-batch/popularity negative sampling, and uncalibrated BCE loss. I am now creating a modular validation experimentation script to systematically evaluate negative sampling distributions (popularity-biased, true in-batch), loss functions (unweighted BCE, logQ-corrected BCE, and sampled softmax cross-entropy), and discovery-graded target weighting.",
"strategic_intent": "Develop and execute controlled negative-sampling and loss experiments on the public validation split."
}
### Tool output · 2026-09-29T05:51:51.363Z
Current topic: "Executing Validation Experiments"
Topic summary: I have diagnosed key limitations in the production model: severe popularity bias driven by uniform/inverted negative weighting, lack of proper in-batch/popularity negative sampling, and uncalibrated BCE loss. I am now creating a modular validation experimentation script to systematically evaluate negative sampling distributions (popularity-biased, true in-batch), loss functions (unweighted BCE, logQ-corrected BCE, and sampled softmax cross-entropy), and discovery-graded target weighting.
Strategic Intent: Develop and execute controlled negative-sampling and loss experiments on the public validation split.
## Step 42 · agent · 2026-09-29T05:52:19.710Z
### Recorded reasoning summary
Exploring experiment execution script
I'm currently mapping out `run_experiment.py`. My thinking is leaning towards importing from `train_current_model.py` and then selectively overriding components, or perhaps creating a parameterized version to manage variations. I need to analyze `train_current_model.py` further to understand which specific functions will be impacted by these changes.
### Recorded reasoning summary
Refining scoring logic
I'm focusing on `score_target_microbatches` now, as its parameters `sequence_vectors`, `flat_states`, and `flat_activity` suggest it's central to processing sequence data. Understanding how these are fed and used will inform how I integrate it.
### Recorded reasoning summary
Analyzing scoring implementation details
My current focus is on the `score_target_microbatches` function. I'm dissecting the microbatch loop, particularly how `sequence_vectors` are handled with `requires_grad_(True)` when `backward` is set. This seems critical for gradient calculation during backpropagation.
### Recorded reasoning summary
Calculating loss summation
I'm examining the loss summation within the current microbatch. My focus is on ensuring that the `loss_sum` remains finite, as an `RuntimeError` is raised if it becomes non-finite. This check is crucial for stable training.
### Recorded reasoning summary
Investigating backward propagation logic
I'm digging into how the backward pass functions within the microbatch loop. Specifically, I'm tracing how `loss_sum` is calculated and if `sequence_input.grad` is indeed being populated correctly. Ensuring gradient flow here is key to my current adjustments.
### Recorded reasoning summary
Analyzing loss calculation for negatives
I'm now examining how negative items are sampled and incorporated into the loss. Specifically, I'm looking into the impact of using catalog popularity weights for negative sampling and how this might interact with the `cross_entropy` calculation and the `temperature` parameter.
### Recorded reasoning summary
Sampling negative items strategically
I'm now focused on the distribution for negative item sampling. My thinking revolves around using catalog popularity, weighted by $\alpha$ values from 0.5 to 1.0, to create a mix of uniform and popularity-based negatives. This aims to both cover the entire catalog and specifically penalize popular over-recommendations, potentially alongside true in-batch negatives.
### write_file · 2026-09-29T05:52:19.710Z
{
"instruction": "Create run_experiment.py for testing negative sampling strategies",
"file_path": "/app/solution/run_experiment.py",
"content": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n"
}
### Tool output · 2026-09-29T05:52:19.723Z
Successfully created and wrote to new file: /app/solution/run_experiment.py. Here is the updated code:
#!/usr/bin/env python3
"""Run controlled experiments on the public validation split."""
from __future__ import annotations
import argparse
import sys
import time
from pathlib import Path
from dataclasses import replace
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
sys.path.insert(0, "/app/solution")
sys.path.insert(0, "/task")
import train_current_model as trainer
import two_tower_model as base
import evaluate_public
def build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:
"""Precompute a table for ultra-fast popularity-weighted negative sampling."""
counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)
probs = np.power(counts + 1.0, power)
probs /= probs.sum()
item_counts = np.random.multinomial(table_size, probs)
table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)
np.random.shuffle(table)
return torch.from_numpy(table.astype(np.int64))
def custom_draw_negatives(
positive_items: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
uniform_count: int,
pop_count: int,
pop_table: torch.Tensor | None,
generator: torch.Generator,
device: torch.device,
) -> torch.Tensor:
batch_size = len(positive_items)
targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
total_neg = uniform_count + pop_count
negatives = np.empty((batch_size, total_neg), dtype=np.int64)
if uniform_count > 0:
negatives[:, :uniform_count] = torch.randint(
n_items, (batch_size, uniform_count), generator=generator, device="cpu"
).numpy()
if pop_count > 0 and pop_table is not None:
idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device="cpu")
negatives[:, uniform_count:] = pop_table[idx].numpy()
# Reject invalid negatives
for _ in range(32):
invalid = trainer.invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
break
num_invalid = int(invalid.sum())
if pop_table is not None:
repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device="cpu")
negatives[invalid] = pop_table[repl_idx].numpy()
else:
negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device="cpu").numpy()
return torch.from_numpy(negatives).to(device)
def score_target_microbatches_custom(
model: base.SequentialTwoTower,
prepared: trainer.SequenceData,
config: trainer.Config,
sequence_vectors: torch.Tensor,
flat_states: np.ndarray,
flat_activity: np.ndarray,
positive_items: torch.Tensor,
negatives: torch.Tensor,
sample_weights: np.ndarray,
device: torch.device,
backward: bool,
loss_type: str = "bce_unweighted",
temperature: float = 1.0,
item_logq: torch.Tensor | None = None,
) -> tuple[dict[str, float], torch.Tensor | None]:
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, _ = trainer.pool_explicit_states(
model, prepared, selected_states, device
)
counts = prepared.state_counts[selected_states]
numeric = np.zeros((end - begin, 6), dtype=np.float32)
numeric[:, :4] = flat_activity[begin:end]
numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
query_vectors = trainer.user_queries(
model,
sequence_input,
liked_vectors,
disliked_vectors,
torch.from_numpy(numeric).to(device),
)
positive_vectors, negative_vectors = trainer.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 loss_type == "bce_unweighted":
# BCE where all negatives have equal weight (no inverse propensity downweighting)
pos_loss = F.softplus(-positive_logits)
neg_loss = F.softplus(negative_logits).mean(dim=1)
loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()
elif loss_type == "bce_logq":
# BCE with logQ correction: s - log(q)
pos_lq = item_logq[positive_items[begin:end]]
neg_lq = item_logq[negatives[begin:end]]
pos_loss = F.softplus(-(positive_logits - pos_lq))
neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)
loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()
elif loss_type.startswith("softmax"):
all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)
if "logq" in loss_type and item_logq is not None:
pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)
neg_lq = item_logq[negatives[begin:end]]
all_logq = torch.cat([pos_lq, neg_lq], dim=1)
all_logits = all_logits - all_logq
scaled_logits = all_logits / temperature
labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)
loss = F.cross_entropy(scaled_logits, labels, reduction="none")
loss_sum = (selected_weights * loss).sum()
else:
raise ValueError(f"Unknown loss_type: {loss_type}")
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
## Step 43 · agent · 2026-09-29T05:52:23.741Z
### Recorded reasoning summary
Developing `run_experiment.py`
I'm now focused on completing `run_experiment.py` by integrating the training loop, ranking, and evaluation mechanisms. This involves implementing `run_training_epoch_custom`, the `train_and_evaluate` function, and defining necessary CLI arguments for execution.
### read_file · 2026-09-29T05:52:23.741Z
{
"end_line": 200,
"file_path": "/app/solution/run_experiment.py",
"start_line": 150
}
### Tool output · 2026-09-29T05:52:23.748Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 150-194 of 194 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: 195.
--- FILE CONTENT (truncated) ---
if loss_type == "bce_unweighted":
# BCE where all negatives have equal weight (no inverse propensity downweighting)
pos_loss = F.softplus(-positive_logits)
neg_loss = F.softplus(negative_logits).mean(dim=1)
loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()
elif loss_type == "bce_logq":
# BCE with logQ correction: s - log(q)
pos_lq = item_logq[positive_items[begin:end]]
neg_lq = item_logq[negatives[begin:end]]
pos_loss = F.softplus(-(positive_logits - pos_lq))
neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)
loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()
elif loss_type.startswith("softmax"):
all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)
if "logq" in loss_type and item_logq is not None:
pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)
neg_lq = item_logq[negatives[begin:end]]
all_logq = torch.cat([pos_lq, neg_lq], dim=1)
all_logits = all_logits - all_logq
scaled_logits = all_logits / temperature
labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)
loss = F.cross_entropy(scaled_logits, labels, reduction="none")
loss_sum = (selected_weights * loss).sum()
else:
raise ValueError(f"Unknown loss_type: {loss_type}")
if not bool(torch.isfinite(loss_sum)):
raise RuntimeError("training produced non-finite loss")
if backward:
(loss_sum / total_weight).backward()
if sequence_input.grad is None:
raise RuntimeError("sequence microbatch did not produce a gradient")
sequence_gradient[begin:end] = sequence_input.grad
statistics["loss_sum"] += float(loss_sum.detach().cpu())
statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
statistics["pair_correct"] += float(
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
## Step 44 · agent · 2026-09-29T05:52:53.512Z
### Recorded reasoning summary
Implementing Core Functions
I'm currently focused on integrating the remaining training and evaluation functions into `run_experiment.py`. This includes adding `run_training_epoch_custom` and `train_custom`. My next step will be to flesh out the `main()` function, ensuring it correctly handles the `--uniform-negatives` argument with its default value.
### write_file · 2026-09-29T05:52:53.512Z
{
"content": "#!/usr/bin/env python3\n\"\"\"Run controlled experiments on the public validation split.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\nfrom dataclasses import replace\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\n\nimport train_current_model as trainer\nimport two_tower_model as base\nimport evaluate_public\n\n\ndef build_popularity_table(prepared: trainer.SequenceData, power: float = 0.75, table_size: int = 500_000) -> torch.Tensor:\n \"\"\"Precompute a table for ultra-fast popularity-weighted negative sampling.\"\"\"\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n item_counts = np.random.multinomial(table_size, probs)\n table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)\n np.random.shuffle(table)\n return torch.from_numpy(table.astype(np.int64))\n\n\ndef compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:\n counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)\n probs = np.power(counts + 1.0, power)\n probs /= probs.sum()\n logq = np.log(probs).astype(np.float32)\n return torch.from_numpy(logq)\n\n\ndef custom_draw_negatives(\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n) -> torch.Tensor:\n batch_size = len(positive_items)\n targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n\n total_neg = uniform_count + pop_count\n negatives = np.empty((batch_size, total_neg), dtype=np.int64)\n\n if uniform_count > 0:\n negatives[:, :uniform_count] = torch.randint(\n n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n ).numpy()\n\n if pop_count > 0 and pop_table is not None:\n idx = torch.randint(len(pop_table), (batch_size, pop_count), generator=generator, device=\"cpu\")\n negatives[:, uniform_count:] = pop_table[idx].numpy()\n\n # Reject invalid negatives\n for _ in range(32):\n invalid = trainer.invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n break\n num_invalid = int(invalid.sum())\n if pop_table is not None:\n repl_idx = torch.randint(len(pop_table), (num_invalid,), generator=generator, device=\"cpu\")\n negatives[invalid] = pop_table[repl_idx].numpy()\n else:\n negatives[invalid] = torch.randint(n_items, (num_invalid,), generator=generator, device=\"cpu\").numpy()\n\n return torch.from_numpy(negatives).to(device)\n\n\ndef score_target_microbatches_custom(\n model: base.SequentialTwoTower,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n loss_type: str = \"bce_unweighted\",\n temperature: float = 1.0,\n item_logq: torch.Tensor | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = sequence_vectors[begin:end].detach().requires_grad_(True)\n else:\n sequence_input = sequence_vectors[begin:end]\n\n liked_vectors, disliked_vectors, _ = trainer.pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n\n query_vectors = trainer.user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = trainer.candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n\n if loss_type == \"bce_unweighted\":\n # BCE where all negatives have equal weight (no inverse propensity downweighting)\n pos_loss = F.softplus(-positive_logits)\n neg_loss = F.softplus(negative_logits).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type == \"bce_logq\":\n # BCE with logQ correction: s - log(q)\n pos_lq = item_logq[positive_items[begin:end]]\n neg_lq = item_logq[negatives[begin:end]]\n pos_loss = F.softplus(-(positive_logits - pos_lq))\n neg_loss = F.softplus(negative_logits - neg_lq).mean(dim=1)\n loss_sum = (selected_weights * (pos_loss + neg_loss) / 2.0).sum()\n elif loss_type.startswith(\"softmax\"):\n all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)\n if \"logq\" in loss_type and item_logq is not None:\n pos_lq = item_logq[positive_items[begin:end]].unsqueeze(1)\n neg_lq = item_logq[negatives[begin:end]]\n all_logq = torch.cat([pos_lq, neg_lq], dim=1)\n all_logits = all_logits - all_logq\n scaled_logits = all_logits / temperature\n labels = torch.zeros(len(positive_logits), dtype=torch.long, device=device)\n loss = F.cross_entropy(scaled_logits, labels, reduction=\"none\")\n loss_sum = (selected_weights * loss).sum()\n else:\n raise ValueError(f\"Unknown loss_type: {loss_type}\")\n\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n\n return statistics, sequence_gradient\n\n\ndef run_training_epoch_custom(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: trainer.SequenceData,\n config: trainer.Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n known_positive_packed: np.ndarray | None,\n uniform_count: int,\n pop_count: int,\n pop_table: torch.Tensor | None,\n loss_type: str,\n temperature: float,\n item_logq: torch.Tensor | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = uniform_count + pop_count\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = trainer.build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = trainer.event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n\n negatives = custom_draw_negatives(\n positive_items,\n flat_user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n uniform_count,\n pop_count,\n pop_table,\n generator,\n device,\n )\n\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches_custom(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n loss_type=loss_type,\n temperature=temperature,\n item_logq=item_logq,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 250 == 0:\n running_loss = total_loss / max(total_weight, 1.0e-12)\n trainer.log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"running_loss={running_loss:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--uniform-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-negatives\", type=int, default=16)\n parser.add_argument(\"--pop-power\", type=float, default=0.75)\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce_unweighted\", \"bce_logq\", \"softmax\", \"softmax_logq\"),\n default=\"bce_unweighted\",\n )\n parser.add_argument(\"--temperature\", type=float, default=0.2)\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"organic-strong\", \"discovery-graded\"),\n default=\"organic-strong\",\n )\n parser.add_argument(\"--epochs\", type=int, default=1)\n parser.add_argument(\"--learning-rate\", type=float, default=1e-3)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/exp_output.parquet\"))\n args = parser.parse_args()\n\n torch.set_num_threads(8)\n config = trainer.Config(\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n target_weighting=args.target_weighting,\n )\n trainer.seed_everything(config.seed)\n device = torch.device(\"cpu\")\n\n public = Path(\"/task\")\n cache_path = Path(\"/app/solution/validation_cache.npz\")\n prepared = trainer.load_cache(cache_path, \"validation\", config)\n eligibility = trainer.CandidateSet.read(public / \"data\" / \"validation_eligible_candidates.npz\")\n\n partition = trainer.chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n\n model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config)).to(device)\n optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay)\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n\n target_weights = trainer.build_target_weights(\n public,\n \"validation\",\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)\n\n pop_table = None\n if args.pop_negatives > 0:\n pop_table = build_popularity_table(prepared, power=args.pop_power)\n trainer.log(f\"built popularity table: power={args.pop_power} size={len(pop_table):,}\")\n\n item_logq = None\n if \"logq\" in args.loss_type:\n item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)\n\n for epoch in range(1, config.epochs + 1):\n diag = run_training_epoch_custom(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n partition.training_counts,\n known_positive_packed,\n args.uniform_negatives,\n args.pop_negatives,\n pop_table,\n args.loss_type,\n args.temperature,\n item_logq,\n generator,\n device,\n epoch,\n )\n trainer.log(f\"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s\")\n\n ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)\n trainer.write_ranking(ranking, args.output)\n\n # Evaluate NDCG@10\n targets_frame = pd.read_parquet(public / \"data\" / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n cand_set = evaluate_public.CandidateSet(public / \"data\" / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(cand_set, targets)\n ranked = evaluate_public.ranked_items(ranking)\n score_report = evaluate_public.score(ranked, graded)\n trainer.log(f\"VALIDATION SCORE: {score_report}\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/run_experiment.py",
"instruction": "Add complete training, ranking and evaluation logic to run_experiment.py"
}
### Tool output · 2026-09-29T05:52:53.523Z
Successfully overwrote file: /app/solution/run_experiment.py. Here is the updated code:
...
table = np.repeat(np.arange(len(prepared.item_ids)), item_counts)
np.random.shuffle(table)
return torch.from_numpy(table.astype(np.int64))
def compute_item_logq(prepared: trainer.SequenceData, power: float = 0.75) -> torch.Tensor:
counts = np.bincount(prepared.sequence_items.astype(np.int64), minlength=len(prepared.item_ids)).astype(np.float64)
probs = np.power(counts + 1.0, power)
probs /= probs.sum()
logq = np.log(probs).astype(np.float32)
return torch.from_numpy(logq)
def custom_draw_negatives(
positive_items: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
...
(positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
)
statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
return statistics, sequence_gradient
def run_training_epoch_custom(
model: base.SequentialTwoTower,
optimizer: torch.optim.Optimizer,
prepared: trainer.SequenceData,
config: trainer.Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
target_counts: np.ndarray,
known_positive_packed: np.ndarray | None,
uniform_count: int,
pop_count: int,
pop_table: torch.Tensor | None,
loss_type: str,
temperature: float,
item_logq: torch.Tensor | 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 = uniform_count + pop_count
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 = trainer.build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_counts,
np.zeros(len(users), dtype=np.int64),
start,
steps,
config,
)
sequence_outputs, _ = trainer.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)
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 = custom_draw_negatives(
positive_items,
flat_user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
uniform_count,
pop_count,
pop_table,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches_custom(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
loss_type=loss_type,
temperature=temperature,
item_logq=item_logq,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
torch.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 % 250 == 0:
running_loss = total_loss / max(total_weight, 1.0e-12)
trainer.log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"running_loss={running_loss:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
"loss": total_loss / max(total_weight, 1.0e-12),
"positive_logit": total_positive / max(total_targets, 1),
"negative_logit": total_negative / max(total_targets * negative_count, 1),
"pair_accuracy": total_pair_correct / max(total_targets * negative_count, 1),
"targets": float(total_targets),
"target_weight_sum": total_weight,
"batches": float(batches),
"seconds": time.monotonic() - started,
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--uniform-negatives", type=int, default=16)
parser.add_argument("--pop-negatives", type=int, default=16)
parser.add_argument("--pop-power", type=float, default=0.75)
parser.add_argument(
"--loss-type",
choices=("bce_unweighted", "bce_logq", "softmax", "softmax_logq"),
default="bce_unweighted",
)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument(
"--target-weighting",
choices=("organic-strong", "discovery-graded"),
default="organic-strong",
)
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--learning-rate", type=float, default=1e-3)
parser.add_argument("--output", type=Path, default=Path("/app/solution/exp_output.parquet"))
args = parser.parse_args()
torch.set_num_threads(8)
config = trainer.Config(
epochs=args.epochs,
learning_rate=args.learning_rate,
target_weighting=args.target_weighting,
)
trainer.seed_everything(config.seed)
device = torch.device("cpu")
public = Path("/task")
cache_path = Path("/app/solution/validation_cache.npz")
prepared = trainer.load_cache(cache_path, "validation", config)
eligibility = trainer.CandidateSet.read(public / "data" / "validation_eligible_candidates.npz")
partition = trainer.chronological_target_partition(
prepared,
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
model = base.SequentialTwoTower(prepared.item_features, trainer.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)
target_weights = trainer.build_target_weights(
public,
"validation",
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = trainer.build_known_positive_filter(prepared, partition.history_training_counts)
pop_table = None
if args.pop_negatives > 0:
pop_table = build_popularity_table(prepared, power=args.pop_power)
trainer.log(f"built popularity table: power={args.pop_power} size={len(pop_table):,}")
item_logq = None
if "logq" in args.loss_type:
item_logq = compute_item_logq(prepared, power=args.pop_power).to(device)
for epoch in range(1, config.epochs + 1):
diag = run_training_epoch_custom(
model,
optimizer,
prepared,
config,
target_weights,
partition.window_starts,
partition.training_counts,
known_positive_packed,
args.uniform_negatives,
args.pop_negatives,
pop_table,
args.loss_type,
args.temperature,
item_logq,
generator,
device,
epoch,
)
trainer.log(f"Epoch {epoch} finished: loss={diag['loss']:.5f} pair_acc={diag['pair_accuracy']:.4f} in {diag['seconds']:.1f}s")
ranking, _ = trainer.rank_candidates(model, prepared, eligibility, config, device, ranking_batch_size=64)
trainer.write_ranking(ranking, args.output)
# Evaluate NDCG@10
targets_frame = pd.read_parquet(public / "data" / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
cand_set = evaluate_public.CandidateSet(public / "data" / "validation_eligible_candidates.npz")
graded = evaluate_public.validation_truth(cand_set, targets)
ranked = evaluate_public.ranked_items(ranking)
score_report = evaluate_public.score(ranked, graded)
trainer.log(f"VALIDATION SCORE: {score_report}")
if __name__ == "__main__":
main()
## Step 45 · agent · 2026-09-29T05:52:57.749Z
### Recorded reasoning summary
Starting First Experiment
I'm initiating the first experimental run, focusing on the `--uniform-negatives 16 --pop-negatives 16 --pop-power 0.75 --loss-type bce_unweighted` configuration. My immediate goal is to observe the impact of these parameters on the validation score, keeping the baseline performance in mind.
### run_shell_command · 2026-09-29T05:52:57.749Z
{
"command": "python3 /app/solution/run_experiment.py \\\n --uniform-negatives 16 \\\n --pop-negatives 16 \\\n --pop-power 0.75 \\\n --loss-type bce_unweighted \\\n --target-weighting organic-strong \\\n --output /app/solution/exp1.parquet",
"description": "Run Experiment 1: 16 uniform + 16 popularity negatives with unweighted BCE"
}
### Tool output · 2026-09-29T05:57:40.578Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
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
built popularity table: power=0.75 size=500,000
epoch=1/1 batches=250 targets=987,417 running_loss=0.578803 elapsed=182.4s
Epoch 1 finished: loss=0.57662 pair_acc=0.7400 in 195.1s
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
VALIDATION SCORE: {'quality_ndcg@10': 0.021723, 'quality_users': 4263}
Process Group PGID: 1945
## Step 46 · agent · 2026-09-29T06:01:21.958Z
### Recorded reasoning summary
Improving NDCG Performance
I've observed a positive uptick in NDCG@10, now standing at 0.021723. My current focus is on further enhancing this by exploring the impact of increased popularity negatives or a higher popularity power weighting. I'll also be investigating the effects of softmax and sampling strategies next.
### Recorded reasoning summary
Evaluating Loss Functions
I'm currently dissecting the implications of cross-entropy versus BCE loss, and more critically, the impact of `discovery-graded` target weighting. The significant weighting given to repeat listens within `discovery-graded` seems problematic given their ineligibility at test time. I'm also examining the role of softmax and appropriate temperature scaling for logits.
### run_shell_command · 2026-09-29T06:01:21.958Z
{
"command": "python3 -c '\nimport pandas as pd\n\npred1 = pd.read_parquet(\"/app/solution/exp1.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\").set_index(\"item_id\")\nprint(\"Exp1 unique items predicted:\", pred1[\"item_id\"].nunique())\ntop10_1 = pred1[pred1[\"rank\"] <= 10].join(catalog, on=\"item_id\")\nprint(\"Exp1 top 10 popularity rank:\")\nprint(top10_1[\"popularity_rank\"].describe())\n'",
"description": "Inspect Exp 1 predictions and popularity rank distribution"
}
### Tool output · 2026-09-29T06:01:24.736Z
Output: Exp1 unique items predicted: 2284
Exp1 top 10 popularity rank:
count 84560.000000
mean 7597.098735
std 6991.639796
min 2.000000
25% 2035.000000
50% 6491.000000
75% 11957.000000
max 29915.000000
Name: popularity_rank, dtype: float64
Process Group PGID: 2031
## Step 47 · agent · 2026-09-29T06:05:14.018Z
### Recorded reasoning summary
Analyzing Popularity Trends
My analysis of the top 10 predicted items reveals a stark difference between the baseline and Experiment 1. The baseline shows a mean popularity rank of 2,597 and a median of 111, indicating reasonable predictions. However, Experiment 1's results are concerning, with a drastically increased mean rank of 7,597 and a median of 6,491, suggesting a significant degradation in prediction quality.
### Recorded reasoning summary
Investigating Logits Scaling
I've realized the baseline was skewed; Experiment 1 actually aligns better with the ground truth distribution for discovery relevance. Now I'm exploring Softmax loss and different temperature settings, particularly `softmax_logq`, to understand how scaling logits by `logq` affects predictions for popular versus rare items.
### Recorded reasoning summary
Adjusting Logits for Discovery
I'm revisiting the logits scaling, realizing that subtracting `logq` might be counterproductive for discovery-focused ranking at inference. My current focus is on how omitting this subtraction during training influences the learned dot products, potentially capturing PMI directly and favoring discovery over sheer popularity.
### Recorded reasoning summary
Testing Discovery Objective
I'm shifting focus to the music discovery goal: prioritizing personalized affinity (PMI) over global popularity. I'll test `target_weighting = "discovery-graded"` with `loss_type = "bce_unweighted"`, and also a `softmax` loss configuration to see how these impact predictions.
### run_shell_command · 2026-09-29T06:05:14.018Z
{
"description": "Check runtime and stats of discovery-graded target weighting",
"command": "python3 -c '\nimport sys\nsys.path.insert(0, \"/app/solution\")\nfrom train_current_model import build_target_weights, Config, load_cache, chronological_target_partition\nfrom pathlib import Path\nimport time\n\ncfg = Config(target_weighting=\"discovery-graded\")\nprepared = load_cache(Path(\"/app/solution/validation_cache.npz\"), \"validation\", cfg)\npartition = chronological_target_partition(prepared, cfg.validation_fraction, cfg.max_train_targets_per_user, cfg.max_validation_targets_per_user)\nt0 = time.time()\nweights = build_target_weights(Path(\"/task\"), \"validation\", prepared, partition.history_training_counts, cfg)\nprint(\"discovery-graded weights computed in\", round(time.time() - t0, 2), \"s\")\nprint(\"Weights mean:\", weights.mean(), \"min:\", weights.min(), \"max:\", weights.max())\n'"
}
### Tool output · 2026-09-29T06:05:25.728Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
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
discovery-graded weights computed in 7.46 s
Weights mean: 0.30997276 min: 0.25 max: 3.0
Process Group PGID: 2069
## Step 48 · agent · 2026-09-29T06:05:31.055Z
### Recorded reasoning summary
Discovering Initial Targets
My recent focus has been on the initial discovery phase. I've successfully run the `discovery-graded` weights, which completed remarkably fast at just 7.4 seconds. This process identified a substantial 46,417 grade-3 targets, assigning them a weight of 3.0. Additionally, repeat listens have been assigned a weight of 0.25. I'm now preparing to initiate Experiment 2, which will involve the `--uniform-negatives` parameter.
### run_shell_command · 2026-09-29T06:05:31.055Z
{
"description": "Run Experiment 2: 16 uniform + 16 pop negatives with discovery-graded weighting",
"command": "python3 /app/solution/run_experiment.py \\\n --uniform-negatives 16 \\\n --pop-negatives 16 \\\n --pop-power 0.75 \\\n --loss-type bce_unweighted \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp2.parquet"
}
### Tool output · 2026-09-29T06:10:37.977Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
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
built popularity table: power=0.75 size=500,000
epoch=1/1 batches=250 targets=987,417 running_loss=0.578532 elapsed=198.7s
Epoch 1 finished: loss=0.57588 pair_acc=0.7478 in 211.0s
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
VALIDATION SCORE: {'quality_ndcg@10': 0.02402, 'quality_users': 4263}
Process Group PGID: 2095
## Step 49 · agent · 2026-09-29T06:22:24.159Z
### Recorded reasoning summary
Observing Validation Improvements
My validation NDCG@10 metric has seen a significant boost, climbing to 0.024020 from 0.021280, a nearly 13% relative gain. Concurrently, the pair accuracy reached 0.7478. This positive shift seems linked to the implementation of `discovery-graded` target weighting.
### Recorded reasoning summary
Testing Negative Sampling
I'm now focusing on refining negative sampling strategies. My current plan is to investigate different popularity weighting exponents ($\alpha$) and alter the mix of uniform versus popularity-biased negatives. I'll also analyze the computational cost of increasing the total number of negatives.
### Recorded reasoning summary
Evaluating Softmax Temperatures
I'm exploring the impact of `softmax` loss with varying temperature parameters. Initial logit scale analysis suggests temperatures around 0.15-0.25 might be optimal for achieving good separation between positive and negative examples within the sampled batch. I will proceed with testing $\tau = 0.2$ and $\tau = 0.1$.
### run_shell_command · 2026-09-29T06:22:24.159Z
{
"command": "python3 /app/solution/run_experiment.py \\\n --uniform-negatives 16 \\\n --pop-negatives 16 \\\n --pop-power 0.75 \\\n --loss-type softmax \\\n --temperature 0.2 \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp3_softmax_t02.parquet",
"description": "Run Experiment 3: Softmax loss with temperature 0.2 and 16 uniform + 16 pop negatives"
}
### Tool output · 2026-09-29T06:27:14.261Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
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
built popularity table: power=0.75 size=500,000
epoch=1/1 batches=250 targets=987,417 running_loss=2.947044 elapsed=182.5s
Epoch 1 finished: loss=2.93405 pair_acc=0.7597 in 194.8s
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
VALIDATION SCORE: {'quality_ndcg@10': 0.026195, 'quality_users': 4263}
Process Group PGID: 2225
## Step 50 · agent · 2026-09-29T06:27:43.871Z
### Recorded reasoning summary
Improving NDCG@10 Score
My recent efforts have significantly boosted NDCG@10 to 0.026195, a substantial 23.1% relative increase from 0.021280. This advancement was achieved with a training and ranking time of approximately 255 seconds, while also maintaining a pair accuracy of 0.7597.
### Recorded reasoning summary
Tuning for Higher Scores
I'm analyzing various parameters to further improve the NDCG@10 score. Specifically, I'm exploring optimal softmax temperature values, different negative sampling configurations (varying `pop_power` and the number/mix of uniform and popularity negatives), and the impact of learning rate and epoch count on performance, keeping the 60-minute time limit in mind.
### Recorded reasoning summary
Refining Hyperparameters Rapidly
With only 25 minutes remaining, I'm prioritizing 2-3 final validation experiments. My current focus is on optimizing softmax temperature and negative sampling strategies, specifically exploring a lower temperature of 0.15 and adjusting the mix of uniform and popularity-based negatives, considering the skewed nature of catalog popularity.
### run_shell_command · 2026-09-29T06:27:43.871Z
{
"command": "python3 /app/solution/run_experiment.py \\\n --uniform-negatives 16 \\\n --pop-negatives 16 \\\n --pop-power 0.75 \\\n --loss-type softmax \\\n --temperature 0.15 \\\n --target-weighting discovery-graded \\\n --output /app/solution/exp4_t015.parquet",
"description": "Run Experiment 4: Softmax loss with temperature 0.15"
}
### Tool output · 2026-09-29T06:32:34.062Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
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
built popularity table: power=0.75 size=500,000
epoch=1/1 batches=250 targets=987,417 running_loss=2.950549 elapsed=182.0s
Epoch 1 finished: loss=2.93730 pair_acc=0.7597 in 194.1s
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
VALIDATION SCORE: {'quality_ndcg@10': 0.023909, 'quality_users': 4263}
Process Group PGID: 2289
## Step 51 · agent · 2026-09-29T06:32:39.682Z
### Recorded reasoning summary
Adjusting Hyperparameters
My exploration into temperature values has yielded an interesting outcome: a slightly higher temperature of 0.20 resulted in a marginal improvement, achieving 0.026195 compared to 0.15's 0.023909. This suggests that the lower temperature may have over-emphasized hard negatives, negatively impacting overall quality. I'm now considering further adjustments.
## Recorded session usage
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