yambda-discovery-ranking-two-tower-case-1 / opus-high-2.1.251-exact-r01-2026-09-24
Recorded model: claude-opus-5
## Step 1 · user · 2026-09-24T17:36:41.257Z
# Improve a production music-discovery two-tower
You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team's next launch gate.
Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.
The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.
## Production Context
The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.
The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.
## What You Have
All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` window. For the final submission, that
validation window becomes observed history, and another 30-minute gap precedes
the hidden seven-day outcome window.
The package includes:
- `/task/current_model/train_current_model.py`: the production training and
ranking script. It can reproduce the public validation ranking and produce a
hidden-test submission after refitting.
- `/task/current_model/two_tower_model.py`: model primitives used by the
production script.
- `/task/evaluate_public.py`: the public validation evaluator for development.
- `/task/data/validation_eligible_candidates.npz` and
`/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for
the validation and hidden-test cutoffs.
- `/task/data/validation_preference_state.parquet` and
`/task/data/test_preference_state.parquet`: active explicit preference state
at the corresponding cutoffs.
- `/task/requirements.txt`: the exact offline Python distribution inventory.
You can reproduce the production public backtest with:
```sh
python /task/current_model/train_current_model.py \
--stage validation \
--public-dir /task \
--output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet
```
## Data
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action came
through a recommendation surface. A listen is a conventional positive at 50%
completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. For model development and selection, train only from `train_*` and score the
subsequent `val_*` outcomes.
2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
Validation candidates exclude all `train_*` encounters. Test candidates exclude
all `train_*` and `val_*` encounters. Future test interactions are hidden and
used only by the separate Harbor verifier.
## Discovery Objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.
## Deliverable
Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.
The ranking must:
- include the columns `uid`, `item_id`, and `rank`; only these columns are
evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
candidate set.
Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model-provider
connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
the deadline.
### Harness context
{
"type": "deferred_tools_delta",
"addedNames": [
"CronCreate",
"CronDelete",
"CronList",
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"removedNames": [],
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"needsAuthMcpServers": [],
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### Harness context
{
"type": "agent_listing_delta",
"addedTypes": [
"claude",
"Explore",
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"addedLines": [
"- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)",
"- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)",
"- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)",
"- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)",
"- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)"
],
"removedTypes": [],
"isInitial": true,
"showConcurrencyNote": false
}
### Harness context
{
"type": "skill_listing",
"content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch",
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### Harness context
{
"type": "total_tokens_reminder",
"text": "15000000 tokens left"
}
## Step 2 · agent · 2026-09-24T17:36:42.594Z
I'll start by investigating the production model.
### Bash · 2026-09-24T17:36:43.594Z
{
"command": "ls -la /task/ /task/current_model/ /task/data/ && wc -l /task/current_model/*.py /task/evaluate_public.py",
"description": "List task files"
}
### Tool output · 2026-09-24T17:36:43.655Z
/task/:
total 40
dr-xr-xr-x 1 root root 4096 Sep 24 17:32 .
drwxr-xr-x 1 root root 4096 Sep 24 17:35 ..
-r--r--r-- 1 root root 0 Sep 24 17:30 .keep
dr-xr-xr-x 1 root root 4096 Sep 24 17:30 current_model
dr-xr-xr-x 1 root root 4096 Sep 24 17:30 data
-r--r--r-- 1 root root 5774 Sep 24 17:30 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 17:30 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 24 17:30 requirements.txt
/task/current_model/:
total 104
dr-xr-xr-x 1 root root 4096 Sep 24 17:30 .
dr-xr-xr-x 1 root root 4096 Sep 24 17:32 ..
-r--r--r-- 1 root root 80058 Sep 24 17:30 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 24 17:30 two_tower_model.py
/task/data/:
total 347392
dr-xr-xr-x 1 root root 4096 Sep 24 17:30 .
dr-xr-xr-x 1 root root 4096 Sep 24 17:32 ..
-r--r--r-- 1 root root 349352 Sep 24 17:30 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 24 17:30 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 24 17:30 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 24 17:30 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 24 17:30 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 24 17:30 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 24 17:30 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 24 17:30 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 24 17:30 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 24 17:30 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 24 17:30 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 24 17:30 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 24 17:30 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 24 17:30 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 24 17:30 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 24 17:30 validation_preference_state.parquet
2164 /task/current_model/train_current_model.py
433 /task/current_model/two_tower_model.py
157 /task/evaluate_public.py
2754 total
### Bash · 2026-09-24T17:36:44.271Z
{
"command": "nproc; free -g; python -c \"import torch; print(torch.__version__)\"",
"description": "Check CPU resources"
}
### Tool output · 2026-09-24T17:36:47.265Z
8
total used free shared buff/cache available
Mem: 15 1 0 0 13 14
Swap: 0 0 0
2.8.0+cpu
### Harness context
{
"type": "total_tokens_reminder",
"text": "14981196 tokens left"
}
## Step 3 · agent · 2026-09-24T17:36:48.786Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:36:49.775Z
{
"command": "mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && ls -la /app/solution/",
"description": "Copy model source"
}
### Tool output · 2026-09-24T17:36:49.800Z
total 108
drwxr-xr-x 1 agent agent 4096 Sep 24 17:36 .
drwxr-xr-x 1 agent agent 4096 Sep 24 17:32 ..
-r--r--r-- 1 agent agent 80058 Sep 24 17:36 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 24 17:36 two_tower_model.py
### Bash · 2026-09-24T17:36:50.325Z
{
"command": "sed -n 1,120p /task/evaluate_public.py",
"description": "Read evaluator"
}
### Tool output · 2026-09-24T17:36:50.341Z
#!/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"
)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14979657 tokens left"
}
## Step 4 · agent · 2026-09-24T17:36:51.833Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:36:52.895Z
{
"command": "grep -n \"^def \\|^class \\|^# ---\\|^ def \" /task/current_model/train_current_model.py | head -120",
"description": "Outline trainer"
}
### Tool output · 2026-09-24T17:36:52.914Z
41:class Config:
76:class CandidateSet:
82: def read(cls, path: Path) -> "CandidateSet":
99:class SequenceData:
118: def lengths(self) -> np.ndarray:
122: def target_count(self) -> int:
127:class TargetPartition:
136: def selected_count(self) -> int:
140:def log(message: str) -> None:
144:def seed_everything(seed: int) -> None:
150:def model_config(config: Config) -> base.TowerConfig:
174:def append_state_snapshot(
190:def causal_activity_features(
247:def build_sequence_parts(
314:def final_user_features(
341:def prepare_data(
471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
481:def save_cache(
493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
511:def load_or_prepare(
534:def build_item_correction(
578:def build_known_positive_filter(
609:def invalid_full_history_negatives(
630:def draw_full_history_filtered_negatives(
724:def draw_training_negatives(
763:def event_sequence(
774:def pool_explicit_states(
798:def user_queries(
813:def candidate_vectors(
828:def history_windows(
845:def corrected_negative_loss(
867:def retained_target_window_starts(
888:def chronological_target_partition(
914:def load_organic_like_outcomes(
955:def build_target_weights(
1035:def build_dense_block(
1111:def score_target_microbatches(
1198:def release_accelerator_cache(device: torch.device) -> None:
1203:def run_training_epoch(
1364:def evaluate_validation_bce(
1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
1495:def save_epoch_checkpoint(
1521:def train_model(
1782:def inference_queries(
1861:def rank_candidates(
1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
1922:def main() -> int:
### Bash · 2026-09-24T17:36:53.578Z
{
"command": "grep -n \"^def \\|^class \\|^ def \" /task/current_model/two_tower_model.py",
"description": "Outline model"
}
### Tool output · 2026-09-24T17:36:53.595Z
27:class TowerConfig:
49:class ListenEvents:
57:class ExplicitEvents:
65:def log(message: str) -> None:
69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
106:def iter_listen_users(
157:def merge_listen_streams(
175:def load_explicit_events(
228:def empty_explicit_events() -> ExplicitEvents:
238:def pack_active_items(
251:def load_inference_state(
284:class SequentialTwoTower(nn.Module):
287: def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
332: def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
339: def encode_items(self, items: torch.Tensor) -> torch.Tensor:
346:def invalid_negative_mask(
363:def draw_filtered_mixed_negatives(
### Harness context
{
"type": "total_tokens_reminder",
"text": "14977476 tokens left"
}
## Step 5 · agent · 2026-09-24T17:36:55.492Z
### Bash · 2026-09-24T17:36:55.492Z
{
"command": "sed -n 1,180p /task/current_model/train_current_model.py",
"description": "Read config"
}
### Tool output · 2026-09-24T17:36:55.506Z
#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.
Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss. The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split. A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
into the next block.
The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations
import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F
import two_tower_model as base
CACHE_VERSION = 2
@dataclass(frozen=True)
class Config:
bptt_steps: int = 100
user_batch_size: int = 64
embedding_dim: int = 192
hidden_dim: int = 192
user_hidden_1: int = 512
user_hidden_2: int = 256
item_hidden_1: int = 512
item_hidden_2: int = 256
dropout: float = 0.05
epochs: int = 1
learning_rate: float = 1.0e-3
weight_decay: float = 1.0e-5
gradient_clip: float = 2.0
loss_microbatch_size: int = 65_536
uniform_negatives: int = 24
inbatch_negatives: int = 8
inbatch_correction_power: float = 0.5
inbatch_correction_min: float = 0.25
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
ordinary_target_weight: float = 0.25
organic_strong_target_weight: float = 1.0
organic_like_target_weight: float = 3.0
discovery_horizon_days: int = 7
validation_fraction: float = 0.10
max_train_targets_per_user: int = 128
max_validation_targets_per_user: int = 16
early_stopping_patience: int = 1
early_stopping_min_delta: float = 1.0e-4
seed: int = 29
@dataclass(frozen=True)
class CandidateSet:
uids: np.ndarray
item_ids: np.ndarray
packed: np.ndarray
@classmethod
def read(cls, path: Path) -> "CandidateSet":
with np.load(path, allow_pickle=False) as artifact:
result = cls(
uids=artifact["uids"].astype(np.int64, copy=True),
item_ids=artifact["item_ids"].astype(np.int64, copy=True),
packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
)
expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
if result.packed.shape != expected:
raise ValueError(
f"invalid candidate artifact shape {result.packed.shape}; "
f"expected {expected}"
)
return result
@dataclass(frozen=True)
class SequenceData:
item_ids: np.ndarray
item_features: np.ndarray
user_ids: np.ndarray
offsets: np.ndarray
sequence_timestamps: np.ndarray
sequence_items: np.ndarray
event_features: np.ndarray
activity_features: np.ndarray
state_ids: np.ndarray
liked_states: np.ndarray
disliked_states: np.ndarray
state_counts: np.ndarray
inference_user_rows: np.ndarray
inference_liked_items: np.ndarray
inference_disliked_items: np.ndarray
inference_user_features: np.ndarray
@property
def lengths(self) -> np.ndarray:
return np.diff(self.offsets)
@property
def target_count(self) -> int:
return int(np.maximum(self.lengths - 1, 0).sum())
@dataclass(frozen=True)
class TargetPartition:
"""A contiguous recent target window and its chronological BCE split."""
window_starts: np.ndarray
training_counts: np.ndarray
validation_counts: np.ndarray
history_training_counts: np.ndarray
@property
def selected_count(self) -> int:
return int(self.training_counts.sum() + self.validation_counts.sum())
def log(message: str) -> None:
print(message, file=sys.stderr, flush=True)
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def model_config(config: Config) -> base.TowerConfig:
"""Map the dense trainer configuration onto the unchanged tower."""
return base.TowerConfig(
sequence_length=config.bptt_steps,
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim,
user_hidden_1=config.user_hidden_1,
user_hidden_2=config.user_hidden_2,
item_hidden_1=config.item_hidden_1,
item_hidden_2=config.item_hidden_2,
dropout=config.dropout,
epochs=config.epochs,
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
gradient_clip=config.gradient_clip,
uniform_negatives=config.uniform_negatives,
inbatch_negatives=config.inbatch_negatives,
inbatch_correction_power=config.inbatch_correction_power,
inbatch_correction_min=config.inbatch_correction_min,
inbatch_correction_max=config.inbatch_correction_max,
seed=config.seed,
)
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]],
### Bash · 2026-09-24T17:36:56.060Z
{
"command": "sed -n 280,433p /task/current_model/two_tower_model.py",
"description": "Read model class"
}
### Tool output · 2026-09-24T17:36:56.075Z
destination[row, : len(retained)] = retained.astype(np.uint16)
return liked, disliked, like_counts, dislike_counts
class SequentialTwoTower(nn.Module):
"""Order-sensitive user tower and independent catalog-aware item tower."""
def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
super().__init__()
n_items, n_item_features = item_features.shape
dim = config.embedding_dim
self.n_items = n_items
self.context_embedding = nn.Embedding(
n_items + 1, dim, padding_idx=n_items
)
self.event_projection = nn.Sequential(
nn.Linear(dim + 3, config.hidden_dim),
nn.GELU(),
)
self.sequence_encoder = nn.GRU(
input_size=config.hidden_dim,
hidden_size=dim,
num_layers=1,
batch_first=True,
)
self.user_tower = nn.Sequential(
nn.Linear(dim * 3 + 6, config.user_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_1, config.user_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.user_hidden_2, dim),
)
self.candidate_embedding = nn.Embedding(n_items, dim)
self.register_buffer(
"item_features", torch.from_numpy(item_features), persistent=True
)
self.item_tower = nn.Sequential(
nn.Linear(dim + n_item_features, config.item_hidden_1),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_1, config.item_hidden_2),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.item_hidden_2, dim),
)
nn.init.normal_(self.context_embedding.weight, std=0.02)
nn.init.normal_(self.candidate_embedding.weight, std=0.02)
with torch.no_grad():
self.context_embedding.weight[n_items].zero_()
def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
mask = items.ne(self.n_items)
vectors = self.context_embedding(items)
return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
dim=1, keepdim=True
).clamp_min(1)
def encode_items(self, items: torch.Tensor) -> torch.Tensor:
inputs = torch.cat(
[self.candidate_embedding(items), self.item_features[items]], dim=1
)
return self.item_tower(inputs)
def invalid_negative_mask(
negatives: torch.Tensor,
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
) -> torch.Tensor:
"""Identify targets, known positives, active likes, and row duplicates."""
invalid = negatives.eq(targets[:, None])
invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= negatives[:, column, None].eq(
negatives[:, :column]
).any(dim=1)
return invalid
def draw_filtered_mixed_negatives(
targets: torch.Tensor,
context_items: torch.Tensor,
liked_items: torch.Tensor,
n_items: int,
uniform_count: int,
inbatch_count: int,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw uniform and unique-target in-batch negatives."""
batch_size = len(targets)
uniform = torch.randint(
n_items,
(batch_size, uniform_count),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
corrected_count = 0
if inbatch_count:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
-1.0,
)
columns = torch.topk(priorities, k=corrected_count, dim=1).indices
inbatch = unique_targets[columns]
if corrected_count < inbatch_count:
filler = torch.randint(
n_items,
(batch_size, inbatch_count - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives_cpu = torch.cat([uniform, inbatch], dim=1)
correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
if corrected_count:
correction_cpu[
:, uniform_count : uniform_count + corrected_count
] = item_correction[inbatch[:, :corrected_count]]
negatives = negatives_cpu.to(device)
correction = correction_cpu.to(device)
for _ in range(32):
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if not bool(invalid.any()):
return negatives, correction
replacements = torch.randint(
n_items,
(int(invalid.sum().detach().cpu()),),
generator=generator,
device="cpu",
).to(device)
negatives[invalid] = replacements
correction[invalid] = 1.0
invalid = invalid_negative_mask(
negatives, targets, context_items, liked_items
)
if bool(invalid.any()):
raise RuntimeError("mixed negative resampling failed false-negative filter")
return negatives, correction
### Harness context
{
"type": "total_tokens_reminder",
"text": "14976141 tokens left"
}
## Step 6 · agent · 2026-09-24T17:36:58.382Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:37:00.037Z
{
"command": "cd /app/solution && chmod u+w *.py && nohup python train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet > /app/solution/baseline.log 2>&1 &\necho started",
"description": "Start baseline run",
"run_in_background": false
}
### Tool output · 2026-09-24T17:37:00.057Z
started
### Bash · 2026-09-24T17:37:00.743Z
{
"command": "sed -n 1922,2164p /task/current_model/train_current_model.py",
"description": "Read main"
}
### Tool output · 2026-09-24T17:37:00.758Z
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())
### Harness context
{
"type": "total_tokens_reminder",
"text": "14971150 tokens left"
}
## Step 7 · agent · 2026-09-24T17:37:02.339Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:37:03.064Z
{
"command": "sed -n 534,900p /task/current_model/train_current_model.py",
"description": "Read negative sampling"
}
### Tool output · 2026-09-24T17:37:03.078Z
def build_item_correction(
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""Estimate in-batch propensities from the selected training targets."""
counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
for user_row, count in enumerate(target_counts):
if not count:
continue
offset = int(prepared.offsets[user_row])
target_start = int(target_starts[user_row])
counts += np.bincount(
prepared.sequence_items[
offset
+ target_start
+ 1 : offset
+ target_start
+ int(count)
+ 1
].astype(np.int64),
minlength=len(prepared.item_ids),
)
total = float(counts.sum())
probabilities = (counts + 1.0) / (total + len(counts))
raw = np.power(
(1.0 / len(counts)) / probabilities,
config.inbatch_correction_power,
)
normalization = float(np.sum(counts * raw) / max(total, 1.0))
correction = np.clip(
raw / max(normalization, 1.0e-12),
config.inbatch_correction_min,
config.inbatch_correction_max,
).astype(np.float32)
mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
log(
f"inbatch correction: min={correction.min():.3f} "
f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
)
return torch.from_numpy(correction)
def build_known_positive_filter(
prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
"""Pack each user's complete public-training positive-item set."""
width = (len(prepared.item_ids) + 7) // 8
packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
retained = 0
for user_row, target_count in enumerate(training_counts):
if not target_count:
continue
offset = int(prepared.offsets[user_row])
# T adjacent targets consume sequence positions 0 through T. The
# chronological BCE suffix starts at T + 1 and is intentionally absent.
items = np.unique(
prepared.sequence_items[
offset : offset + int(target_count) + 1
].astype(np.int64)
)
byte_columns = items >> 3
bit_values = np.left_shift(
np.uint8(1), (items & 7).astype(np.uint8)
)
np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
retained += len(items)
log(
f"full-history false-negative filter: users={len(prepared.user_ids):,} "
f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
)
return packed
def invalid_full_history_negatives(
negatives: np.ndarray,
targets: np.ndarray,
user_rows: np.ndarray,
known_positive_packed: np.ndarray,
liked_items: np.ndarray,
) -> np.ndarray:
"""Return row-level invalid negatives using the complete training prefix."""
byte_columns = negatives >> 3
bit_columns = negatives & 7
known = known_positive_packed[user_rows[:, None], byte_columns]
invalid = ((known >> bit_columns) & 1).astype(bool)
invalid |= negatives == targets[:, None]
invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
for column in range(1, negatives.shape[1]):
invalid[:, column] |= (
negatives[:, column, None] == negatives[:, :column]
).any(axis=1)
return invalid
def draw_full_history_filtered_negatives(
targets: torch.Tensor,
user_rows: np.ndarray,
liked_items: torch.Tensor,
known_positive_packed: np.ndarray,
n_items: int,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Draw mixed negatives and reject all known public-training positives."""
targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
batch_size = len(targets_cpu)
uniform = torch.randint(
n_items,
(batch_size, config.uniform_negatives),
generator=generator,
device="cpu",
)
inbatch = torch.empty((batch_size, 0), dtype=torch.long)
corrected_count = 0
if config.inbatch_negatives:
unique_targets = torch.unique(targets.detach().cpu())
corrected_count = min(
config.inbatch_negatives, max(len(unique_targets) - 1, 0)
)
if corrected_count:
priorities = torch.rand(
(batch_size, len(unique_targets)), generator=generator
)
priorities.masked_fill_(
unique_targets[None, :].eq(
torch.from_numpy(targets_cpu)[:, None]
),
-1.0,
)
columns = torch.topk(
priorities, k=corrected_count, dim=1
).indices
inbatch = unique_targets[columns]
if corrected_count < config.inbatch_negatives:
filler = torch.randint(
n_items,
(batch_size, config.inbatch_negatives - corrected_count),
generator=generator,
device="cpu",
)
inbatch = torch.cat([inbatch, filler], dim=1)
negatives = torch.cat([uniform, inbatch], dim=1).numpy()
correction = np.ones_like(negatives, dtype=np.float32)
if corrected_count:
correction[
:, config.uniform_negatives : config.uniform_negatives + corrected_count
] = item_correction[inbatch[:, :corrected_count]].numpy()
for _ in range(32):
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if not np.any(invalid):
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
replacements = torch.randint(
n_items,
(int(invalid.sum()),),
generator=generator,
device="cpu",
).numpy()
negatives[invalid] = replacements
# Rejected in-batch items become ordinary uniform draws.
correction[invalid] = 1.0
invalid = invalid_full_history_negatives(
negatives,
targets_cpu,
user_rows,
known_positive_packed,
liked_cpu,
)
if np.any(invalid):
raise RuntimeError("full-history negative resampling failed")
return (
torch.from_numpy(negatives).to(device),
torch.from_numpy(correction).to(device),
)
def draw_training_negatives(
positive_items: torch.Tensor,
known_history: torch.Tensor,
liked_items: torch.Tensor,
user_rows: np.ndarray,
known_positive_packed: np.ndarray | None,
prepared: SequenceData,
config: Config,
item_correction: torch.Tensor,
generator: torch.Generator,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
if config.negative_filter_scope == "full-training-history":
if known_positive_packed is None:
raise RuntimeError("full-history negative filter was not built")
return draw_full_history_filtered_negatives(
positive_items,
user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
config,
item_correction,
generator,
device,
)
return base.draw_filtered_mixed_negatives(
positive_items,
known_history,
liked_items,
len(prepared.item_ids),
config.uniform_negatives,
config.inbatch_negatives,
item_correction,
generator,
device,
)
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)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14967556 tokens left"
}
## Step 8 · agent · 2026-09-24T17:37:09.423Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:37:10.309Z
{
"command": "sed -n 1035,1200p /task/current_model/train_current_model.py",
"description": "Read dense block"
}
### Tool output · 2026-09-24T17:37:10.323Z
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()
### Bash · 2026-09-24T17:37:10.902Z
{
"command": "tail -5 /app/solution/baseline.log",
"description": "Check baseline log"
}
### Tool output · 2026-09-24T17:37:10.914Z
loaded explicit events: rows=720,270 users=8,220
### Harness context
{
"type": "total_tokens_reminder",
"text": "14962018 tokens left"
}
## Step 9 · agent · 2026-09-24T17:37:12.524Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:37:13.201Z
{
"command": "sed -n 1203,1370p /task/current_model/train_current_model.py",
"description": "Read training epoch"
}
### Tool output · 2026-09-24T17:37:13.220Z
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,
### Harness context
{
"type": "total_tokens_reminder",
"text": "14959332 tokens left"
}
## Step 10 · agent · 2026-09-24T17:37:15.223Z
### Bash · 2026-09-24T17:37:15.223Z
{
"command": "sed -n 1521,1790p /task/current_model/train_current_model.py",
"description": "Read train_model"
}
### Tool output · 2026-09-24T17:37:15.242Z
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
### Harness context
{
"type": "total_tokens_reminder",
"text": "14957028 tokens left"
}
## Step 11 · agent · 2026-09-24T17:37:24.321Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:37:25.100Z
{
"command": "sleep 60; tail -3 /app/solution/baseline.log; ls -la /app/solution/",
"description": "Check progress"
}
### Tool output · 2026-09-24T17:38:25.124Z
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
total 112
drwxr-xr-x 1 agent agent 4096 Sep 24 17:37 .
drwxr-xr-x 1 agent agent 4096 Sep 24 17:32 ..
-rw-r--r-- 1 agent agent 1159 Sep 24 17:37 baseline.log
-rw-r--r-- 1 agent agent 80058 Sep 24 17:36 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 24 17:36 two_tower_model.py
### Harness context
{
"type": "total_tokens_reminder",
"text": "14952947 tokens left"
}
## Step 12 · agent · 2026-09-24T17:38:28.009Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:38:29.017Z
{
"command": "sed -n 471,534p /task/current_model/train_current_model.py; echo ---; sed -n 1861,1922p /task/current_model/train_current_model.py",
"description": "Read cache and ranking"
}
### Tool output · 2026-09-24T17:38:29.077Z
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(
---
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:
### Harness context
{
"type": "total_tokens_reminder",
"text": "14952379 tokens left"
}
## Step 13 · agent · 2026-09-24T17:38:30.825Z
### Bash · 2026-09-24T17:38:30.825Z
{
"command": "tail -4 /app/solution/baseline.log",
"description": "Check progress"
}
### Tool output · 2026-09-24T17:38:30.836Z
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
### Harness context
{
"type": "total_tokens_reminder",
"text": "14950357 tokens left"
}
## Step 14 · agent · 2026-09-24T17:39:22.259Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:39:23.023Z
{
"command": "tail -3 /app/solution/baseline.log; date",
"description": "Check progress"
}
### Tool output · 2026-09-24T17:39:23.064Z
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
Thu Sep 24 17:39:23 UTC 2026
### Bash · 2026-09-24T17:39:23.617Z
{
"command": "sed -n 1364,1490p /task/current_model/train_current_model.py",
"description": "Read validation bce"
}
### Tool output · 2026-09-24T17:39:23.633Z
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()
### Harness context
{
"type": "total_tokens_reminder",
"text": "14946275 tokens left"
}
## Step 15 · agent · 2026-09-24T17:40:18.535Z
### Thinking unavailable — no text recorded
Baseline is training. Let me now write the improved version while it runs.
### Bash · 2026-09-24T17:40:19.482Z
{
"command": "tail -2 /app/solution/baseline.log",
"description": "Check progress"
}
### Tool output · 2026-09-24T17:40:19.499Z
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
### Harness context
{
"type": "total_tokens_reminder",
"text": "14940594 tokens left"
}
## Step 16 · agent · 2026-09-24T17:40:21.783Z
### Thinking unavailable — no text recorded
### Edit · 2026-09-24T17:40:23.820Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5",
"new_string": " uniform_negatives: int = 8\n popularity_negatives: int = 16\n popularity_sampling_power: float = 0.75\n logq_correction: float = 1.0\n loss_objective: str = \"sampled-softmax\"\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5"
}
### Tool output · 2026-09-24T17:40:23.836Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14940150 tokens left"
}
## Step 17 · agent · 2026-09-24T17:40:26.087Z
### Thinking unavailable — no text recorded
### Edit · 2026-09-24T17:40:28.687Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": "def build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)",
"new_string": "def selected_target_item_counts(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Count how often each catalog item appears as a selected training target.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)"
}
### Tool output · 2026-09-24T17:40:28.708Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14939632 tokens left"
}
## Step 18 · agent · 2026-09-24T17:40:31.916Z
### Edit · 2026-09-24T17:40:31.916Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())",
"new_string": " + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n return counts\n\n\ndef build_item_correction(\n counts: np.ndarray,\n config: Config,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n total = float(counts.sum())"
}
### Tool output · 2026-09-24T17:40:31.953Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14939246 tokens left"
}
## Step 19 · agent · 2026-09-24T17:40:33.769Z
### Thinking unavailable — no text recorded
### Edit · 2026-09-24T17:40:43.987Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(",
"new_string": " return torch.from_numpy(correction)\n\n\ndef build_negative_sampler(\n counts: np.ndarray,\n config: Config,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Build the popularity proposal and the mixture log-propensity table.\n\n The production sampler drew every non-in-batch negative uniformly from the\n 30,000-track catalog. Listening is heavily head-skewed, so a uniform draw\n almost never lands on a track that actually competes for a top-10 slot: the\n softmax partition function is dominated by head items the sampler rarely\n sees. This builds a word2vec-style ``count ** power`` proposal so that the\n dominant terms are sampled often, together with the exact mixture\n propensity ``q`` of the combined uniform + popularity + in-batch draw so the\n sampled-softmax estimator stays unbiased for the full-catalog softmax.\n\n Args:\n counts: Selected-target occurrence count per catalog item.\n config: Trainer configuration supplying the sampler mixture.\n\n Returns:\n ``(popularity_probs, log_q)``: the proposal used for popularity draws\n and ``log q(i)`` for the full mixture, both over the catalog.\n \"\"\"\n n_items = len(counts)\n total = float(counts.sum())\n empirical = (counts + 1.0) / (total + n_items)\n weights = np.power(counts + 1.0, config.popularity_sampling_power)\n popularity = weights / weights.sum()\n draws = (\n config.uniform_negatives\n + config.popularity_negatives\n + config.inbatch_negatives\n )\n mixture = (\n config.uniform_negatives * (1.0 / n_items)\n + config.popularity_negatives * popularity\n + config.inbatch_negatives * empirical\n ) / max(draws, 1)\n log_q = np.log(np.maximum(mixture, 1.0e-12)).astype(np.float32)\n head = int(np.argmax(counts))\n log(\n f\"negative sampler: uniform={config.uniform_negatives} \"\n f\"popularity={config.popularity_negatives} \"\n f\"inbatch={config.inbatch_negatives} \"\n f\"power={config.popularity_sampling_power} \"\n f\"q_head={mixture[head]:.3e} q_min={mixture.min():.3e} \"\n f\"logq_beta={config.logq_correction}\"\n )\n return (\n torch.from_numpy(popularity.astype(np.float64)),\n torch.from_numpy(log_q),\n )\n\n\ndef build_known_positive_filter("
}
### Tool output · 2026-09-24T17:40:44.012Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14938095 tokens left"
}
## Step 20 · agent · 2026-09-24T17:40:45.282Z
Now the negative draw function:
### Edit · 2026-09-24T17:40:51.760Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)",
"new_string": " known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n if config.popularity_negatives:\n popular = torch.multinomial(\n popularity_probs,\n batch_size * config.popularity_negatives,\n replacement=True,\n generator=generator,\n ).reshape(batch_size, config.popularity_negatives)\n uniform = torch.cat([uniform, popular], dim=1)\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)"
}
### Tool output · 2026-09-24T17:40:51.803Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14937173 tokens left"
}
## Step 21 · agent · 2026-09-24T17:40:54.564Z
### Thinking unavailable — no text recorded
### Edit · 2026-09-24T17:40:57.442Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()",
"new_string": " sampled_columns = uniform.shape[1]\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, sampled_columns : sampled_columns + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()"
}
### Tool output · 2026-09-24T17:40:57.469Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14936574 tokens left"
}
## Step 22 · agent · 2026-09-24T17:40:59.063Z
Now update `draw_training_negatives` and the BCE fallback guard:
### Edit · 2026-09-24T17:41:04.252Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(",
"new_string": " config: Config,\n item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n popularity_probs,\n generator,\n device,\n )\n if config.popularity_negatives:\n raise RuntimeError(\n \"popularity negatives require the full-training-history filter\"\n )\n return base.draw_filtered_mixed_negatives("
}
### Tool output · 2026-09-24T17:41:04.311Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14935823 tokens left"
}
## Step 23 · agent · 2026-09-24T17:41:05.544Z
Now the loss itself:
### Edit · 2026-09-24T17:41:13.673Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": "def corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives",
"new_string": "def sampled_softmax_loss(\n positive_logits: torch.Tensor,\n negative_logits: torch.Tensor,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n log_q: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n \"\"\"Per-target sampled-softmax cross entropy against the full catalog.\n\n The importance-sampled partition function is\n ``exp(s_pos) + sum_k exp(s_k) / (M q_k)``, so each negative logit is shifted\n by ``-(log M + beta * log q_k)``. With ``beta = 1`` the estimator is\n unbiased for the full-catalog softmax while the popularity proposal\n concentrates draws on the terms that dominate that sum.\n\n Args:\n positive_logits: Dot-product score of each target's true item.\n negative_logits: Dot-product scores of the drawn negatives.\n positive_items: Catalog row of each target's true item.\n negatives: Catalog rows of the drawn negatives.\n log_q: Mixture log-propensity per catalog item.\n config: Trainer configuration supplying the correction strength.\n\n Returns:\n A per-target cross-entropy vector.\n \"\"\"\n count = negatives.shape[1]\n shifted = negative_logits - (\n config.logq_correction * log_q[negatives] + float(np.log(max(count, 1)))\n )\n logits = torch.cat([positive_logits[:, None], shifted], dim=1)\n return F.cross_entropy(\n logits,\n torch.zeros(len(logits), dtype=torch.long, device=logits.device),\n reduction=\"none\",\n )\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives"
}
### Tool output · 2026-09-24T17:41:13.693Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14934855 tokens left"
}
## Step 24 · agent · 2026-09-24T17:41:18.772Z
### Edit · 2026-09-24T17:41:18.772Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"new_string": " selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_objective == \"sampled-softmax\":\n per_target = sampled_softmax_loss(\n positive_logits,\n negative_logits,\n positive_items[begin:end],\n negatives[begin:end],\n log_q,\n config,\n )\n else:\n per_target = (\n F.softplus(-positive_logits)\n + corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n ) / 2.0\n loss_sum = (selected_weights * per_target).sum()"
}
### Tool output · 2026-09-24T17:41:18.804Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14934270 tokens left"
}
## Step 25 · agent · 2026-09-24T17:41:22.260Z
### Edit · 2026-09-24T17:41:22.260Z
{
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:",
"new_string": " negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n log_q: torch.Tensor,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:"
}
### Tool output · 2026-09-24T17:41:22.273Z
The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14933884 tokens left"
}
## Step 26 · agent · 2026-09-24T17:41:25.191Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:41:37.739Z
{
"command": "cd /app/solution && python - <<'EOF'\nimport re\np='train_current_model.py'\ns=open(p).read()\n\n# call sites: draw_training_negatives\nold_draw = \"\"\" known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\"\"\"\nnew_draw = \"\"\" known_positive_packed,\n prepared,\n config,\n item_correction,\n popularity_probs,\n generator,\n device,\n )\"\"\"\nassert s.count(old_draw)==2, s.count(old_draw)\ns=s.replace(old_draw,new_draw)\n\nold_score = \"\"\" negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=\"\"\"\nnew_score = \"\"\" negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n log_q,\n device,\n backward=\"\"\"\nassert s.count(old_score)==2\ns=s.replace(old_score,new_score)\n\n# signatures: run_training_epoch and evaluate_validation_bce\nold_sig = \"\"\" item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\"\"\"\nnew_sig = \"\"\" item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n log_q: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\"\"\"\nassert s.count(old_sig)==1\ns=s.replace(old_sig,new_sig)\n\nold_sig2 = \"\"\" item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\"\"\"\nnew_sig2 = \"\"\" item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n log_q: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\"\"\"\nassert s.count(old_sig2)==1\ns=s.replace(old_sig2,new_sig2)\n\n# train_model call sites\nold_c1 = \"\"\" target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\"\"\"\nnew_c1 = \"\"\" target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n popularity_probs,\n log_q,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\"\"\"\nassert s.count(old_c1)==1\ns=s.replace(old_c1,new_c1)\n\nold_c2 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\"\"\"\nnew_c2 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n popularity_probs,\n log_q,\n known_positive_packed,\n device,\n )\"\"\"\nassert s.count(old_c2)==1\ns=s.replace(old_c2,new_c2)\n\nold_c3 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\"\"\"\nnew_c3 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n popularity_probs,\n log_q,\n known_positive_packed,\n device,\n )\"\"\"\nassert s.count(old_c3)==1\ns=s.replace(old_c3,new_c3)\n\n# build_item_correction call in train_model\nold_bc = \"\"\" item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\"\"\"\nnew_bc = \"\"\" target_item_counts = selected_target_item_counts(\n prepared, partition.window_starts, training_counts\n )\n item_correction = build_item_correction(target_item_counts, config)\n popularity_probs, log_q_cpu = build_negative_sampler(\n target_item_counts, config\n )\n log_q = log_q_cpu.to(device)\"\"\"\nassert s.count(old_bc)==1\ns=s.replace(old_bc,new_bc)\nopen(p,'w').write(s)\nprint(\"ok\")\nEOF",
"description": "Wire up new sampler plumbing"
}
### Tool output · 2026-09-24T17:41:37.860Z
ok
### Harness context
{
"type": "total_tokens_reminder",
"text": "14932103 tokens left"
}
## Step 27 · agent · 2026-09-24T17:41:40.511Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:41:51.178Z
{
"command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\n\nold = \"\"\" parser.add_argument(\n \"--negative-filter-scope\",\"\"\"\nnew = \"\"\" parser.add_argument(\n \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n )\n parser.add_argument(\n \"--popularity-negatives\", type=int, default=Config.popularity_negatives\n )\n parser.add_argument(\n \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n )\n parser.add_argument(\n \"--popularity-sampling-power\",\n type=float,\n default=Config.popularity_sampling_power,\n )\n parser.add_argument(\n \"--logq-correction\", type=float, default=Config.logq_correction\n )\n parser.add_argument(\n \"--loss-objective\",\n choices=(\"sampled-softmax\", \"bce\"),\n default=Config.loss_objective,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\"\"\"\nassert s.count(old)==1\ns=s.replace(old,new,1)\n\nold2 = \"\"\" config = Config(\n bptt_steps=args.bptt_steps,\"\"\"\nnew2 = \"\"\" if (\n args.uniform_negatives < 0\n or args.popularity_negatives < 0\n or args.inbatch_negatives < 0\n or args.uniform_negatives\n + args.popularity_negatives\n + args.inbatch_negatives\n < 1\n or args.popularity_sampling_power < 0\n or args.logq_correction < 0\n ):\n parser.error(\"negative sampler counts and exponents are invalid\")\n config = Config(\n bptt_steps=args.bptt_steps,\"\"\"\nassert s.count(old2)==1\ns=s.replace(old2,new2,1)\n\nold3 = \"\"\" negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\"\"\"\nnew3 = \"\"\" uniform_negatives=args.uniform_negatives,\n popularity_negatives=args.popularity_negatives,\n inbatch_negatives=args.inbatch_negatives,\n popularity_sampling_power=args.popularity_sampling_power,\n logq_correction=args.logq_correction,\n loss_objective=args.loss_objective,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\"\"\"\nassert s.count(old3)==1\ns=s.replace(old3,new3,1)\n\nold4 = ''' \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),'''\nnew4 = ''' \"negative_sampling\": (\n f\"{config.uniform_negatives}-uniform-plus-\"\n f\"{config.popularity_negatives}-popularity-plus-\"\n f\"{config.inbatch_negatives}-inbatch\"\n ),\n \"loss_objective\": config.loss_objective,'''\nassert s.count(old4)==1\ns=s.replace(old4,new4,1)\n\n# negative_count diagnostic\nold5 = \" negative_count = config.uniform_negatives + config.inbatch_negatives\"\nnew5 = (\" negative_count = (\\n\"\n \" config.uniform_negatives\\n\"\n \" + config.popularity_negatives\\n\"\n \" + config.inbatch_negatives\\n\"\n \" )\")\nassert s.count(old5)==1\ns=s.replace(old5,new5,1)\nopen(p,'w').write(s)\nprint(\"ok\")\nEOF\npython -c \"import ast;ast.parse(open('/app/solution/train_current_model.py').read())\" && echo parsed",
"description": "Add CLI flags"
}
### Tool output · 2026-09-24T17:41:51.435Z
ok
parsed
### Harness context
{
"type": "total_tokens_reminder",
"text": "14930633 tokens left"
}
## Step 28 · agent · 2026-09-24T17:41:53.730Z
### Bash · 2026-09-24T17:41:53.730Z
{
"command": "grep -n \"objective\\|epochs = 1\\|loss_objective\" train_current_model.py | head -20; tail -3 baseline.log; date",
"description": "Check wiring and baseline"
}
### Tool output · 2026-09-24T17:41:53.797Z
60: loss_objective: str = "sampled-softmax"
1295: if config.loss_objective == "sampled-softmax":
1773: objective_changed = (
1780: if "optimizer_state" in payload and not objective_changed:
1790: if "generator_state" in payload and not objective_changed:
1801: if objective_changed:
1804: "BCE objective changed: filter "
2112: "--loss-objective",
2114: default=Config.loss_objective,
2224: loss_objective=args.loss_objective,
2326: "objective": "sample-weighted-BCE-at-every-timestep",
2332: "loss_objective": config.loss_objective,
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=213.3s
Thu Sep 24 17:41:53 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14930476 tokens left"
}
## Step 29 · agent · 2026-09-24T17:41:58.354Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:41:58.913Z
{
"command": "sleep 150; tail -4 /app/solution/baseline.log",
"description": "Wait for baseline"
}
### Tool error · 2026-09-24T17:43:58.939Z
Exit code 143
Command timed out after 2m 0s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14929728 tokens left"
}
## Step 30 · agent · 2026-09-24T17:44:00.842Z
### Bash · 2026-09-24T17:44:00.842Z
{
"command": "tail -4 /app/solution/baseline.log; ls -la /app/solution/*.parquet 2>/dev/null",
"description": "Check baseline"
}
### Tool output · 2026-09-24T17:44:00.860Z
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
-rw-r--r-- 1 agent agent 1244230 Sep 24 17:43 /app/solution/production_validation.parquet
### Harness context
{
"type": "total_tokens_reminder",
"text": "14929563 tokens left"
}
## Step 31 · agent · 2026-09-24T17:44:03.416Z
### Bash · 2026-09-24T17:44:03.416Z
{
"command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/val_cache.npz --output /app/solution/improved_validation.parquet > /app/solution/improved.log 2>&1 &\nsleep 2; echo started",
"description": "Start improved run"
}
### Tool output · 2026-09-24T17:44:05.434Z
started
### Bash · 2026-09-24T17:44:04.187Z
{
"command": "cd /task && python evaluate_public.py /app/solution/production_validation.parquet",
"description": "Evaluate baseline"
}
### Tool output · 2026-09-24T17:44:08.882Z
{
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Shell cwd was reset to /app
### Harness context
{
"type": "total_tokens_reminder",
"text": "14929189 tokens left"
}
## Step 32 · agent · 2026-09-24T17:44:15.420Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:44:18.639Z
{
"command": "cd /app/solution && python - <<'EOF'\nimport numpy as np, pandas as pd\nd=np.load('/task/data/test_eligible_candidates.npz')\nuids=d['uids']; items=d['item_ids']; packed=d['packed_eligible']\nrows=[]\nfor r,u in enumerate(uids):\n m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n sel=np.flatnonzero(m)[:100] # catalog is popularity-ordered\n assert len(sel)==100, (u,len(sel))\n for rank,c in enumerate(sel,1):\n rows.append((int(u),int(items[c]),rank))\nf=pd.DataFrame(rows,columns=['uid','item_id','rank'])\nf.to_parquet('/app/solution/submission.parquet',index=False)\nprint(len(f), f.uid.nunique())\nEOF",
"description": "Write popularity fallback submission"
}
### Tool output · 2026-09-24T17:44:21.535Z
845600 8456
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928247 tokens left"
}
## Step 33 · agent · 2026-09-24T17:44:23.079Z
### Bash · 2026-09-24T17:44:23.079Z
{
"command": "tail -6 /app/solution/improved.log",
"description": "Check improved log"
}
### Tool output · 2026-09-24T17:44:23.090Z
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928127 tokens left"
}
## Step 34 · agent · 2026-09-24T17:44:24.625Z
### Bash · 2026-09-24T17:44:24.625Z
{
"command": "sleep 110; tail -5 /app/solution/improved.log",
"description": "Monitor improved"
}
### Tool output · 2026-09-24T17:46:14.649Z
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
negative sampler: uniform=8 popularity=16 inbatch=8 power=0.75 q_head=1.198e-03 q_min=9.847e-06 logq_beta=1.0
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
### Harness context
{
"type": "total_tokens_reminder",
"text": "14927915 tokens left"
}
## Step 35 · agent · 2026-09-24T17:46:17.225Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:46:17.774Z
{
"command": "sleep 240; tail -4 /app/solution/improved.log",
"description": "Monitor improved"
}
### Tool error · 2026-09-24T17:48:17.792Z
Exit code 143
Command timed out after 2m 0s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14927493 tokens left"
}
## Step 36 · agent · 2026-09-24T17:48:19.391Z
### Bash · 2026-09-24T17:48:19.391Z
{
"command": "sleep 115; tail -3 /app/solution/improved.log",
"description": "Monitor improved"
}
### Tool output · 2026-09-24T17:50:14.432Z
epoch=1/1 batches=250 targets=987,417 batch_bce=9.528758 running_bce=9.306299 elapsed=209.4s
epoch=1/1 train_loss=9.29833 validation_loss=9.05439 pair_accuracy=0.672 train_targets=1,052,953 validation_targets=130,103 train_seconds=222.6 validation_seconds=16.6
restored best validation-BCE checkpoint: epoch=1 loss=9.054388
### Harness context
{
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## Step 37 · agent · 2026-09-24T17:50:16.720Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:50:17.591Z
{
"command": "sleep 45; tail -2 /app/solution/improved.log; ls -la /app/solution/improved_validation.parquet 2>/dev/null",
"description": "Wait for ranking"
}
### Tool output · 2026-09-24T17:51:02.631Z
"device": "cpu"
}
-rw-r--r-- 1 agent agent 1185639 Sep 24 17:50 /app/solution/improved_validation.parquet
### Harness context
{
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## Step 38 · agent · 2026-09-24T17:51:04.484Z
### Bash · 2026-09-24T17:51:04.484Z
{
"command": "cd /task && python evaluate_public.py /app/solution/improved_validation.parquet",
"description": "Evaluate improved"
}
### Tool output · 2026-09-24T17:51:07.876Z
{
"model": "/app/solution/improved_validation.parquet",
"scores": {
"quality_ndcg@10": 0.015572,
"quality_users": 4263
}
}
Shell cwd was reset to /app
### Harness context
{
"type": "total_tokens_reminder",
"text": "14926761 tokens left"
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## Step 39 · agent · 2026-09-24T17:51:24.048Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:51:25.461Z
{
"command": "grep -E \"pair_accuracy|epoch=1/1 batches\" /app/solution/baseline.log; grep -E \"query_norm|item_norm\" /app/solution/baseline.log /app/solution/improved.log; grep -A6 '\"embedding_diagnostics\"' /app/solution/baseline.log /app/solution/improved.log",
"description": "Compare diagnostics"
}
### Tool output · 2026-09-24T17:51:25.487Z
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=213.3s
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=226.7 validation_seconds=19.3
"pair_accuracy": 0.7547733612041563,
/app/solution/improved.log: "query_norm_mean": 1.3343452215194702,
/app/solution/improved.log: "query_norm_std": 0.7968101501464844,
/app/solution/improved.log: "item_norm_mean": 2.111861228942871,
/app/solution/improved.log: "item_norm_std": 1.071254849433899
/app/solution/baseline.log: "query_norm_mean": 1.603016972541809,
/app/solution/baseline.log: "query_norm_std": 0.7697497606277466,
/app/solution/baseline.log: "item_norm_mean": 1.7570279836654663,
/app/solution/baseline.log: "item_norm_std": 0.8279485702514648
/app/solution/improved.log: "embedding_diagnostics": {
/app/solution/improved.log- "query_norm_mean": 1.3343452215194702,
/app/solution/improved.log- "query_norm_std": 0.7968101501464844,
/app/solution/improved.log- "item_norm_mean": 2.111861228942871,
/app/solution/improved.log- "item_norm_std": 1.071254849433899
/app/solution/improved.log- },
/app/solution/improved.log- "torch_version": "2.8.0+cpu",
/app/solution/baseline.log: "embedding_diagnostics": {
/app/solution/baseline.log- "query_norm_mean": 1.603016972541809,
/app/solution/baseline.log- "query_norm_std": 0.7697497606277466,
/app/solution/baseline.log- "item_norm_mean": 1.7570279836654663,
/app/solution/baseline.log- "item_norm_std": 0.8279485702514648
/app/solution/baseline.log- },
/app/solution/baseline.log- "torch_version": "2.8.0+cpu",
### Harness context
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## Step 40 · agent · 2026-09-24T17:51:45.799Z
### Thinking unavailable — no text recorded
Softmax + full logQ correction underperformed (0.0156 vs 0.0213). Pivoting to a change that preserves the proven BCE calibration and only enriches the negative pool:
### Bash · 2026-09-24T17:51:56.455Z
{
"command": "cd /app/solution && python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\n\ns=s.replace(''' uniform_negatives: int = 8\n popularity_negatives: int = 16\n popularity_sampling_power: float = 0.75\n logq_correction: float = 1.0\n loss_objective: str = \"sampled-softmax\"''',''' uniform_negatives: int = 24\n popularity_negatives: int = 16\n popularity_sampling_power: float = 0.75\n logq_correction: float = 1.0\n loss_objective: str = \"bce\"''',1)\n\n# popularity block correction inside the draw\nold=''' sampled_columns = uniform.shape[1]\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:'''\nnew=''' sampled_columns = uniform.shape[1]\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if config.popularity_negatives:\n popularity_slice = slice(\n config.uniform_negatives, sampled_columns\n )\n correction[:, popularity_slice] = item_correction[\n torch.from_numpy(negatives[:, popularity_slice])\n ].numpy()\n if corrected_count:'''\nassert s.count(old)==1\ns=s.replace(old,new,1)\n\nold2=''' raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total'''\nnew2=''' raw = F.softplus(logits)\n total = (\n config.uniform_negatives\n + config.popularity_negatives\n + config.inbatch_negatives\n )\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n cursor = config.uniform_negatives\n for count in (config.popularity_negatives, config.inbatch_negatives):\n if not count:\n continue\n selected = slice(cursor, cursor + count)\n weights = correction[:, selected]\n result += count * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n cursor += count\n return result / total'''\nassert s.count(old2)==1\ns=s.replace(old2,new2,1)\nopen(p,'w').write(s)\nprint('ok')\nEOF\npython -c \"import ast;ast.parse(open('/app/solution/train_current_model.py').read())\" && echo parsed",
"description": "Switch to BCE with corrected popularity negatives"
}
### Tool output · 2026-09-24T17:51:56.588Z
ok
parsed
### Harness context
{
"type": "total_tokens_reminder",
"text": "14921879 tokens left"
}
## Step 41 · agent · 2026-09-24T17:51:59.741Z
### Bash · 2026-09-24T17:51:59.741Z
{
"command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/val_cache.npz --output /app/solution/v2_validation.parquet > /app/solution/v2.log 2>&1 & sleep 30; tail -3 /app/solution/v2.log",
"description": "Run v2 validation"
}
### Tool output · 2026-09-24T17:52:29.760Z
negative sampler: uniform=24 popularity=16 inbatch=8 power=0.75 q_head=8.100e-04 q_min=1.768e-05 logq_beta=1.0
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
### Harness context
{
"type": "total_tokens_reminder",
"text": "14921664 tokens left"
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## Step 42 · agent · 2026-09-24T17:52:33.793Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:52:35.318Z
{
"command": "nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/test_cache.npz --output /tmp/unused.parquet --prepare-only --threads 2 > /app/solution/prep_test.log 2>&1 & sleep 5; echo ok",
"description": "Prebuild test-stage cache"
}
### Tool output · 2026-09-24T17:52:40.345Z
ok
### Harness context
{
"type": "total_tokens_reminder",
"text": "14921130 tokens left"
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## Step 43 · agent · 2026-09-24T17:52:42.447Z
### Bash · 2026-09-24T17:52:42.447Z
{
"command": "sleep 115; tail -2 /app/solution/v2.log; tail -1 /app/solution/prep_test.log",
"description": "Monitor"
}
### Tool output · 2026-09-24T17:54:37.486Z
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
}
### Harness context
{
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"text": "14920998 tokens left"
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## Step 44 · agent · 2026-09-24T17:54:39.521Z
### Bash · 2026-09-24T17:54:39.521Z
{
"command": "ls -la /app/solution/test_cache.npz; grep -c . /app/solution/prep_test.log",
"description": "Check test cache"
}
### Tool output · 2026-09-24T17:54:39.541Z
-rw-r--r-- 1 agent agent 552686232 Sep 24 17:53 /app/solution/test_cache.npz
61
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## Step 45 · agent · 2026-09-24T17:54:41.256Z
### Bash · 2026-09-24T17:54:41.256Z
{
"command": "sleep 230; tail -3 /app/solution/v2.log",
"description": "Monitor v2"
}
### Tool error · 2026-09-24T17:56:41.284Z
Exit code 143
Command timed out after 2m 0s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14920626 tokens left"
}
## Step 46 · agent · 2026-09-24T17:56:43.034Z
### Bash · 2026-09-24T17:56:43.034Z
{
"command": "sleep 115; tail -3 /app/solution/v2.log",
"description": "Monitor v2"
}
### Tool output · 2026-09-24T17:58:38.083Z
epoch=1/1 batches=250 targets=987,417 batch_bce=0.555003 running_bce=0.550155 elapsed=293.8s
epoch=1/1 train_loss=0.54852 validation_loss=0.50628 pair_accuracy=0.723 train_targets=1,052,953 validation_targets=130,103 train_seconds=309.4 validation_seconds=20.4
restored best validation-BCE checkpoint: epoch=1 loss=0.506278
### Harness context
{
"type": "total_tokens_reminder",
"text": "14920479 tokens left"
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## Step 47 · agent · 2026-09-24T17:58:40.203Z
### Bash · 2026-09-24T17:58:40.203Z
{
"command": "sleep 50; ls -la /app/solution/v2_validation.parquet && cd /task && python evaluate_public.py /app/solution/v2_validation.parquet",
"description": "Evaluate v2"
}
### Tool output · 2026-09-24T17:59:33.599Z
-rw-r--r-- 1 agent agent 1251666 Sep 24 17:59 /app/solution/v2_validation.parquet
{
"model": "/app/solution/v2_validation.parquet",
"scores": {
"quality_ndcg@10": 0.020402,
"quality_users": 4263
}
}
Shell cwd was reset to /app
### Harness context
{
"type": "total_tokens_reminder",
"text": "14920160 tokens left"
}
## Step 48 · agent · 2026-09-24T17:59:43.862Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T17:59:46.847Z
{
"command": "cd /app/solution && python - <<'EOF'\nimport numpy as np, pandas as pd\nd=np.load('/task/data/validation_eligible_candidates.npz')\nuids=d['uids']; items=d['item_ids']; packed=d['packed_eligible']\nrows=[]\nfor r,u in enumerate(uids):\n m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n sel=np.flatnonzero(m)[:100]\n for rank,c in enumerate(sel,1):\n rows.append((int(u),int(items[c]),rank))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/pop_validation.parquet',index=False)\nEOF\ncd /task && python evaluate_public.py /app/solution/pop_validation.parquet",
"description": "Popularity-only diagnostic"
}
### Tool output · 2026-09-24T17:59:52.980Z
{
"model": "/app/solution/pop_validation.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Shell cwd was reset to /app
### Harness context
{
"type": "total_tokens_reminder",
"text": "14919006 tokens left"
}
## Step 49 · agent · 2026-09-24T18:00:38.657Z
### Thinking unavailable — no text recorded
Both hard-negative variants regressed. The trend says popularity-skewed negatives hurt this objective. Testing the opposite direction:
### Bash · 2026-09-24T18:00:41.015Z
{
"command": "cd /app/solution && nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/val_cache.npz --uniform-negatives 40 --popularity-negatives 0 --inbatch-negatives 0 --output /app/solution/v3_validation.parquet > /app/solution/v3.log 2>&1 & sleep 20; tail -2 /app/solution/v3.log; date",
"description": "Run v3: pure uniform negatives"
}
### Tool output · 2026-09-24T18:01:01.067Z
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
Thu Sep 24 18:01:01 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14915471 tokens left"
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## Step 50 · agent · 2026-09-24T18:01:04.181Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T18:01:04.775Z
{
"command": "sleep 115; tail -2 /app/solution/v3.log",
"description": "Monitor v3"
}
### Tool output · 2026-09-24T18:02:59.801Z
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
### Harness context
{
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"text": "14915120 tokens left"
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## Step 51 · agent · 2026-09-24T18:03:01.553Z
### Bash · 2026-09-24T18:03:01.553Z
{
"command": "sleep 118; tail -2 /app/solution/v3.log",
"description": "Monitor v3"
}
### Tool output · 2026-09-24T18:04:59.593Z
epoch=1/1 train_loss=0.52329 validation_loss=0.47733 pair_accuracy=0.793 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.0 validation_seconds=19.1
restored best validation-BCE checkpoint: epoch=1 loss=0.477329
### Harness context
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## Step 52 · agent · 2026-09-24T18:05:01.365Z
### Bash · 2026-09-24T18:05:01.365Z
{
"command": "sleep 45; cd /task && python evaluate_public.py /app/solution/v3_validation.parquet",
"description": "Evaluate v3"
}
### Tool error · 2026-09-24T18:05:49.145Z
Exit code 1
Traceback (most recent call last):
File "/task/evaluate_public.py", line 157, in
main()
File "/task/evaluate_public.py", line 152, in main
ranked = load_submission(path, eligibility, targets)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/task/evaluate_public.py", line 113, in load_submission
frame = validate_ranking(read_ranking(path), targets)
^^^^^^^^^^^^^^^^^^
File "/task/ranking_evaluation.py", line 98, in read_ranking
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 669, in read_parquet
return impl.read(
^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 258, in read
path_or_handle, handles, filesystem = _get_path_or_handle(
^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 141, in _get_path_or_handle
handles = get_handle(
^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle
handle = open(handle, ioargs.mode)
^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/app/solution/v3_validation.parquet'
### Harness context
{
"type": "total_tokens_reminder",
"text": "14914683 tokens left"
}
## Step 53 · agent · 2026-09-24T18:05:51.295Z
### Bash · 2026-09-24T18:05:51.295Z
{
"command": "sleep 40; ls /app/solution/v3_validation.parquet && cd /task && python evaluate_public.py /app/solution/v3_validation.parquet; date",
"description": "Evaluate v3"
}
### Tool output · 2026-09-24T18:06:34.690Z
/app/solution/v3_validation.parquet
{
"model": "/app/solution/v3_validation.parquet",
"scores": {
"quality_ndcg@10": 0.020408,
"quality_users": 4263
}
}
Thu Sep 24 18:06:34 UTC 2026
Shell cwd was reset to /app
### Harness context
{
"type": "total_tokens_reminder",
"text": "14913852 tokens left"
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## Step 54 · agent · 2026-09-24T18:06:54.695Z
### Thinking unavailable — no text recorded
None of the variants beat the baseline. Shipping the production configuration for the final refit — starting it now so a real model-based submission is guaranteed:
### Bash · 2026-09-24T18:06:57.326Z
{
"command": "cd /app/solution && nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/test_cache.npz --uniform-negatives 24 --popularity-negatives 0 --inbatch-negatives 8 --loss-objective bce --output /app/solution/submission.parquet > /app/solution/submission.log 2>&1 & sleep 25; tail -3 /app/solution/submission.log; date",
"description": "Start final submission refit"
}
### Tool output · 2026-09-24T18:07:22.347Z
negative sampler: uniform=24 popularity=0 inbatch=8 power=0.75 q_head=6.940e-04 q_min=2.523e-05 logq_beta=1.0
target weighting=organic-strong min=0.250 mean=0.576 max=1.000
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
Thu Sep 24 18:07:22 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14912076 tokens left"
}
## Step 55 · agent · 2026-09-24T18:07:25.702Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T18:07:27.974Z
{
"command": "cd /app/solution && python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\nold=''' uniform_negatives: int = 24\n popularity_negatives: int = 16'''\nnew=''' uniform_negatives: int = 24\n popularity_negatives: int = 0'''\nassert s.count(old)==1\ns=s.replace(old,new,1)\nopen(p,'w').write(s)\nEOF\ngrep -n \"uniform_negatives: int\\|popularity_negatives: int\\|inbatch_negatives: int\\|loss_objective: str\\|logq_correction: float\\|popularity_sampling_power\" /app/solution/train_current_model.py | head",
"description": "Set shipped defaults"
}
### Tool output · 2026-09-24T18:07:28.038Z
56: uniform_negatives: int = 24
57: popularity_negatives: int = 0
58: popularity_sampling_power: float = 0.75
59: logq_correction: float = 1.0
60: loss_objective: str = "bce"
61: inbatch_negatives: int = 8
615: weights = np.power(counts + 1.0, config.popularity_sampling_power)
633: f"power={config.popularity_sampling_power} "
2121: default=Config.popularity_sampling_power,
2224: or args.popularity_sampling_power < 0
### Harness context
{
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}
## Step 56 · agent · 2026-09-24T18:07:53.049Z
### Write · 2026-09-24T18:07:53.049Z
{
"file_path": "/app/solution/README.md",
"content": "# Made-for-You two-tower: negative-sampling investigation\n\n## What was measured\n\nAll numbers are public-backtest graded NDCG@10 (`/task/evaluate_public.py`,\n4,263 scored users), trained on `train_*` only and scored on `val_*`.\n\n| Run | Negative sampling | Loss | NDCG@10 |\n| --- | --- | --- | --- |\n| Popularity-order control (no model) | – | – | 0.01071 |\n| **Production baseline (shipped)** | 24 uniform + 8 propensity-corrected in-batch | sample-weighted BCE | **0.02128** |\n| v1 | 8 uniform + 16 popularity^0.75 + 8 in-batch | sampled softmax, full logQ correction | 0.01557 |\n| v2 | 24 uniform + 16 popularity^0.75 (propensity-corrected) + 8 in-batch | sample-weighted BCE | 0.02040 |\n| v3 | 40 uniform only | sample-weighted BCE | 0.02041 |\n\n## Diagnosis\n\nThe production sampler draws 24 of its 32 negatives uniformly from the 30,000\ntrack catalog. Listening is strongly head-skewed, so a uniform draw almost never\nlands on a track that actually competes for a top-10 slot: the softmax partition\nfunction (and, under BCE, the set of negatives with non-negligible\n`sigmoid(s)` gradient) is dominated by head items the sampler rarely sees. That\nis the textbook motivation for popularity-proportional (\"hard\") negatives, so\nthe hypothesis under test was that enriching the proposal toward the head would\nraise retrieval quality.\n\n**The hypothesis did not hold on this objective, and the measurements say why.**\nBoth interventions that shifted negative mass toward the head regressed, and\nthey regressed monotonically in how strongly they do so:\n\n- v1 (softmax with an unbiased logQ correction) was the most aggressive and lost\n 27%. With a `count^0.75` proposal and `beta = 1`, the correction shifts tail\n negatives by roughly `+8` logits versus `+3.2` for head negatives inside the\n log-sum-exp. The corrected tail therefore *becomes* the effective hard\n negative set, and the model spends its capacity suppressing tail items — the\n opposite of what the popularity proposal was meant to buy.\n- v2 (the same proposal, propensity-corrected, under the unchanged BCE) lost\n 3.5%.\n- v3, which removed the head-skewed component entirely (40 uniform, no in-batch),\n also landed at 0.0204 — i.e. the two BCE variants are indistinguishable from\n each other and both sit just below the baseline.\n\nThe discovery label is itself head-skewed: grade comes from organic likes and\n≥80% organic listens, and a pure popularity ordering already reaches half the\nproduction model's NDCG (0.0107 vs 0.0213). Anything that pushes head items down\n— whether directly (harder head negatives under BCE) or indirectly (a logQ\ncorrection that up-weights the tail) — trades away score. The production mix of\nmostly-uniform negatives plus a *small, partially* debiased in-batch component\n(power 0.5, clipped to [0.25, 4]) is already close to the local optimum of this\nknob: 24/8 beats both 40/0 and 24/16/8.\n\n## Model change shipped\n\n**None to the objective.** No variant beat the production baseline on the public\nbacktest, so the shipped artifact is the production configuration. Shipping a\nmeasured regression to claim a change would be worse than shipping the baseline.\n\n`train_current_model.py` is nonetheless a superset of the production trainer:\nthe negative sampler is now configurable (`--uniform-negatives`,\n`--popularity-negatives`, `--popularity-sampling-power`, `--inbatch-negatives`,\n`--logq-correction`, `--loss-objective`) so the experiments above are\nreproducible and the knob can be re-swept. **The defaults reproduce the\nproduction model exactly** (24 uniform + 8 in-batch, BCE); with\n`--popularity-negatives 0` the negative-draw and loss code paths are\nbit-identical to the original. The data protocol, the two-tower serving\ncontract (precomputed item vectors, one user vector, dot-product scoring, no\nuser-ID table), and the evaluation boundary are untouched.\n\n## Submission\n\n```sh\npython /app/solution/train_current_model.py \\\n --stage submission --public-dir /task \\\n --output /app/solution/submission.parquet\n```\n\nRefits on `train_* + val_*` and ranks against `test_eligible_candidates.npz`.\n\n## Where I would look next\n\nThe negative-sampling knob looks exhausted at this budget; the remaining\nheadroom is more likely in training volume than in the proposal distribution.\nThe trainer currently sees one epoch over the most recent 128 targets per user —\n1.05M of 20.0M available next-item targets (95% of targets are discarded). A\nsecond epoch, or a larger `--max-train-targets-per-user`, is the untested lever\nwith the largest surface. I did not run it inside the time budget because a\nsingle epoch already costs ~4 minutes of the 60-minute allowance and I chose to\nspend the remaining runs on the negative-sampling question that was posed.\n"
}
### Tool output · 2026-09-24T18:07:53.074Z
File created successfully at: /app/solution/README.md (file state is current in your context — no need to Read it back)
### Harness context
{
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"text": "14909334 tokens left"
}
## Step 57 · agent · 2026-09-24T18:07:55.088Z
### Bash · 2026-09-24T18:07:55.088Z
{
"command": "sleep 115; tail -2 /app/solution/submission.log; date",
"description": "Monitor submission run"
}
### Tool output · 2026-09-24T18:09:50.118Z
target weighting=organic-strong min=0.250 mean=0.576 max=1.000
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
Thu Sep 24 18:09:50 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14909176 tokens left"
}
## Step 58 · agent · 2026-09-24T18:09:52.152Z
### Bash · 2026-09-24T18:09:52.152Z
{
"command": "sleep 118; tail -1 /app/solution/submission.log",
"description": "Monitor"
}
### Tool output · 2026-09-24T18:11:50.216Z
restored best validation-BCE checkpoint: epoch=1 loss=0.497949
### Harness context
{
"type": "total_tokens_reminder",
"text": "14908967 tokens left"
}
## Step 59 · agent · 2026-09-24T18:11:52.129Z
### Bash · 2026-09-24T18:11:52.129Z
{
"command": "sleep 75; tail -2 /app/solution/submission.log; ls -la /app/solution/submission.parquet",
"description": "Wait for ranking"
}
### Tool output · 2026-09-24T18:13:07.162Z
"device": "cpu"
}
-rw-r--r-- 1 agent agent 1212474 Sep 24 18:12 /app/solution/submission.parquet
### Harness context
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"text": "14908799 tokens left"
}
## Step 60 · agent · 2026-09-24T18:13:08.740Z
Final submission written. Validating it against the deliverable rules:
### Bash · 2026-09-24T18:13:14.132Z
{
"command": "python - <<'EOF'\nimport numpy as np, pandas as pd, os\nf=pd.read_parquet('submission.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nprint(\"cols\",list(f.columns),\"rows\",len(f))\nprint(\"islink\",os.path.islink('submission.parquet'),\"csv_exists\",os.path.exists('submission.csv'))\nfor c in ['uid','item_id','rank']:\n assert np.isfinite(f[c]).all() and (f[c]==f[c].astype(np.int64)).all(), c\nprint(\"users match:\", set(f.uid.astype(int))==t, len(t))\ng=f.groupby('uid')\nassert (g.size()==100).all()\nassert (g['item_id'].nunique()==100).all()\nassert g['rank'].apply(lambda r: sorted(r)==list(range(1,101))).all()\nd=np.load('/task/data/test_eligible_candidates.npz')\nuids,items,packed=d['uids'],d['item_ids'],d['packed_eligible']\npos={int(x):i for i,x in enumerate(items)}\nrow={int(u):r for r,u in enumerate(uids)}\nbad=0\nfor u,grp in g:\n m=np.unpackbits(packed[row[int(u)]],count=len(items),bitorder='little').astype(bool)\n bad+=int((~m[[pos[int(i)] for i in grp.item_id]]).sum())\nprint(\"ineligible items:\",bad)\nprint(\"OK\")\nEOF",
"description": "Validate submission"
}
### Tool output · 2026-09-24T18:13:16.821Z
cols ['uid', 'item_id', 'rank'] rows 845600
islink False csv_exists False
users match: True 8456
ineligible items: 0
OK
### Harness context
{
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## Step 61 · agent · 2026-09-24T18:13:29.322Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T18:13:30.629Z
{
"command": "rm -f val_cache.npz test_cache.npz pop_validation.parquet && ls -la && python -c \"\nimport ast,sys; ast.parse(open('train_current_model.py').read()); print('script parses')\"",
"description": "Clean up caches"
}
### Tool output · 2026-09-24T18:13:30.801Z
total 6124
drwxr-xr-x 1 agent agent 4096 Sep 24 18:13 .
drwxr-xr-x 1 agent agent 4096 Sep 24 17:32 ..
-rw-r--r-- 1 agent agent 4765 Sep 24 18:07 README.md
-rw-r--r-- 1 agent agent 4694 Sep 24 17:43 baseline.log
-rw-r--r-- 1 agent agent 5045 Sep 24 17:50 improved.log
-rw-r--r-- 1 agent agent 1185639 Sep 24 17:50 improved_validation.parquet
-rw-r--r-- 1 agent agent 2291 Sep 24 17:53 prep_test.log
-rw-r--r-- 1 agent agent 1244230 Sep 24 17:43 production_validation.parquet
-rw-r--r-- 1 agent agent 4195 Sep 24 18:12 submission.log
-rw-r--r-- 1 agent agent 1212474 Sep 24 18:12 submission.parquet
-rw-r--r-- 1 agent agent 87721 Sep 24 18:07 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 24 17:36 two_tower_model.py
-rw-r--r-- 1 agent agent 4203 Sep 24 17:59 v2.log
-rw-r--r-- 1 agent agent 1251666 Sep 24 17:59 v2_validation.parquet
-rw-r--r-- 1 agent agent 4198 Sep 24 18:06 v3.log
-rw-r--r-- 1 agent agent 1195740 Sep 24 18:06 v3_validation.parquet
script parses
### Harness context
{
"type": "total_tokens_reminder",
"text": "14907060 tokens left"
}
## Step 62 · agent · 2026-09-24T18:13:33.613Z
### Thinking unavailable — no text recorded
## Outcome
**I did not beat the production baseline.** The submission ships the production configuration, refit on `train_* + val_*`.
### Measured (public backtest, graded NDCG@10, 4,263 users)
| Run | Negatives | Loss | NDCG@10 |
|---|---|---|---|
| Popularity-order control (no model) | – | – | 0.01071 |
| **Production baseline — shipped** | 24 uniform + 8 corrected in-batch | weighted BCE | **0.02128** |
| v1 | 8 uniform + 16 pop^0.75 + 8 in-batch | sampled softmax, full logQ | 0.01557 |
| v2 | 24 uniform + 16 pop^0.75 (corrected) + 8 in-batch | weighted BCE | 0.02040 |
| v3 | 40 uniform only | weighted BCE | 0.02041 |
### What the experiments say
The stated hypothesis — uniform draws over a 30k head-skewed catalog almost never sample a real top-10 competitor, so the head needs more negative mass — is the textbook diagnosis, and it is wrong for *this* objective. Both head-enriching interventions regressed, monotonically in aggressiveness. v1's full logQ correction shifts tail negatives ~`+8` logits vs ~`+3.2` for head ones inside the log-sum-exp, so the *corrected tail* becomes the effective hard-negative set — the inverse of the intent. v2 and v3 land at the same 0.0204 from opposite directions (more head skew, and none at all), which reads as this knob being flat-to-slightly-negative around the production 24/8 mix.
The reason is in the label: grade comes from organic likes and ≥80% organic listens, and a pure popularity ordering already reaches half the model's score (0.0107 vs 0.0213). Suppressing head items costs NDCG regardless of how it's done. The production mix — mostly uniform, with a small *partially* debiased in-batch component (power 0.5, clipped) — is near the local optimum.
I did not ship a measured regression to claim a change.
### Delivered
- `/app/solution/submission.parquet` — 845,600 rows, 8,456 users, verified: exactly 100 distinct items and ranks 1–100 per user, all integer/finite, users match `target_users.parquet` exactly, 0 ineligible items, regular file, no competing `.csv`.
- `/app/solution/train_current_model.py` + `two_tower_model.py` — a strict superset of the production trainer. The sampler is now configurable (`--uniform-negatives`, `--popularity-negatives`, `--popularity-sampling-power`, `--inbatch-negatives`, `--logq-correction`, `--loss-objective`) so the sweep is reproducible. **Defaults reproduce production exactly**; with `--popularity-negatives 0` the draw and loss paths are identical to the original. Data protocol, two-tower serving contract, and evaluation boundary untouched.
- `/app/solution/README.md` — diagnosis, table, and reasoning; per-run logs and validation rankings retained.
### Untested lever I'd go to next
The trainer uses one epoch over the most recent 128 targets per user: **1.05M of 20.0M available next-item targets, so 95% are discarded.** A second epoch or a larger `--max-train-targets-per-user` is the largest untouched surface. I spent the run budget on the negative-sampling question as posed — an epoch costs ~4 of the 60 minutes, and adding one would have pushed the final refit past the deadline with the gain unvalidated.