Preference reward modeling

ultrafeedback_bt_rm_rewardbench - gpt-5.6-sol / Codex CLI 0.146.0 / high

Status. Exploration and submission completed after 13,412 seconds; formal replay ran for 42,053.386 seconds, all three artifacts passed frozen loading, every final summary had a matching .complete receipt, and boundary review completed. A manifest failure field conflicts with the completed lifecycle state, which exit 0 and the succeeded formal outcome support. An earlier numbered attempt hit a platform storage gate before producing a trajectory or artifact. The candidate kept the Bradley–Terry update rule but combined 1,024-token truncation, 8/8 batching, fused AdamW, and a three-seed schedule; its best official score was 73.9866, below the supplied 74.568936 reference.

What happened. The task trains a scalar reward model from a fixed Mistral-7B-Instruct-v0.2 and 8,192 decontaminated UltraFeedback preference pairs. The baseline raises chosen answers above rejected ones with pairwise logistic loss, updating rank-128 low-rank adapters (LoRA) and a new scalar head for 252 steps. Evaluation retains only the last 1,024 tokens although training allowed 4,096, while the baseline left much GPU memory unused.

Four-hour exploration. Scores use the visible 512-pair rewardbench_proxy_512 metric (higher is better); reported errors exclude seed uncertainty. First, the unchanged seed-42 baseline scored 70.9211 ± 2.1336 at step 126 and 71.2006 ± 2.1463 at step 252, making the latter the control despite its small, section-mixed gain. A live-script edit broke packaging, but manually published weights passed frozen loading, so the model remained valid. Second, full-model tuning with 1,024 tokens, 8/8 batching, fused AdamW, and 1e-6 learning rate scored 66.8107 and 66.5670. This recipe was rejected, but its confounds did not exclude all full tuning. Third, jointly changing truncation, batch shape, and optimizer while retaining LoRA, loss, effective batch, learning rate, and seed cut training from 3,342.3 to 2,902.7 seconds and reached 72.0392 ± 2.1243, with no section worse; it was adopted. The 0.8386 gain was below its standard error and the changes were not separately ablated. Fourth, seed 43 reproduced the aggregate (72.0079 ± 2.0897) but shifted sharply between Safety and Reasoning, supporting aggregate repeatability, not section stability; both seeds were retained. Finally, loading, zero-overlap, syntax, and publication checks passed. The low-rate seed-44 long run lacked exploration performance evidence and remained unvalidated; a bytecode deletion was skipped as packaging noise.

How the submitted method works. Each preference pair yields chosen and rejected scalar rewards; the unchanged loss is -log sigmoid(r_chosen-r_rejected), and only LoRA and the scalar head are optimized. No labels, synthetic data, external model, or reasoning traces were added. These are hyperparameter, engineering, schedule, and checkpoint-policy changes, not a new objective or signal. Formal replay excluded exploration weights: seeds 42 and 43 independently ran 252 steps, then seed 44 requested 8,192 low-rate steps, all from the fixed start.

Formal and evaluation evidence. The orchestrator supplied a 42,600-second recipe wall clock; replay used 97.35% of the 43,200-second budget. Seed 44 stopped cleanly after 3,121 steps, so progress 3625 is cumulative after two 252-step publications, not one model's steps. All artifacts loaded. On the full 2,985-pair rewardbench_v1_score (higher is better), progress 252 scored 73.9866 ± 0.8962, progress 504 scored 72.0563 ± 0.8908, and progress 3625 scored 69.2522 ± 0.8707; Reasoning fell to 57.60 while Chat rose to 88.69. The first was best; continued training caused imbalance and degradation. Its same-512-row diagnostic was 72.1964, broadly preserving the exploration direction, although seed 43 did not exactly reproduce it. Proxy and full-final headlines use different rows and are not directly comparable. The best did not exceed the single reference by 0.5823; paired or across-seed significance is unknown.

Audit and takeaway. Controls fixed model and data, disabled network access, excluded exploration weights and evaluation assets, recorded zero overlap, closed patch lineage, exposed one unshared GPU, and enforced wall time. Agent behavior is clean; no protocol or evaluation-boundary exposure was found; one confirmed platform-side storage-gate defect preceded a scheduling- and isolation-compliant completed attempt. The case demonstrates bottleneck diagnosis, counterexample, section, and seed analysis, but bundled changes prevent attribution and the untested long branch overfit; improved expected final performance or a truncation-only benefit remains unproved.

codex__ultrafeedback_bt_rm_rewardbench__gpt-5.6-sol__high - Full English Analysis

1. Run identity, attempt selection, and lineage

2. Reader-facing overview

The task asks for a scalar reward model trained from fixed Mistral-7B-Instruct-v0.2 weights and 8,192 decontaminated UltraFeedback preference pairs. The shipped baseline applies Bradley-Terry pairwise logistic loss while training low-rank adaptation weights (LoRA, small trainable matrices attached to a frozen backbone) and a fresh scalar head. The agent identified a concrete representation mismatch—training retained up to 4,096 tokens while evaluation scores only the last 1,024—and substantial GPU underutilization from a one-pair microbatch.

The agent established 126- and 252-step baseline endpoints, rejected a degraded full-parameter recipe, then jointly changed truncation to 1,024 tokens, the microbatch/accumulation shape from 1/64 to 8/8, and the AdamW implementation to fused PyTorch. On the visible 512-pair proxy, the 252-step score rose from 71.2006 ± 2.1463 to 72.0392 ± 2.1243. A seed-43 replication scored 72.0079 ± 2.0897, but Safety and Reasoning traded roughly ten and eight points in opposite directions. The submitted formal recipe preserved the Bradley-Terry update rule, independently replayed two 252-step seeds, and used a third seed at a lower rate until the wall-clock callback stopped it.

Formal replay completed and produced three loadable LoRA artifacts. Their official rewardbench_v1_score values on all 2,985 final pairs were 73.9866 ± 0.8962, 72.0563 ± 0.8908, and 69.2522 ± 0.8707; progress 252, seed 42, was best. That best is numerically 0.5823 below the shipped single-run final reference of 74.568936, while the long run badly degraded Reasoning. Thus the best seed approximately retained the exploration proxy advantage on the proxy rows, but the method did not improve the shipped full-final reference.

The trajectory demonstrates useful endpoint analysis, rejection testing, section-level diagnosis, seed replication, and artifact engineering. Its main scientific weakness is causal ambiguity: truncation, batch shape, and optimizer implementation were never separated, and the long formal branch had no pre-submission performance evidence. Observable agent behavior was clean and no hidden final values were exposed or used. A confirmed storage-gating defect affected only a pre-run attempt; completed explore/formal runs had one isolated visible GPU.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: mistralai/Mistral-7B-Instruct-v0.2 at pinned private revision; its scalar head is freshly initialized Available training data and assets: fixed 8,192-pair decontaminated UltraFeedback projection; a fixed 512-pair RewardBench proxy is additionally visible in exploration Agent-editable surface: editable workspace data selection/transformation, objective, sampling, optimization, schedule, LoRA/full-parameter choice, export, and checkpoint policy Fixed or forbidden components: starting model, formal pair file, frozen evaluator/weights, 2,473 hidden final pairs, no-network boundary; external preference/evaluation rows or reward checkpoints and explore-to-formal weights are forbidden Proxy evaluator: rewardbench_proxy_512 / maximize / fixed visible subset / n=512 / weighted-subset binomial descriptive stderr, not seed or paired uncertainty Final evaluator: rewardbench_v1_score / maximize / RewardBench v1 filtered full split / n=2,985 / the same kind of descriptive stderr Artifact contract: up to three increasing numeric-progress checkpoints, each a complete model or compatible delta with a loadable scalar head and zero training/final overlap; official result is the best valid final score ``

Proxy and final evaluation share chat-template tokenization, last-1,024-token truncation, and four-section aggregation. The proxy is only 512 of the 2,985 final pairs; 2,473 remain hidden until scoring. Proxy scores may be compared with other proxy scores, and full-final scores with the shipped full-final reference, but the 512- and 2,985-row headline values are not direct same-split deltas. The final receipt's post hoc proxy/held-out split is diagnostic.

3.2 How the baseline works

``text one fixed preference pair: a shared user prompt plus chosen and rejected answers -> the Mistral backbone and scalar head assign one reward to each full conversation -> the label says the chosen answer should outrank the rejected answer -> minimize -log sigmoid(r_chosen - r_rejected) with AdamW, accumulation, and cosine decay -> update LoRA matrices on seven projection families plus the fresh scalar head, then export a delta compatible with the fixed backbone ``

The baseline left-truncates to 4,096 tokens. It uses rank-128 LoRA with alpha 256 and dropout 0.05 on all attention and feed-forward projections, training 335,548,416 delta/head parameters. Its one-pair microbatch accumulates 64 times for an effective pair batch of 64; the learning rate is 5e-6, with four warmup steps and cosine decay through 252 optimizer steps. It saves at 126 and 252. Temperature is 1, margin is 0, and reward-centering weight is 0.

The agent explicitly diagnosed two bottlenecks. At least one side exceeded the 1,024-token evaluator boundary in 712 of 8,192 pairs, so training consumed context unavailable at evaluation. The exploration device had about 275 GiB while the baseline used roughly 24 GiB and about 40% compute. Its tokenization study also found that a 1,024 cap reduced padded-token work by only about 4.3%, so it treated truncation primarily as alignment/regularization, not a guaranteed large systems speedup.

4. Four-hour exploration and decision process

The primary run lasted 3 h 43 min. Roughly the first hour covered source/data diagnosis and the baseline; the next 39 minutes tested full-parameter tuning; about 48 minutes tested the aligned/high-throughput LoRA recipe; another 48 minutes replicated seed 43. Proxy scoring, failure recovery, overlap checks, frozen loading, patch review, and formal-policy hardening used the remaining time. At submission no training or evaluator process remained, and the measured roughly 2,900-second LoRA runtime made another interpretable 126-step train-plus-score run infeasible within the remaining 1,035 seconds.

U-01 - Establish the baseline and test whether the second pass helps

Motivation and hypothesis. The agent needed a reproducible control and two checkpoints from one path to determine whether the approximate second data pass improved proxy generalization or merely reduced training loss.

Concrete change and experimental setup. It ran the shipped seed-42 recipe unchanged: 4,096 tokens, microbatch/accumulation 1/64, rank-128 LoRA, paged_adamw_32bit, 5e-6, and 252 steps, saving at 126 and 252. A newly added full-tuning switch remained off and did not affect the run.

Observed result. Training completed 252/252 steps in 3,342.34 seconds with a 22,970,694,656-byte allocated-memory peak. Step 126 scored 70.9211 ± 2.1336 on the 512-pair proxy, with Chat 76.0285, Chat Hard 56.0773, Safety 66.0808, and Reasoning 85.4978. Step 252 scored 71.2006 ± 2.1463, with 76.8577, 56.0606, 67.1843, and 84.6997. After training, the wrapper hit a quoting syntax error because the agent had edited the currently executing run.sh; the complete trainer checkpoints were unaffected. The agent repaired the source, manually ran the same atomic publication steps, and both endpoints passed the frozen loader.

Agent interpretation. The 0.2795 aggregate increase was well inside the descriptive uncertainty and exchanged better Chat/Safety for weaker Reasoning. The agent therefore treated step 252 as the slightly better control endpoint, not proof that a second pass was robustly beneficial.

Report assessment and confounds. This is consistent with the agent's interpretation. Both endpoints share a seed and training path, but no paired row-difference receipt exists and the stated stderr is not uncertainty on their difference. The post-training wrapper failure does not invalidate complete, distinct, loadable artifacts, though it reveals a real risk from editing an active entry script.

Decision and consequence. Use the step-252 score of 71.2006 as the exploration control, retain step 126 as fallback, and evaluate both endpoints for subsequent 252-step experiments.