Preference reward modeling

ultrafeedback_bt_rm_rewardbench - gpt-5.6-sol / Codex CLI / medium

Status. Exploration, submission, formal retraining, validation of three artifacts, and three official final evaluations completed with verifiable lineage. A subprocess recorded exit 137 during submit, but authoritative lifecycle and submission receipts establish success. Observable agent behavior was clean. The faster, seed-portfolio candidate reached a best rewardbench_v1_score of 73.686 ± 0.884, below the shipped same-protocol reference of 74.568936.

What happened. The task trains a scalar reward model from fixed Mistral-7B-Instruct-v0.2 and 8,192 decontaminated UltraFeedback chosen/rejected pairs. The baseline applies a Bradley–Terry loss to update a rank-128 low-rank adapter (LoRA) and random scalar head while freezing the backbone. The agent identified slow 4,096-token, microbatch-one training and strong random-head seed sensitivity; it submitted 1,024-token fast LoRA plus multi-seed screening and three fixed-start retrains, without changing the preference update rule.

Four-hour exploration. The maximize-direction proxy uses 512 visible RewardBench rows; its standard error describes finite evaluation samples, not seed variance. Baseline seed 42 scored 71.017 ± 2.117 at step 126 and 71.612 ± 2.109 at step 252 in 3,273 seconds. Weak reward/length correlation made the agent abandon explicit length offset; category oversampling was considered but not run. Reducing length to 1,024, increasing microbatch from one to four, reducing accumulation from 64 to 16, and grouping by length preserved global batch 64 and scored 72.974 ± 2.170 and 73.214 ± 2.109 in 1,846 seconds. The 44% speedup is robust; proxy quality evidence is only directional.

Full-parameter tuning scored 71.058 ± 2.074 and produced a 14-GB artifact, so it was rejected. Commentary said 87 GB versus the receipt's 32.99 GB; exact peak is inconclusive, without OOM. Label smoothing 0.05 first required a restart after an incomparable cosine horizon, then scored 72.247 ± 2.183 and was rejected. Seeds 17 and 3407 scored only 67.680 and 68.115, with severe Reasoning losses. Zero-initializing the scalar head first exposed a dummy-label normalization bug that invalidated and stopped that run; after a fixed-start correction it scored 70.217 ± 1.855 with Reasoning 53.247. This rejected zero initialization, fixed formal validation, and motivated a portfolio with guaranteed seed 42. Patch replay, a three-screen/three-final smoke, and artifact loading passed. It submitted with 1,232 seconds unused: a completeness limit, not a violation.

How the submitted method works. Baseline flow is pair → two scalar rewards → fixed chosen/rejected target → Bradley–Terry loss → LoRA and scalar-head updates. The candidate changes truncation, batching, grouped sampling, scheduling, engineering, and checkpoint policy, not its labels, loss, learning rate, LoRA rank, or global batch. Each screen restarts from the base, trains on 7,168 pairs, and ranks seeds by loss on 1,024 held-out training pairs. Formal replay completed 21 screens, guaranteed seed 42, selected loss-best alternatives 73 and 809, discarded screen weights, and retrained all three from base on all 8,192 pairs for 252/252 steps. No exploration weights, generated labels, external data, or pre-existing reasoning entered formal training.

Formal and evaluation evidence. The orchestrator used 40,820.71 of 42,600 injected seconds and stopped screening to preserve three complete finals plus harness reserve; no branch was truncated. All three artifacts loaded. Their numeric progress IDs encode 21 screens, branch order, and 252 steps—not one model's training progression. On maximize-direction full filtered RewardBench (n=2,985), seed 42/checkpoint 2101252 scored 73.686 ± 0.884; seed 73/2102252 scored 71.042 ± 0.880; seed 809/2103252 scored 65.158 ± 0.877, with Reasoning collapsing to 49.283. Holdout-best seed 73 lost to the guaranteed fallback, and second-best 809 was worst, so holdout selection failed to predict RewardBench. Seed 42's same-512-row final diagnostic, 73.023, approximately preserved its exploration proxy behavior, but proxy and full totals are not directly comparable. The evidence shows no improvement over the shipped reference; without paired reference uncertainty it does not establish significant inferiority.

Audit and takeaway. Formal mounts contained only the fixed base, pairs, and hash-matched patch; decontamination found zero prompt/response overlap, and no external input, hidden-row reconstruction, or hidden-value exposure was found. One GPU was isolated and runtime complied. Thus agent behavior is clean and protocol exposure is none found. Platform status is a confirmed defect because the first two final scorings were interrupted and stale receipts blocked retries until partial outputs were archived; later summary.json plus .complete receipts are official and valid. It demonstrated throughput diagnosis and replay engineering but left seed sensitivity and selection unresolved.

codex__ultrafeedback_bt_rm_rewardbench__gpt-5.6-sol__medium - Full English Analysis

1. Run identity, attempt selection, and lineage

2. Reader-facing overview

The task asks for a scalar reward model starting from a fixed Mistral-7B-Instruct-v0.2 and 8,192 decontaminated UltraFeedback chosen/rejected pairs. The shipped recipe applies a Bradley–Terry pairwise preference loss while training a low-rank adapter (LoRA) and a randomly initialized scalar head. Its concrete limitations were slow 4,096-token, microbatch-one training and severe sensitivity to the random reward-head initialization.

During exploration, the agent reproduced the baseline, then aligned training length with the evaluator at 1,024 tokens, raised the microbatch from one to four, reduced gradient accumulation from 64 to 16, and grouped examples by length. Global batch size and the learning rule stayed unchanged. This “fast LoRA” improved the 512-row visible proxy from the baseline's 71.612 at step 252 to 73.214 while reducing training time from 3,273 to 1,846 seconds. Full-parameter tuning, 0.05 pairwise label smoothing, and a zero-initialized reward head were rejected. Two weak random-seed replications then motivated a formal portfolio: screen many seeds using an UltraFeedback holdout, always retain known seed 42, and retrain three candidates from the fixed base.

Formal replay completed 21 holdout screens and three independent 252-step full-data retrains. All three artifacts loaded successfully. On the official maximize-direction rewardbench_v1_score over the 2,985-row filtered full split, seeds 42, 73, and 809 scored 73.686 ± 0.884, 71.042 ± 0.880, and 65.158 ± 0.877. The fixed seed-42 fallback was best, but it was 0.882 below the shipped same-protocol reference of 74.568936. Because the reference has no paired or repeated-run uncertainty, this establishes no demonstrated improvement, not a significance claim.

The strongest capability was turning diagnostic evidence and negative ablations into a valid, replayable three-artifact procedure. The central scientific failure was selection mismatch: the best UltraFeedback holdout seed did not win RewardBench, and seed 809 suffered a Reasoning collapse. Observable agent behavior was clean and no hidden-value exposure was found. There was, however, a confirmed platform retry-cleanup defect in the first two final evaluations; later summary-plus-completion receipts make the official results valid.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting model or artifact: a pinned Mistral-7B-Instruct-v0.2; its scalar head is untrained, so there is no meaningful starting score Available training data and assets: 8,192 fixed decontaminated UltraFeedback chosen/rejected pairs; exploration additionally exposes a fixed 512-row RewardBench proxy What the agent may change: training code, hyperparameters, sampling and schedule, engineering, and artifact-publication policy Fixed or prohibited components: base model, training pairs, formal mounts, final data, and four-section weighting; no external preference/evaluation rows, reward checkpoints, evaluation lookup, or reuse of exploration weights in formal replay Proxy evaluator: rewardbench_proxy_512 / maximize / fixed visible subset of full RewardBench / n=512 / descriptive propagated weighted-binomial standard error Final evaluator: rewardbench_v1_score / maximize / filtered full RewardBench / n=2,985, with 2,473 rows hidden during exploration / the same kind of descriptive standard error Artifact contract: a complete model or compatible PEFT delta plus scalar head, published as checkpoint-<increasing progress>; at most three valid artifacts may be retained simultaneously, and the official result is the best of at most three valid artifacts ``

Both evaluators score a pair of responses with the scalar head and test whether the preferred response receives the larger reward. The reported score is the unweighted mean of Chat, Chat Hard, Safety, and Reasoning section scores; within sections, examples are weighted by their counts. The reported uncertainty propagates finite-sample binomial variance. It is not seed variance, a training confidence interval, or a paired test. The proxy is a subset of the full split, not an independent identically distributed replication. Proxy and full scores are therefore reported separately. The final evaluator's same-512-row diagnostic can test approximate behavioral preservation, but full and proxy totals must not be treated as one learning curve.

3.2 How the baseline works

``text A prompt with chosen and rejected responses -> the fixed Mistral backbone encodes each response and a scalar head emits one reward per response -> the chosen/rejected relation supplies the target that the chosen reward should be larger -> an unsmoothed, temperature-one Bradley–Terry log-sigmoid loss on the reward difference is backpropagated -> the backbone remains frozen while a rank-128 LoRA delta and the scalar head are updated and exported as a PEFT-compatible artifact ``

The baseline left-truncates at 4,096 tokens, uses microbatch one and 64 accumulation steps for global batch 64, and requests 252 steps with seed 42, learning rate 5e-6, cosine decay, four warmup steps, weight decay 0.001, and checkpoints at steps 126 and 252. It retains at most three checkpoints. The agent initially identified three bottlenecks: a training length four times the evaluator's 1,024-token limit; little apparent value from a second pass; and very large early gradients, clipping, and reward-margin saturation caused by the random scalar head. It also hypothesized length and category bias, but subsequent diagnostics did not support an explicit correction.

4. Four-hour exploration and decision process

The active session ran from 17:43 to 21:23 UTC, about 3 hours 39 minutes. The first hour combined an exact baseline run with data/evaluator diagnostics. The next hour covered fast LoRA, full-parameter tuning, and smoothing. Roughly another hour tested seed and reward-head stability. The last forty minutes implemented, debugged, and replayed the formal orchestrator against a clean task copy. The submission receipt shows 1,232 seconds remaining and no active GPU work; the agent used that tail for replay and artifact audits and judged that another interpretable full training run would not fit.

U-01 - Baseline reproduction and the value of a second pass

Motivation and hypothesis. The agent needed a comparable proxy reference and wanted to determine whether step 252 justified another pass beyond the roughly one-pass step-126 artifact.

Concrete change and setup. There was no method change: seed 42, 4,096 tokens, microbatch one, accumulation 64, rank-128 LoRA, and the shipped loss were used. Both saved steps were evaluated.

Observed result. On maximize-direction rewardbench_proxy_512 (n=512), step 126 scored 71.017 ± 2.117, with Chat/Chat Hard/Safety/Reasoning of 76.858/55.447/65.671/86.093. Step 252 scored 71.612 ± 2.109, with 79.338/55.430/68.765/82.914. All 252 requested steps completed in 3,272.94 seconds. Editing run.sh while its outer shell was still active caused a read-offset error after training; summaries, weights, loading, and proxy evaluation remained valid.

Agent interpretation. The extra pass gained only about 0.59 overall while trading Reasoning for Chat and Safety. Together with clipping and saturated margins, the agent saw little marginal value in simply extending the same run.

Report assessment and confounds. The difference is below one reported proxy standard error and has no replicated seed, so it is not a robust step effect. The shell error occurred after saving and does not invalidate the artifact, although editing an active script was an engineering risk.

Decision and impact. Both steps became references; later work prioritized a faster configuration that could support more complete experiments.