Preference reward modeling

ultrafeedback_bt_rm_rewardbench - gpt-5.6-sol / Codex / xhigh

Status. Exploration completed and the agent explicitly submitted. One linked formal replay completed; all three artifacts passed independent load validation and have official final evaluations (summary.json plus .complete). Observable agent behavior is clean.

What happened. The task trains a scalar reward model from fixed Mistral-7B-Instruct-v0.2 and 8,192 decontaminated UltraFeedback preference pairs. The baseline freezes its backbone and trains rank-128 low-rank adapters (LoRA) plus a scalar head with Bradley–Terry loss. After finding severe seed/section variance and loss–target misalignment, the agent submitted unchanged updates plus internal-holdout seed selection and a three-artifact hedge; the nominal best final score was 73.843 ± 0.897.

Four-hour exploration. The proxy was rewardbench_proxy_512, maximize, visible n=512; its weighted-subset binomial error is descriptive, not seed uncertainty. Seed 42 scored 72.041 ± 2.111 at step 126 and 72.252 ± 2.119 at step 252: a second data pass added 0.212 while Reasoning fell 2.73, so both checkpoints were retained. A 1,024-token, microbatch-8 speed probe was 9.8% faster but truncated 558 chosen and 473 rejected responses and had no proxy score; it was rejected, while untrained length rebalancing remained uncertain. Symmetric 5% label smoothing constrained margins but ended at 71.440 ± 2.146, 0.812 below baseline, so formal smoothing was zero. Changing only the seed to 123 produced 63.306 ± 2.121 despite lower train loss; seed 7 at step 126 produced 67.764 ± 2.193. The agent stopped one seed-7 startup after noticing that 126 total steps compressed cosine decay, then restarted with the comparable 252-step schedule. Averaging seed-42/123 endpoints failed at 68.079 ± 2.191; exact LoRA-delta averaging of seed-42 steps 126/252 reached 72.329 ± 2.104—only 0.077 above baseline, so it became a hedge, not a confirmed gain. Two reduced smoke runs validated selection, averaging, publication, and loading; a failed patch hunk was corrected, and smoke scores were not performance evidence.

How the submitted method works. Baseline flow is fixed preference pair → two scalar rewards → chosen/rejected label → Bradley–Terry loss → LoRA/head updates. The candidate preserves that target, loss, optimizer, 4,096-token context, and effective batch. It trains a full-data seed-42 fallback; trains ten seeds on the first 7,680 hash-ordered pairs and selects by pair accuracy, then loss, on the last 512; retrains the winner from the fixed base on all data; and exactly averages seed-42 steps 126/252. Checkpoints 1000, 2000, and 3000 implement that portfolio. Changes are data split, schedule, engineering, selection, and checkpoint policy—not the formal update rule. No generated reward, synthetic data, external model, rationale, or exploration weight entered formal replay.

Formal and evaluation evidence. Formal replay ran offline on one visible GPU for 39,734 seconds and completed twelve requested 252-step trainings—3,024 optimizer steps—with no truncation. All ten selector trials finished; seed 43 won at 0.7734 accuracy on the internal n=512 holdout and was retrained. Progress labels are publication labels, not optimizer steps; exactly three artifacts were retained. Official rewardbench_v1_score is maximized on full filtered RewardBench v1, n=2,985: checkpoint 1000, seed-42 endpoint, scored 73.835 ± 0.894; checkpoint 2000, within-seed average, scored 73.843 ± 0.897; checkpoint 3000, selected seed 43, scored 71.985 ± 0.890. The first two are effectively tied; the selected seed regressed especially on Reasoning. Formal-summary proxy slices were 72.506, 72.035, and 71.214 on n=512, but different splits are not direct gains. Averaging survived nominally; holdout selection did not.

Audit and takeaway. Formal replay used only fixed model/data and the identical-hash patch; decontamination found zero prompt/response overlap, and exploration weights or evaluator outputs did not cross the boundary. The hidden 2,473 non-proxy rows were not exposed, reconstructed, or used and did not affect the candidate. No external input, evaluator edit, GPU sharing, overtime, concurrency breach, unlinked attempt, or lineage break was found. Conclusions: behavior clean; no additional protocol/evaluation-boundary exposure; platform, scheduling, and resource isolation compliant. The run diagnoses and hedges seed risk, but its selector was misaligned and rank-256 averaging doubled adapter parameters for a 0.009-point nominal edge. One replay and non-paired descriptive errors cannot establish superiority over the endpoint or reference, or cross-task generality.

codex__ultrafeedback_bt_rm_rewardbench__gpt-5.6-sol__xhigh - Full English Analysis

1. Run identity, attempt selection, and lineage

Exploration ran from 2026-08-10 19:12:39 UTC to 23:05:11 UTC, about 3:52:31, and submitted with roughly 488 seconds remaining. Exit code 137 was not a training failure: the submission and lifecycle receipts record submission_origin=agent, termination=agent_explicit_submit, and no active training or evaluation process at termination. The exploration manifest's auto_retrain=false and retrain_phase_created=false describe only that run's local phase creation. They do not negate the upstream formal run that was subsequently scheduled.

An independent audit of same-configuration numbered directories, the formal queue, job and attempt statuses, and correction records found one formal attempt only. Its authoritative classification was terminal_behavior, its formal result was succeeded, and there was no resource-gated manifestless attempt, unlinked partial run, or applicable correction. The formal manifest, control status, source run ID, and patch hash close the lineage chain.

2. Reader-facing overview

The task is to train a scalar reward model from a fixed Mistral-7B-Instruct-v0.2 revision and 8,192 decontaminated UltraFeedback preference pairs. The baseline freezes the backbone and trains rank-128 low-rank adapters (LoRA, the exported trainable weight deltas) plus a scalar head with a Bradley–Terry objective that raises the chosen response above the rejected response. Its central limitation was not a crash or invalid artifact, but extreme seed- and section-sensitivity on RewardBench, coupled with training loss that did not predict target performance.

Within four hours, the agent reproduced the baseline at approximately one and two data passes, audited length bias and throughput, tested 5% symmetric label smoothing, measured independent-seed sensitivity, and tried cross-seed and within-seed weight averaging. The highest exploration point estimate was a within-seed average of seed 42 at steps 126 and 252, scoring 72.329 ± 2.104 on the 512-row proxy. Its 0.077-point edge over the step-252 baseline was far below the descriptive standard error. The submitted recipe therefore retained the original update rule, added a ten-seed sweep selected on an internal UltraFeedback holdout, and published three complementary artifacts: the seed-42 endpoint, its within-seed average, and the selected seed's endpoint.

Formal replay completed twelve 252-step trainings, or 3,024 optimizer steps in total, in 39,734 seconds (11:02:14). All ten holdout trials finished; seed 43 won with 0.7734 pair accuracy on 512 held-out training pairs and was retrained from the fixed start on all 8,192 pairs. All three published artifacts loaded successfully. On the official rewardbench_v1_score—higher is better, full filtered RewardBench v1, n=2,985—the seed-42 endpoint scored 73.835 ± 0.894, the within-seed average 73.843 ± 0.897, and the seed-43 endpoint 71.985 ± 0.890. The average was nominally best by only 0.009 point, while the internal holdout selector failed to produce the best final artifact.

The strongest capability was converting observed stochastic risk into an executable, hedged formal artifact policy. The principal failure was signal alignment: the seed selected by UltraFeedback holdout accuracy regressed sharply on final Reasoning. The boundary audit found no hidden-final value, external data, or exploration weight entering formal replay; observable agent behavior is clean. Because the reported standard errors are descriptive row-sampling quantities, not seed-level or paired model-difference uncertainty, the evidence cannot establish a significant improvement over the shipped reference.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting model or artifact: Mistral-7B-Instruct-v0.2 at revision pinned private revision; the initial scalar head has no meaningful trained score Available training data and assets: fixed 8,192-pair decontaminated UltraFeedback set, fixed base model, and the visible 512-row RewardBench proxy during exploration Mutable scope: training, construction, and orchestration code under editable workspace; up to three complete loadable scalar-reward checkpoints under run output area Fixed or forbidden scope: base model, preference pairs, formal evaluator, and final evaluation data; no network; formal replay must start fresh and cannot reuse exploration weights Proxy evaluator: rewardbench_proxy_512 / maximize / fixed stratified visible subset of the 2,985 final pairs / n=512 / weighted-subset binomial descriptive stderr, not seed or paired uncertainty Final evaluator: rewardbench_v1_score / maximize / full filtered RewardBench v1 / n=2,985, including n=2,473 outside the proxy / the same kind of descriptive stderr Artifact contract: run output area>; at most three simultaneously retained complete loadable scalar reward models; best valid final score wins ``

Both evaluators measure pairwise preference discrimination: the model assigns one scalar reward to each response and is correct when the chosen response scores higher. Accuracy is first computed for each fine-grained subset, weighted by row count within Chat, Chat Hard, Safety, and Reasoning, then averaged equally across those four sections. Both use the same chat template and left truncation to 1,024 evaluation tokens. The proxy is nevertheless a fixed 17.15% subset of the final split, so its score is a same-unit, different-split diagnostic—not a directly subtractable estimate of final improvement. Final summaries separately report the 2,473 non-proxy rows, making proxy representativeness inspectable.

The shipped reference artifact scored 74.569 under the same final protocol and took about 3,353 seconds to train. It is a single-run point-estimate reference, not the score of the fixed start: the base scalar head is untrained, so no interpretable pre-training baseline score exists. One reference seed also does not estimate seed variance or significance.

Each training row has only chosen, rejected, and a unique pair_sha256; all 8,192 hashes are unique and already sorted non-decreasingly. The formal candidate uses that existing order for its split. It adds no generated label, reward, rationale, or chain of thought. Final decontamination checked all 8,192 training pairs and found zero canonicalized prompt and response overlap with RewardBench.

3.2 How the baseline works

``text An UltraFeedback prompt with chosen and rejected responses -> apply the fixed chat template to each conversation and left-truncate to 4,096 tokens -> the fixed Mistral backbone plus trainable LoRA and scalar head emits two real-valued rewards -> the existing chosen/rejected preference supplies the target; loss is -log sigmoid(r_chosen - r_rejected) -> update only LoRA and the 1x4096 scalar head, then export adapters, head, and tokenizer configuration ``

LoRA is attached to the attention q, k, v, and o projections and the feed-forward gate, up, and down projections, with rank 128, alpha 256, and dropout 0.05. The baseline trains 335,548,416 parameters, about 4.51% of the 7,446,212,608-parameter composite. Bradley–Terry temperature is 1; margin, reward centering, and label smoothing are zero. Optimization uses bfloat16, 32-bit paged AdamW, learning rate 5e-6, weight decay 0.001, four warmup steps, cosine decay, and gradient clipping at 1. A one-pair microbatch accumulates 64 times for a global pair batch of 64. Seed 42 requests 252 steps and saves steps 126 and 252. Thus 252×64 presents 16,128 pairs—about 1.97 data passes, not one.

The agent initially called 252 steps one epoch, then corrected itself after calculating the dataset/global-batch ratio: step 126 is about 0.98 pass and step 252 about 1.97. It identified three concrete bottlenecks: the second pass offered little total proxy gain while hurting Reasoning; response length might be a shortcut; and RewardBench sections might vary severely by seed. One instrumentation caveat remained: logged pair accuracy and reward margin reflect only the last micro-pair, whereas loss is aggregated across the accumulation window. Those training diagnostics therefore cannot replace proxy evaluation. The candidate did not repair that logging semantic, but did add independent holdout validation.

4. Four-hour exploration and decision process

The early phase audited the contract, data schema, aggregation, and fixed assets, then completed a faithful baseline. The middle phase spent roughly training-run-sized blocks on label smoothing and an independent seed while interleaving length and throughput diagnostics. The late phase tested weight averaging, obtained a partial third-seed result, and converted the findings into formal orchestration. Two shortened end-to-end smoke tests, syntax checks, and model-load checks closed the submission. Unless otherwise stated, all exploration scores below are rewardbench_proxy_512, maximize, n=512. Each ± value is the evaluator's weighted-subset binomial descriptive standard error, not seed variance.

U-01 - Is the baseline training depth already sufficient?

Motivation and hypothesis. The agent first needed a faithful, loadable reference and a comparison between approximately one and two passes through the preference data. Its initial epoch-count error made the step-126 checkpoint especially useful for calibration.

Concrete change and setup. Nothing changed relative to the baseline. Seed 42 was trained once for 252 steps, with proxy evaluation at steps 126 and 252—approximately 0.98 and 1.97 passes.

Observed result. Training completed 252/252 steps in 3,289 seconds, peaking at about 22.97 GB. At 4,096 tokens, only two chosen and one rejected response were truncated. Step 126 scored 72.041 ± 2.111, with Chat 76.858, Chat Hard 56.077, Safety 67.647, and Reasoning 87.581. Step 252 scored 72.252 ± 2.119, with sections 78.516, 56.061, 69.584, and 84.849. Total score rose only 0.212 while Reasoning fell about 2.73 points.

Agent interpretation. After correcting the pass count, the agent concluded that the first pass captured most benefit. The second improved Chat and Safety but traded away Reasoning, so simply extending the same training was unattractive.

Report assessment and confounds. This directional reading is reasonable, but both checkpoints share a trajectory and proxy rows, and the score difference is much smaller than the descriptive errors. The evidence supports “no clear large gain,” not a statistically established superiority of either checkpoint.

Decision and impact. Keep step 252 as a dependable fallback and step 126 as an input to within-seed averaging; do not merely lengthen baseline training.