Preference reward modeling

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

Status. Exploration, submission, formal retraining, three artifact validations, three final evaluations, and the boundary audit completed; source-run ID, patch hash, and completion receipts close the lineage.

What happened. The task fixes Mistral-7B-Instruct-v0.2 and 8,192 decontaminated chosen/rejected pairs. The baseline scores both responses and uses a Bradley–Terry pairwise logistic loss to train low-rank adapters (LoRA) plus a scalar reward head, but microbatch 1 with 64-step accumulation makes variable-length 4,096-token training inefficient. The agent sorted examples by length only inside each 64-pair optimizer update, preserving update membership while switching to microbatch 8/accumulation 8. Training became about 1.5× faster, and formal checkpoint 4411 achieved the best full rewardbench_v1_score, 73.8847, below the supplied reference 74.568936.

Four-hour exploration. All explore scores use the fixed 512-pair higher-is-better proxy; reported errors are descriptive, not seed or paired uncertainty. The baseline trained successfully but live editing of its script broke only post-training publication; independently loaded step-252 weights scored 71.874 ± 2.119. Within-update length packing reduced training time from about 3,254 to 2,164 seconds and scored 73.620 ± 2.098, with no section point-estimate decline, so it was adopted without claiming significance. Full-parameter, length-1,024 tuning scored only 70.427 at step 126 and was rejected, although its simultaneous length/LR changes prevent a general capacity conclusion. Zero-initializing the head with tenfold head LR scored 70.424; Chat rose to 91.77 but Reasoning collapsed to 52.45, so it was rejected and showed that low training loss was not a quality selector. Five-percent label smoothing peaked at step 126 (73.228 ± 2.139) then fell to 70.923 at 189; it became a diversity hedge. That run stopped at 195 when device memory exceeded its only visible trainer by about 145.6 GiB and step time doubled, so its tail was infrastructure-confounded. Finally, seed 17 scored 67.002 ± 2.159 with Reasoning 64.77, refuting seed stability. Artifact, scheduler-horizon, and three-checkpoint retention smoke tests established executability, not performance.

How the submitted method works. Baseline flow is fixed pairs → frozen Mistral plus scalar scores → chosen-is-better labels → hard pairwise logistic loss → LoRA and scalar-head updates. The anchor keeps that signal and update rule, changing only within-update ordering and batching; the hedge uses 95% forward plus 5% reverse pairwise loss. Formal replay starts every branch from the fixed base/data: a 252-step seed-42 anchor, a 126-step smoothed hedge retaining a 252-step scheduler horizon, then predetermined 252-step seeds until the wall-clock rule stops. It republishes the anchor, hedge, and lowest-training-loss search seed as the greatest three cumulative progress values. Explore weights, generated labels, external data, and caches do not enter formal replay.

Formal and evaluation evidence. Formal replay completed 18 candidates without failure in 38,403.546 of 43,200 seconds (88.897%). Its 41,400-second inner ceiling left 3,037 internal seconds, below the next-run estimate plus safety and a 900-second reserve. Relaxing that conservative ceiling might have fit one average branch, but this was a long-running wall-clock program, not a short fixed-step exit. Twenty-one publications yielded three loadable scalar-head artifacts. On the full higher-is-better split (n=2,985): 4411, a 252-step anchor, scored 73.8847 ± 0.8777 (Reasoning 83.46); 4412, a 126-step hedge, scored 71.6334 ± 0.9359; and 4413, a 252-step seed-1009 model selected by lowest training loss, scored 65.2973 ± 0.9088, with Reasoning 53.16. The visible proxy is 512/2,985=17.1524% of those official final rows, not an independent split. On the remaining 2,473 held-out rows, the three scores are 74.0552, 71.3799, and 64.9410, preserving the full-set ranking. The anchor retained the proxy direction and won, but loss-based selection failed. The 0.6843 reference gap is only a point-estimate comparison; neither single-run evidence nor descriptive errors establish paired or seed significance.

Audit and takeaway. Fixed model/data hashes, zero train/evaluation overlap, frozen metrics, source-only formal replay, offline receipts, and absence of external commands/data were verified. Evaluator-only held-out rows and result fields were literally cross-checked against the complete trajectory (including tool output), experiment commands, and patch: no hidden prompt, row-identifying response, final score, or split diagnostic appeared; tiny generic 1–4-character matches could not identify rows. Submission with 658 seconds left was clean because the fastest measured complete 126-step run alone took about 721 seconds and every existing checkpoint had been scored. Observable agent behavior is clean. Protocol exposure is confirmed at the benchmark-design level because the visible 512 rows are included in the official 2,985-row full score; the frozen score remains official but is not a purely unseen-row generalization measure. Platform/resource isolation has a confirmed defect limited to the smoothed explore tail, with no corresponding formal/final contamination. The case demonstrates effective shape-aware training engineering, but not statistically significant RewardBench improvement, cross-seed robustness, or reliable selection by training loss.

codex__ultrafeedback_bt_rm_rewardbench__gpt-5.6-sol__max - Full English Analysis (Codex CLI 0.146.0 / max)

1. Run identity, attempt selection, and lineage

The three earlier directories contain only idle-gate records: exploration attempt 1, exploration attempt 2, and exploration attempt 3. exploration attempt 4 has an explore manifest, submission receipt, and explore completion marker. The formal manifest establishes lineage through the source-run ID and exact patch hash, rather than directory-name similarity.

2. Reader-facing overview

The task fixes mistralai/Mistral-7B-Instruct-v0.2 and 8,192 decontaminated UltraFeedback preference pairs and asks for a scalar reward model. The baseline separately scores each preferred and rejected response, then uses a Bradley–Terry pairwise logistic loss to enlarge the preferred-minus-rejected margin. It trains low-rank adapters (LoRA) and a scalar head, but processes one pair per microbatch and accumulates 64 microbatches; highly variable sequence lengths make its 4,096-token training slow.

The agent first recovered and scored the baseline, then sorted examples by length only within each 64-pair optimizer update and switched to microbatch 8 with accumulation 8. This preserves the set of rows in every update while reducing padding. On the visible 512-pair proxy, the baseline endpoint scored 71.874 ± 2.119, whereas the packed endpoint scored 73.620 ± 2.098; training time fell from about 3,254 to 2,164 seconds. Full-parameter tuning and a zero-initialized, higher-learning-rate scalar head were rejected. A 5%-label-smoothed intermediate checkpoint was retained as a diversity hedge, and a poor seed-17 replication exposed large Reasoning variance.

The submitted patch contained no explore weights. Formal replay started fresh and used a wall-clock driver: one seed-42 hard-label anchor, one 126-step smoothed hedge, then 16 complete additional-seed runs. All 18 candidates completed, and the driver republished three selected, loadable artifacts. Their full RewardBench scores were 73.8847, 71.6334, and 65.2973; checkpoint 4411, the anchor, won but remained below the supplied single-run reference point 74.568936.

The main demonstrated capability was turning a padding diagnosis into a throughput improvement without changing optimizer-update row membership, then using section scores and negative ablations to manage risk. The largest scientific failure was selecting the lowest-training-loss search seed as the third formal artifact: its Reasoning score collapsed to 53.16, showing that training loss was not a useful RewardBench selector. Observable agent behavior was clean, with no access to or use of the 2,473 held-out rows; however, the protocol itself includes the visible 512-row proxy in the official 2,985-row full score, a confirmed design-level exposure. One smoothed explore run did suffer a confirmed platform-isolation defect: device memory exceeded the only visible training process by about 145.6 GiB while step time doubled; formal training and final scoring show no corresponding contamination.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: fixed Mistral-7B-Instruct-v0.2; its initial scalar head is untrained, so no meaningful starting RewardBench score exists Available training data and assets: exactly 8,192 fixed, decontaminated UltraFeedback chosen/rejected pairs; a visible 512-pair proxy during exploration Agent-editable surface: objective, sampling, optimization, full or parameter-efficient training, schedule, and artifact export under editable workspace Fixed or forbidden components: base model and pair source, final evaluator and section weights; no external preference/evaluation rows, reward checkpoints, or evaluation-specific lookup Proxy evaluator: rewardbench_proxy_512, higher is better, fixed visible subset, n=512, with weighted subset-binomial descriptive stderr Final evaluator: rewardbench_v1_score, higher is better, full n=2,985 (the proxy 512 plus 2,473 held out during exploration), with the same kind of descriptive stderr Artifact contract: a complete model or compatible delta on the fixed architecture, including a one-scalar reward head; at most the three greatest numeric-progress valid checkpoints are accepted ``

These boundaries are fixed by the instruction, task configuration, and phase declaration. At maximum length 1,024, the evaluator compares each response pair and counts a tie as one half. It combines 23 subsets into Chat, Chat Hard, Safety, and Reasoning with declared within-section weights, then equally averages the four sections. The 512 visible proxy rows are included verbatim in the 2,985-row official final set (17.1524%); only the other 2,473 rows are exploration-hidden. Proxy comparisons remain valid for intended candidate selection, but the proxy is not an independent final split. The frozen full-final score is official while not being a purely unseen-row generalization measure. Reported standard errors describe fixed-sample weighted subset variation, not training-seed variation or paired uncertainty between candidates.

3.2 How the baseline works

``text one chosen/rejected preference pair -> the frozen Mistral backbone encodes each conversation and a randomly initialized scalar head emits two rewards -> the pair file supplies the label that chosen should outrank rejected -> -log(sigmoid(r_chosen-r_rejected)) is optimized with AdamW and a cosine learning-rate schedule -> LoRA weights on attention/feed-forward projections and the scalar reward head change; the backbone remains frozen ``

The baseline run script uses all 8,192 pairs, length 4,096, microbatch 1, accumulation 64, 252 optimizer updates, learning rate 5e-6, seed 42, rank-128 LoRA, and checkpoints at steps 126 and 252. The training implementation left-truncates long sequences. The agent explicitly diagnosed length inefficiency: median length was about 400 tokens, only 25 pairs exceeded 2,048, yet one-pair microbatches prevented useful batching. It also found 712 pairs above the evaluator's 1,024-token limit and that the chosen side was longer in roughly 55% of pairs, so simply shortening training length could introduce a length-related confound.

4. Four-hour exploration and decision process

The selected run used 13,781 seconds. It spent roughly the first hour establishing and repairing a baseline, the middle on length packing, full tuning, head initialization, and label smoothing, and the final portion on seed sensitivity and a replay-safe formal driver. The agent then checked source, retention, loadability, and overlap before submission. All scores below use rewardbench_proxy_512, higher is better, n=512; ± is descriptive stderr and does not establish significance of a candidate difference.

U-01 - Establishing a usable baseline and diagnosing length inefficiency

Motivation and hypothesis. The agent needed actual baseline speed, progress-dependent proxy scores, and corpus lengths before choosing between an objective change and a throughput intervention.

Concrete change and experimental setup. It ran the shipped seed-42 recipe to 252 updates, retaining steps 126 and 252, and profiled token lengths. While that process was live, it edited run.sh in preparation for the next experiment.

Observed result. Training itself completed in about 3,254 seconds. Step 126 scored 71.068 ± 2.112; step 252 scored 71.874 ± 2.119. Postprocessing then failed with editable workspace line 118: EMENTATION: command not found because the live script had acquired stray text. The already-written trainer checkpoints were independently loaded and evaluated.

Agent interpretation. The agent treated the weights as trustworthy because the error occurred only in the publication wrapper, and took the length statistics as evidence for larger, length-homogeneous microbatches rather than dropping long examples.

Report assessment and confounds. The saved checkpoints and independent evaluations support a valid baseline comparison, but editing an executing script was an avoidable engineering error. A single seed gives no seed-variance estimate.

Decision and consequence. The recovered scores became the baseline; isolated output directories, source receipts, and smoke checks were incorporated into later work. Length packing became the primary direction.