Preference reward modeling

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

Status. Exploration, submission, formal retraining, validation, and final evaluation completed. Attempts 001/002 were infrastructure terminations; main exit fields conflict, but formal receipts and three summary/complete pairs agree. Best final 74.3623 is below reference 74.568936. Agent behavior is clean; benchmark-design exposure is confirmed because the visible 512-row proxy is part of the 2,985-row final set; scorer scheduling is a confirmed platform defect.

What happened. The task trains a scalar reward model from fixed Mistral-7B-Instruct-v0.2 weights and 8,192 decontaminated UltraFeedback pairs, maximizing RewardBench. The baseline formats and left-truncates chosen/rejected conversations, emits one scalar each, and trains LoRA plus a scalar head with Bradley–Terry loss. The agent tested optimization/seed alternatives and submitted temperature 2.0 with a wall-clock schedule.

Four-hour exploration. The proxy was rewardbench_proxy_512, maximize, n=512, stderr near 2; it differs from final scoring. Seed-42 baseline scores were 71.1235±2.1083 (126) and 72.0687±2.1196 (252). Micro-batch 2/accumulation 32 was slower in a five-step probe and made no checkpoint, so it was rejected; a benign JSONL diagnostic was corrected. Seed 43 gave 69.7254±2.1752 and 71.1313±2.0838, confirming variance but not adoption. Label smoothing 0.1 gave 65.7183±2.2921 at 126 versus baseline 71.1235±2.1083, so it was rejected. Temperature 2.0 gave 71.3451±2.1189 (126) and 73.2845±2.0370 (252), a matched +1.2159 proxy result and was adopted. Hardening changed defaults and saves. With 63 seconds left, measured full runs (~3,200 s) and screens (~1,620–1,652 s) left no time for a new experiment; timing is clean.

How the submitted method works. Each fixed pair is rendered with the same template, passed through frozen Mistral plus LoRA, and mapped to two scalar rewards. The chosen > rejected order supplies the Bradley–Terry target; logistic loss updates LoRA and the scalar head. Data, sampling, capacity, optimizer, and update rule are unchanged: temperature 1.0→2.0 is the scientific change; 4,096 requested steps, 256-step saves, and three retained checkpoints are schedule engineering. The best exploratory run was submitted; formal replay started fresh from fixed base/pairs, without exploration weights, caches, or proxy rows.

Formal and evaluation evidence. Formal orchestration set a 42,600-second wall limit plus 600-second reserve. Training completed 3,268/4,096 steps in 42,009 seconds; the formal phase receipt used 42,069.97/43,200 seconds (97.38%), and the wall callback wrote a complete checkpoint before stopping. Publications included 2560, 2816, 3072, and 3268; retention/validation left 2816/3072/3268, all loadable. The proxy is 512/2,985=17.1524% of the official final rows, while the remaining 2,473 rows form the exploration-hidden complement. Proxy and held-out values are final-score.log diagnostics, not separate formal proxy receipts.

| Progress | Official final metric | Diagnostics | Conclusion | |---|---:|---|---| | 2816 | rewardbench_v1_score, maximize, full n=2,985: 74.2194 | stderr 0.8238; sections Chat 92.18, Chat Hard 59.21, Reasoning 67.99, Safety 77.50; proxy n=512 75.0638±1.9071; held-out n=2,473 74.0471; train/final overlap 0 | valid, not best | | 3072 | same metric/split: 74.3623 | stderr 0.8227; sections Chat 92.18, Chat Hard 59.21, Reasoning 68.22, Safety 77.84; proxy 75.9515±1.8762; held-out 74.0339; train/final overlap 0 | official best | | 3268 | same metric/split: 74.1476 | stderr 0.8262; sections Chat 91.90, Chat Hard 59.10, Reasoning 68.09, Safety 77.50; proxy 75.3292±1.9160; held-out 73.9049; train/final overlap 0 | valid, later decline |

Checkpoint 3072 is best on the proxy and full official score, while checkpoint 2816 is marginally best on the 2,473 held-out rows (74.0471 versus 74.0339, a 0.0131 gap). This is far below the descriptive errors and is not a robust reversal, but it shows that the visible proxy rows partly drive the full-set winner. No significance claim is justified.

Audit and takeaway. Fixed data/model/evaluator, offline mounts, lineage, numbered attempts, queue/status, and corrections were checked; auto_retrain=false is only an explore field, while formal dispatch/manifest prove retraining. Literal searches of the complete trajectory (including tool returns), patch, and experiment text found no access to or reconstruction/use of the 2,473 held-out rows or any final result value. Agent behavior is clean. Protocol exposure is nevertheless confirmed at the benchmark-design level because the visible proxy is included in the official full-final score; that frozen score remains official but is not a purely unseen-row generalization measure, so the held-out diagnostic must accompany it. Training and validation used an idle single GPU, but each score log first hit an externally occupied GPU and was rejected before an isolated retry: platform/scheduling/resource isolation is a confirmed defect, not agent sharing. Formal underuse is clean because the wall callback followed checkpoint writing. The case demonstrates disciplined ablation and reproducible lineage; two complete seeds and a 512-row proxy leave causal, seed-independent improvement unestablished.

codex__ultrafeedback_bt_rm_rewardbench__gpt-5.6-luna__max - Full English Analysis

1. Run identity, attempt selection, and lineage

Direct lineage sources: job_manifest.json, exploration attempt 1 status, exploration attempt 2 status, exploration attempt 3 status, queue.tsv, and job status.

2. Reader-facing overview

The task asks for a scalar reward model trained from a fixed Mistral-7B-Instruct-v0.2 start and 8,192 decontaminated UltraFeedback preference pairs. The shipped baseline freezes the backbone and trains LoRA adapters plus a newly initialized scalar head with a Bradley–Terry pairwise logistic loss. It was stable and loadable, while a second seed showed substantial section-level variance; its proxy is a same-protocol baseline, not a number to compare with the final reference.

During four hours the agent established an exact baseline, tried a larger micro-batch throughput probe, replayed the baseline with seed 43, screened label smoothing 0.1, and screened then fully confirmed temperature 2.0. The probe was stopped without a checkpoint; seed 43 established variance; smoothing was rejected; temperature 2.0 improved the matched 252-step proxy score from 72.069 to 73.285. The submitted patch therefore changes the BT temperature and hardens the formal schedule to a 4,096-step ceiling with wall-clock stopping and 256-step saves.

Formal replay started from the fixed base and pair file rather than an exploration checkpoint. It completed 3,268 of 4,096 requested steps, stopped through the wall-clock callback while writing a complete checkpoint, and produced three valid scalar-head adapters. The best official final result is checkpoint 3072 at 74.3623 on all 2,985 final rows (maximize), below the shipped 74.568936 reference. Thus the proxy gain is a valid candidate signal, not evidence of a final benchmark improvement.

The strongest capability is disciplined experiment selection with section-level diagnostics and artifact/lineage checks. The main limitation is only two complete seeds, a 512-row proxy, and a best-midpoint final result that does not beat the reference. Those 512 visible rows are part of the official 2,985-row final set, a confirmed design-level exposure; no access to the other 2,473 held-out rows, network retrieval, or use of any extra external model was found. The 63 seconds remaining at submission were too short for a new interpretable experiment given the measured runtimes.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: mistralai/Mistral-7B-Instruct-v0.2, revision pinned private revision Available training data and assets: fixed 8,192-row pairs.jsonl (sha256 verified private digest), plus a fixed 512-row proxy for exploration Agent-editable surface: training code, objective, sampling, schedule, trainable capacity, export, and checkpoint policy under editable workspace Fixed or forbidden components: base weights, task training source, final RewardBench rows and weights, evaluator, offline boundary; no external rows, reward checkpoints, or evaluator lookup Proxy evaluator: rewardbench_proxy_512 / maximize / fixed 512-row subset of the final set / n=512 / descriptive weighted-subset binomial stderr about 1.88–2.29, not seed or paired uncertainty Final evaluator: rewardbench_v1_score / maximize / all 2,985 rows, with 2,473 held out from exploration / n=2,985 / the same descriptive stderr family, about 0.823–0.826 Artifact contract: a complete model or compatible adapter that loads on the fixed backbone and exposes one scalar reward head ``

The proxy is a fixed visible subset of the official final benchmark: 512/2,985=17.1524%, not a disjoint split. The other 2,473 rows are the exploration-hidden complement. Official scoring reloads all 2,985 rows and therefore includes the 512 proxy rows again. This is confirmed benchmark-design exposure, which is separate from agent exploitation: the agent used the intended proxy, and no access to or reconstruction of the 2,473 held-out rows was found. Proxy, held-out, and full-final scores must be reported separately; the proxy cannot stand in for unseen-row generalization.

The fixed boundaries are specified in the task instruction, task configuration, and phase declaration.

3.2 How the baseline works

One baseline update is:

``text chosen/rejected conversations plus the fixed Mistral backbone -> apply the chat template, left-truncate to 4,096 tokens, and emit one scalar logit per sequence -> the UltraFeedback preference order supplies the chosen > rejected pair signal -> Bradley–Terry loss -log sigmoid((r_chosen-r_rejected-margin)private filesystem location), plus an optional centering term -> freeze the 7.45B backbone and update LoRA adapters and a (1,4096) scalar score head, exporting the adapter ``

The shipped schedule uses 8,192 pairs, micro-batch 1, accumulation 64 (effective pair batch 64), learning rate 5e-6, cosine decay, weight decay 0.001, four warmup steps, gradient clipping 1, seed 42, and at most 252 steps. It saves every 126 steps with retention three. The agent's concrete diagnosis was optimization/seed instability rather than truncation or memory failure: only two chosen and one rejected training sequence exceeded 4,096 tokens, and the baseline was finite and stable. The agent did not establish a unique causal bottleneck.

The implementation is in run.sh and train.py.

4. Four-hour exploration and decision process

The agent spent the initial phase mapping the source, data, artifact, and evaluator contracts, then used roughly 3,200 seconds per full training run. It combined one throughput probe, two complete seeds, two objective directions, and final patch hardening. All performance choices used the same 512-row proxy; last-batch training accuracy was treated as dynamics evidence rather than evaluation.

U-01 - Establish a reproducible baseline and its bottleneck

Motivation and hypothesis. The exact shipped recipe was needed to separate implementation errors from real objective or schedule effects.

Concrete change and experimental setup. No source change; seed 42, all 8,192 pairs, length 4,096, 252 optimizer steps; evaluate progress 126 and 252.

Observed result. Training took 3,224.38 s and produced loadable checkpoints. Proxy scores were 71.1235±2.1083 and 72.0687±2.1196 (n=512, maximize). At step 252, sections were Chat 78.50, Chat Hard 56.06, Reasoning 84.55, and Safety 69.16. There were no non-finite values or memory failures; only three training sequences exceeded the length limit.

Agent interpretation. The agent kept step 252 as a clean fallback and chose to test throughput, seed variance, and regularization rather than alter truncation.

Report assessment and confounds. The proxy stderr is about 2.1, so the small midpoint-to-final change is not a robust convergence claim. The shipped reference is a final-protocol score and cannot be compared numerically with this proxy.

Decision and consequence. Preserve the baseline and test an equivalent-batch throughput change, followed by a complete second seed.