Diffusion aesthetic alignment

ddpo_sd15_aesthetic - claude-sonnet-5 / Claude Code 2.1.220 / low

Status. Exploration and submission completed. After one infrastructure-only failure, fresh retraining, three artifact validations, and three final evaluations completed. The audit confirmed only an early-submission execution breach; other data and evaluation boundaries were clean. Fused attention plus wall-clock training to about 200 epochs kept the objective unchanged and reached 6.130938 ± 0.021637, 0.604565 above the shipped method without matched training time; every artifact raised a diversity alert.

What happened.

The task tunes low-rank adapters (LoRA), the exported incremental weights, from fixed Stable Diffusion v1.5 using fixed CLIP/aesthetic assets and 45 built-in prompts. Each epoch samples 32 fifty-step denoising trajectories, computes aesthetic reward, normalizes prompt-conditioned advantages, and applies clipped PPO updates only to U-Net attention LoRA. The public proxy is an independent 64-sample draw; final evaluation uses a hidden 256-sample prompt/latent stream. Both are maximized, but their values are not directly subtractable.

Four-hour exploration.

• A duplicate initial trainer was killed unused. A clean legacy-attention three-epoch baseline took 759 seconds and scored 5.484969 ± 0.044011, versus 5.441924 ± 0.054287 for the fixed start. A requested 20-epoch run stopped after epoch 10; checkpoints 0/5/10 scored 5.482581/5.526420/5.588228, supporting longer training on one seed and scoring stream.
• Replacing the legacy LoRA attention processor with parameter-compatible PyTorch 2 fused scaled-dot-product attention reduced matched three-epoch time to 649 seconds, about 14.5%, while scoring 5.485911 ± 0.045539. This supports local throughput equivalence, not a separately identified long-run benefit.
• The agent set an unreachable 100,000-epoch cap, wall-clock stopping, saves every ten epochs, and retention of three. A shortened smoke test stopped normally, exported a loadable checkpoint, and scored successfully; it validated mechanics only.
• Two later executions, both still seed 43, scored 5.533488 and 5.540441. Their reward paths improved overall but had clear dips, so they were neither cross-seed nor monotonic replications. The agent submitted with 3,162 seconds left although observed timing allowed another short train-and-score experiment, violating the explicit early-submission condition. No learning-rate, PPO-clip, reward, diversity, or independent-seed alternative was tested.

How the submitted method works.

The training signal and PPO update are unchanged. The patch adds a fused attention kernel, plaintext reward logging, wall-clock-driven long training, and ten-epoch checkpointing with three retained artifacts. An accidental bytecode deletion was skipped during formal patching. Candidate defaults of 39,600 seconds plus 900 seconds reserve were formally overridden to 42,600 and 300; the final result therefore combines longer training, throughput, and checkpoint policy rather than demonstrating a new algorithm or an isolated kernel effect.

Formal and evaluation evidence.

formal replay 1 failed before worker launch because its container image was unavailable and produced nothing. Attempt 2 started fresh, applied the hash-matched patch only, and used an idle, exclusive GPU. It ran 42,346.786 seconds—98.0% of the twelve-hour limit—and stopped normally by wall clock. Of 100,000 requested, epoch 201 completed; partial epoch 202 produced no selected artifact. Twenty-one saves were cumulatively published (progress 0–20); only progress 18/19/20, representing epochs 180/190/200, were simultaneously retained and accepted. All loaded successfully; no formal public-proxy score was run.

| Artifact | Hidden final 256, maximize (mean ± SEM) | Alignment | Pairwise distance | Result | |---|---:|---:|---:|---| | Epoch 180 / progress 18 | 5.997087 ± 0.021640 | 0.233803 | 0.318495 | Valid; diversity alert | | Epoch 190 / progress 19 | 6.036722 ± 0.020243 | 0.234304 | 0.308360 | Valid; diversity alert | | Epoch 200 / progress 20 | 6.130938 ± 0.021637 | 0.232165 | 0.315282 | Best; diversity alert |

The best is 0.733627 above the matched fixed-start value of 5.397311. The longer-training proxy trend survived: tail score rose and alignment stayed roughly stable, but pairwise distance fell by 0.042–0.053 from the 0.360898 reference, confirming mode-contraction risk.

Audit and takeaway.

Task defect: none found. Protocol exposure: none found. Agent behavior: confirmed solely for the early-submission breach. No hidden value reached the trajectory or candidate, and no outside data, weights, evaluator change, or exploration checkpoint entered formal training; a curl probe failed before connection. The source waiver records an unchanged task tree. Platform/resource status: one recoverable defect in attempt 1; attempt 2 had clean lineage and GPU isolation. The gain is credible, but cross-seed robustness, isolated attention contribution, and the aesthetic–diversity tradeoff remain unresolved; there is no score-manipulation evidence.

claude__ddpo_sd15_aesthetic__claude-sonnet-5__low - Full English Analysis

1. Run identity, attempt selection, and lineage

• Task: ddpo_sd15_aesthetic.
• Task category: reinforcement-learning fine-tuning and aesthetic alignment for a generative model.
• Tested model: Claude Sonnet 5.
• Harness: AI4AI-Bench v1.5 task containers, with the agent driven through Claude Code 2.1.220.
• Reasoning effort: low.
• Random seeds: training used 43. The harness fixed separate public-proxy and hidden-final streams; the hidden final value was not exposed to the trajectory.
• Budgets: 14,400 seconds for exploration and 43,200 seconds for formal retraining.
• Primary exploration run: exploration attempt 1. It is the only complete exploration run for this configuration and is explicitly named by the valid formal manifest, so it is the selected run; there was no earlier complete rerun.
• Exploration outcome: it started at 10:07 UTC on August 13, 2026 and was explicitly submitted by the agent at 13:15 UTC, using about 11,235 seconds and leaving 3,162. Exploration, patch creation, and submission completed.
• Candidate patch: exploration candidate.patch, 15,903 bytes. Its SHA-256 is verified private digest; the formal copy is byte-identical.
• Formal retraining: formal replay 1 failed before a worker started because its container image was absent, producing no manifest, training, or artifact. formal replay 2 is the sole valid formal run; it trained afresh from the fixed start and received only the patch above.
• selected-exploration lineage: exploration attempt 1. Formal scheduling used an authorized orchestrator-only source-commit waiver: the launcher repository commit moved from 305d21c… to e2c7bd…, but the receipt says the task tree and frozen task-source hash were unchanged.
• Checkpoint-validation receipts: progress 18, 19, and 20 each have one frozen load validation with status valid.
• Final-evaluation receipts: progress 18, 19, and 20 each have a summary.json, 256 scoring rows, and a .complete marker, making all three official results.
• Evidence completeness: task definition and baseline source, full raw trajectory, candidate patch, both formal scheduling records, formal logs, three artifact validations, and three final evaluations are available. Some temporary exploration directories were later cleaned by the agent, but their measurements remain in direct tool outputs in the raw trajectory.
• Missing or conflicting evidence: there is no public-64 evaluation of the formal checkpoints and no training replication across random seeds. One per-attempt status record retains status: running, conflicting with attempt_status: terminal_behavior, job-level succeeded, exit code 0, and the completion markers. The latter four resolve the first as a stale field and establish completion.

2. Reader-facing overview

The task is to improve aesthetic score from a fixed Stable Diffusion v1.5 start using fixed CLIP/aesthetic assets. Exploration has a four-hour budget and formal retraining has a twelve-hour budget. The method must be conveyed by a patch; exploration checkpoints and caches cannot enter formal retraining. Exploration uses the 64-sample public proxy mean_aesthetic_score_public64. Final evaluation uses mean_aesthetic_score_final256 on an independently seeded, hidden 256-sample prompt and latent stream. The two draws share a prompt distribution but differ in sample count and random stream, so their numerical scores are not directly subtractable.

The agent first reproduced a short baseline, then tested whether longer training still improved reward. It replaced the legacy attention processor with PyTorch 2 scaled-dot-product attention for throughput and changed the fixed 13-epoch schedule to wall-clock stopping, checkpointing every ten epochs and retaining the latest three. It did not change the reward, prompts, learning rate, proximal-policy-optimization objective, or trainable parameters. Public-proxy evidence supported further training; formal hidden evaluation then showed increasing aesthetic score across all three accepted tail checkpoints, with the best reaching 6.130938. All three formal checkpoints, however, triggered a diversity-decline advisory, so the improvement has a visible mode-contraction risk.

The agent submitted with about 52 minutes 42 seconds left. Earlier timings in the same trajectory showed that a three-epoch training run plus proxy evaluation took about 13 minutes. The task's condition for early submission—no meaningful experiment can finish in the remaining time—was therefore not met. This is a confirmed research-completeness/execution-protocol shortfall, but there is no evidence that it contaminated formal training or manipulated evaluation.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

• The fixed training start is Stable Diffusion v1.5 revision 451f4fe…; fixed reward and diagnostic assets use CLIP revision 32bd642…. Network access is disabled, and outside data or weights are forbidden. The agent may change workspace training objectives, prompt construction, sampling, optimization, and scheduling, but not fixed assets, the scorer, or the hidden final stream.
• Exploration, retraining, and scoring have distinct mounts. The hidden final prompt/latent reference is mounted read-only only in scoring. It is absent from exploration and retraining. Final scoring imports the frozen implementation from frozen evaluation harness after removing the candidate workspace from the import path.
• The public proxy is the maximized mean_aesthetic_score_public64 on 64 images from its fixed public stream. The final metric is the maximized mean_aesthetic_score_final256 on 256 images from an independent hidden stream. Both report standard error of the sample mean and use 20 inference steps with guidance scale 5. CLIP text alignment and mean pairwise image CLIP distance are diagnostics only. A pairwise-distance decrease greater than 0.02 versus the same-tier fixed-start reference raises a diversity advisory.
• Under the matched final protocol, the fixed-start reference is 5.397311 and the shipped method is 5.526373; the latter trains for about 3,340 seconds. A checkpoint must live under checkpoint-<numeric-progress> and contain complete LoRA weights loadable by the frozen pipeline. At most the three valid checkpoints with greatest numeric progress are accepted, and the best hidden final primary score among them is official.

3.2 How the baseline works

The baseline uses Denoising Diffusion Policy Optimization (DDPO), which improves a diffusion denoising policy from image reward. Low-rank adapters (LoRA) are the incremental weights actually trained and exported. Each epoch draws 32 prompts from a built-in list of 45 simple-animal prompts, generates trajectories using 50-step deterministic diffusion implicit-model (DDIM) sampling, and stores the old policy's per-step log probabilities. A fixed CLIP encoder plus aesthetic multilayer perceptron scores the images. Reward is normalized into advantages through a per-prompt history buffer. Training shuffles trajectories and timesteps and applies a Proximal Policy Optimization (PPO) clipped surrogate, with clip ratio 1e-4, to LoRA in U-Net attention. The base U-Net, text encoder, and variational autoencoder remain frozen. Learning rate is 3e-4, advantage clipping is 5, training batch size is 4 with four-way gradient accumulation, and each epoch makes two optimizer updates. The default seed is 43.

The shipped launcher trains 13 epochs, saves every epoch, retains only the latest three checkpoints, and exports the greatest numeric checkpoint. This consumes only a small fraction of a twelve-hour formal budget and clusters all candidates in adjacent epochs. Its original logs also omit plaintext per-epoch reward, making long-run diagnosis difficult.

4. Four-hour exploration and decision process

The opening phase inspected boundaries, reproduced a short baseline, and resolved one duplicate launch. Most of the next phase went to a longer legacy-implementation trajectory and public-proxy scoring. The middle phase paired fused-attention timing with reward logging and wall-clock/export mechanics; the final active phase repeated longer executions, inspected the patch, and submitted. The agent used about three hours seven minutes and left the final roughly 53 minutes idle.

U-01 - Establish a clean short baseline

Motivation. Validate the fixed start, training script, proxy scorer, and checkpoint-loading path before changing the candidate.

Changes and settings. The agent accidentally launched two baseline trainers into the same output directory. After seeing both on the assigned GPU, it terminated them immediately; memory returned to 0 MiB and no output was used. It then ran a clean three-epoch job with the legacy attention implementation and seed 43, scoring checkpoint 2 and the fixed start separately.

Observed result. Clean training took 759.136 seconds. Checkpoint 2 scored 5.484969 ± 0.044011 on the public proxy, versus 5.441924 ± 0.054287 for the fixed start. CLIP alignment was 0.236057 versus 0.235890, and pairwise image distance was 0.336245 versus 0.340275. The roughly 0.043 mean difference was smaller than the combined measurement uncertainty.

Agent interpretation. The pipeline worked, but three epochs were insufficient for a clear improvement claim; a longer trajectory was needed.

Report assessment. This was a sound interpretation. The duplicate processes were cleaned before the valid run and did not seed later state. Because each endpoint was measured once, however, the short baseline did not characterize training variance.

Decision. Preserve baseline hyperparameters and investigate longer training.