Diffusion aesthetic alignment

ddpo_sd15_aesthetic - gpt-5.6-sol / Codex CLI / high

Status. Exploration, submission, formal replay, three artifact validations, and three official final evaluations completed; every final summary has .complete. A first attempt failed at storage gating; exploration attempt 2 supplied the hash-matched formal patch.

What happened. The task was to improve a fixed Stable Diffusion v1.5 model's aesthetic score on animal prompts. The baseline uses Denoising Diffusion Policy Optimization (DDPO): 50-step generated images receive rewards from a frozen CLIP-based aesthetic scorer, and a PPO-style clipped loss updates only U-Net attention low-rank adapters (LoRA). The agent found that the shipped 13 epochs underused 12 hours and that 45 random prompts rarely met the per-prompt normalizer's 16-sample threshold. It submitted unchanged DDPO updates inside a faster, longer, multi-seed schedule; the best hidden final score was 5.770235±0.023956.

Four-hour exploration. The proxy was public64 mean aesthetic score, higher is better, n=64 with standard error, and is not directly comparable to final256. Fixed and shipped epoch-4 scores were 5.441924±0.054287 and 5.498931±0.042570. A brief same-GPU train/eval overlap confounded runtime; later jobs were serialized. Twenty-step sampling took 409 s and scored 5.487423±0.053382 versus a confounded 1,301 s at 50 steps, so it was adopted for throughput. Grouping eight images per prompt activated per-prompt statistics but reduced coverage and scored 5.413899±0.045249; rejected. Generating all 32 together was 17% faster but scored 5.438290±0.051371; rejected. Longer training reached 5.599653 at epoch 16, 5.614769 at 26, and 5.594050 at 31; late differences were below uncertainty, making 26 a budget choice within a plateau. Scaling depended on horizon: 2× helped short training but hurt at epoch 16. At epoch 26, 1.25× scored 5.662340 versus raw 5.614769, while 1.5× fell to 5.604593; 1.25× was retained. Fresh seed 44 reached 5.503842 at epoch 16, establishing seed sensitivity. Recent reward ranked the two exploratory seeds correctly, motivating legal multi-start selection, but two seeds were weak evidence. Fresh-source, one-step, three-slot publication, and loader tests passed; a cleanup-bearing test was blocked before execution and rerun safely. With 38 minutes—less than another full run plus evaluation—the agent submitted.

How the submitted method works. Every branch starts from fixed SD1.5, samples the supplied 45 prompts, generates 32 images per epoch with 20-step DDIM, and applies frozen aesthetic rewards, original normalization, AdamW, and PPO-style DDPO to rank-4 attention LoRA for 26 epochs. Branches are ranked by last-five-epoch mean reward; another seed starts only if a full run plus reserve fits. The top two raw LoRAs and a 1.25× up-projection copy of the top one are published. These are sampling, schedule, engineering, and checkpoint-policy changes—not objective, signal, or update-rule changes. No exploration weight entered replay.

Formal and evaluation evidence. Formal replay completed 15 full branches, seeds 43–57, in 38,412.891 of 43,200 s and stopped when the next complete branch plus margin would not fit. It made 45 cumulative publications but retained three simultaneously; all loaded through Diffusers. On mean_aesthetic_score_final256 (higher, hidden, n=256), checkpoint-151, raw seed 47, scored 5.714855±0.025432; checkpoint-152, raw seed 48, scored 5.732940±0.021980; checkpoint-153, seed 47 scaled 1.25×, scored 5.770235±0.023956. None had alerts. Checkpoint-153 beat its matched raw version by +0.055379±0.016152, preserving the scaling direction. Yet training-reward winner 151 lost to second-ranked 152, exposing selector weakness. Declared same-protocol fixed and shipped reference means were 5.397311 and 5.526373, but their uncertainties are unavailable; proxy and final scores were not subtracted.

Audit and takeaway. Training used only fixed assets; no external input, evaluator change, or exploration-weight transfer was found. Protocol exposure is confirmed: a readable constant labeled mock/smoke hashes to the official final generation-seed hash and entered the trajectory. The agent later checked its prefix only in its patch worktree; it never used it for generation, training, or selection, so observable behavior remains clean and no result effect is visible. Scientific runs used one idle, unshared GPU; resource isolation has a confirmed defect only in the gating-only first attempt. Wrapper exit codes conflict, but receipts and patch SHA close lineage. Budget-aware ablation and orchestration were strong; 64 repeatedly tuned proxy samples and sparse seeds preclude universal 20-step, exact-26-epoch, or general training-reward-selector claims, and the case lacks an intact hidden boundary.

codex__ddpo_sd15_aesthetic__gpt-5.6-sol__high - Full English Analysis

1. Run identity, attempt selection, and lineage

2. Reader-facing overview

The task asks for an aesthetic improvement to a fixed Stable Diffusion v1.5 model. The shipped recipe uses Denoising Diffusion Policy Optimization (DDPO): the model generates its own images, a frozen CLIP-based aesthetic network supplies rewards, and a clipped policy-gradient objective updates only low-rank adapters (LoRA) in the U-Net attention layers. The agent identified two linked bottlenecks. The shipped 13-epoch, 50-denoising-step recipe used only a small part of the 12-hour formal budget, while 45 randomly sampled prompts made the nominal per-prompt reward normalizer unlikely to collect its required 16 observations per prompt.

During roughly 3 h 23 min of exploration, the agent measured the shipped curve and investigated shorter sampling, grouped prompts, larger batches, longer training, LoRA export scaling, seed sensitivity, training-reward selection, and formal publication mechanics. The submitted patch did not change the reward, PPO/DDPO loss, optimizer, or trainable modules. It shortened training-time image generation to 20 steps, ran as many independent 26-epoch branches as the formal wall clock safely allowed, selected two raw adapters by mean reward over their last five epochs, and emitted a third candidate by scaling the best adapter's LoRA up-projection matrices by 1.25.

Formal replay completed 15 independent branches and retained three loadable artifacts. On the official hidden mean_aesthetic_score_final256 metric, higher is better and n=256: checkpoints 151, 152, and 153 scored 5.714855, 5.732940, and 5.770235, with standard errors 0.025432, 0.021980, and 0.023956. The best was checkpoint-153, the 1.25-scaled export. The task declaration reports same-protocol fixed-model and shipped-recipe reference means of 5.397311 and 5.526373, so the result is a clear improvement on this evaluation; the missing reference uncertainties and limited training-seed evidence preclude a universal claim.

Observable agent behavior was clean, but protocol-side exposure is confirmed. A numeric constant labeled as mock/smoke in readable final-evaluator source hashes to the official final generation-seed hash, and the source fragment containing it entered the trajectory. The agent did not use it for generation, training, or selection, and it is absent from the patch, so no effect on the candidate is visible. The major scientific weakness is that the public proxy had only 64 fixed samples, seed-selection evidence was thin, and the training-reward ranking of the two raw final adapters reversed under hidden evaluation. There was also a confirmed platform defect in the gating-only first attempt, but the successful scientific runs each used one idle, isolated GPU.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: runwayml/stable-diffusion-v1-5@pinned private revision Available training data and assets: 45 simple_animals text prompts, model-generated images, a fixed CLIP-L/14 encoder and aesthetic MLP, and the supplied DDPO source Agent-editable surface: training source, hyperparameters, schedule, and compliant checkpoint-export logic under solution/ Fixed or forbidden components: starting SD1.5, aesthetic scorer, proxy and final evaluators, fresh formal start, one-GPU/no-network limits, and the hidden final seed Proxy evaluator: mean_aesthetic_score_public64 / higher is better / public proxy split / n=64 / standard error reported; fixed public seed, 20 inference steps, guidance 5 Final evaluator: mean_aesthetic_score_final256 / higher is better / independent hidden split / n=256 / standard error reported; the protocol intends the seed to be mounted only during scoring, but a readable mock/smoke constant accidentally equals it; 20 inference steps, guidance 5 Artifact contract: complete Diffusers-compatible LoRA weights in numeric checkpoints/checkpoint-<progress> directories; at most three retained valid checkpoints, with the best valid final score selected ``

The proxy and final evaluators use the same frozen aesthetic network and inference settings, but their sample counts and seeds differ. Scores may be compared within either protocol, not subtracted across public64 and final256 as if they were matched replications. The declaration additionally provides final256 reference means for the fixed model (5.397311) and shipped recipe (5.526373). Those are cautious same-protocol references, although no standard errors or per-example rows are supplied for them.

3.2 How the baseline works

``text Start from fixed SD1.5 and a randomly selected animal prompt -> generate an image through 50 stochastic DDIM denoising steps while recording policy log probabilities -> score the image with frozen CLIP-L/14 features and a frozen five-layer aesthetic MLP, then normalize reward into an advantage using per-prompt history when available or a global fallback -> form the PPO-style DDPO loss from the new-to-rollout probability ratio, clipped advantage objective, and AdamW optimization -> update only rank-4 LoRA weights in U-Net attention; keep the VAE, text encoder, base U-Net, and scorer frozen ``

Each outer epoch samples 32 prompt/image trajectories as four batches of eight, with guidance 5 and DDIM eta=1. The prompt-statistics buffer is 32 with a minimum count of 16. Training uses batch size 4 and gradient accumulation 4 for effective batch 16, approximately two optimizer updates per epoch, one inner epoch, learning rate 3e-4, PPO clip range 1e-4, advantage clipping at 5, and fp16. The shipped formal defaults request 13 epochs, save each epoch, and retain three checkpoints; the proxy profile overrides training to four epochs.

The agent explicitly diagnosed three limitations. Thirteen epochs took around one hour rather than exploiting the 12-hour allowance; 50-step sampling dominated cost; and drawing only 32 times per epoch from 45 prompts made the per-prompt minimum count largely unreachable. It also anticipated large stochastic-seed variance and treated LoRA export magnitude as a cheap post-training hypothesis.

4. Four-hour exploration and decision process

The agent first inspected the task and implementation, then established fixed-model, shipped-model, and intermediate-epoch proxy measurements. It used short runs to test sampling and batching structure, devoted most of the remaining time to 16–31-epoch behavior, a second seed, and export-scale scans, and reserved the final portion for a fresh-source patch test and formal publication smoke tests. Exploration ran approximately 12,173 of 14,400 seconds, leaving 2,267 seconds. Based on the measured cost of a full 26-epoch run plus evaluation, another relevant end-to-end experiment would not have fit.

U-01 - Establish the baseline curve and resolve concurrent-GPU confounding

Motivation and hypothesis. The agent needed to determine whether the shipped recipe improved the fixed model, whether the curve was still rising at four epochs, and what throughput and memory were available for a longer schedule.

Concrete change and experimental setup. It evaluated the fixed model, then trained the shipped four-epoch, 50-step configuration with seed 43 and evaluated its epoch-2 and epoch-4 artifacts on the same public64 proxy. Reward, batches, and DDPO updates were unchanged.

Observed result. The fixed model scored 5.441924±0.054287. Epoch 2 scored 5.474829±0.043319 and epoch 4 scored 5.498931±0.042570; the paired epoch-4 minus epoch-2 difference was +0.024102±0.016855, with 34/64 wins. Training took 1,300.862 s and peaked at about 58,598 MiB. An accidental overlap between training and proxy inference drove total GPU memory to about 62,132 MiB and slowed both jobs. The agent paused the exact trainer process, completed evaluation, and resumed it.

Agent interpretation. It treated the rising means as preliminary evidence for longer training, while explicitly rejecting the contended wall time as a fair speed baseline.

Report assessment and confounders. The direction is favorable, but the public sample is small and the paired increment is weak relative to uncertainty. Pausing preserved process state, so this is primarily a runtime confound, not evidence that the score is invalid.

Decision and impact. The original update was retained. Subsequent training and evaluation were serialized, and shorter sampling plus longer training became priorities.