One-shot model pruning

owl_wanda_opt6p7b_70pct - gpt-5.6-luna / Codex CLI 0.146.0 / max

Status. The sole exploration attempt completed and explicitly submitted; the sole formal replay exited successfully, all three artifacts validated, and every final score has both required receipts. Observable agent behavior is nevertheless confirmed noncompliant because exploration stopped early and the formal recipe materially underused its budget.

What happened. The task fixes dense OPT-6.7B and C4, forbids fine-tuning/external inputs, and asks for a loadable model with about 70% of decoder-linear weights zero and low perplexity. The supplied OWL/Wanda baseline collects activations from 128 calibration sequences, ranks weights by magnitude times activation scale, reallocates sparsity across layers, and zeros low scores without a loss, gradients, or an optimizer. The agent diagnosed the lack of pruning-error compensation and submitted an OPT implementation of SparseGPT, which uses approximate second-order curvature to choose removals and compensate retained weights.

Four-hour exploration. Proxy scores are lower-is-better WikiText2 validation perplexity over 122 length-2048 blocks; its block-level dispersion is not a score error bar. Removing OWL made Wanda deteriorate from 57.8382 to 181.7436, and a cumulative-ranking variant reached 325.2246, so both were rejected. Tuning OWL was non-monotonic: the best Lambda=0.12, Hyper_m=10 reached 42.2273, while weaker/stronger settings regressed.

Uniform 70% SparseGPT then reached 21.3580 with 128 samples and seed 0; seeds 1/2 scored 22.2979/22.2875. Adding the tuned OWL allocation worsened it to 24.5397, so the candidate kept uniform density. With damping 0.01, increasing calibration width from 128 through 256, 512, and 768 improved scores to 21.0454, 20.7298, and 20.5280, but 1024 regressed to 20.6880. Damping 0.001 at width 256 scored 21.2339, worse than the matched 0.01 result. At width 768, seeds 1/2 scored 20.6542/21.2105, supporting three independent formal constructions.

An incorrect single-seed override started the new default three-seed loop; the agent interrupted and corrected it. A direct evaluator launch also failed on file permissions, while the prescribed wrapper succeeded. Neither failed run entered selection. The agent then explicitly submitted while idle with 561 seconds left. Measured 128-sample construction plus evaluation takes at most about 451 seconds, so a named seed-0, 128-sample, damping-0.1 control could have completed and been compared with existing damping controls. This violates the task's continue-working condition and is confirmed.

How the submitted method works. For each seed 0/1/2, the recipe reloads dense OPT, samples 768 C4 sequences, accumulates a damped approximate Hessian, removes about 70% of weights in 128-column blocks, and propagates inverse-Hessian error corrections before exporting a full model. It uses no labels, rewards, synthetic data, external tools, fine-tuning, or exploration weights. The change combines algorithm/update rule, sampling width, damping, schedule, engineering, and checkpoint policy; the fixed model, C4, sequence length, and sparsity target remain unchanged.

Formal and evaluation evidence. Formal construction ended normally after the fixed seed list in 4,647.251 of 43,200 seconds (10.76%), leaving about 10 hours 42 minutes. It had no scientific or wall-clock stopping condition. Although three complete artifacts are score-eligible, this fixed short endpoint independently violates the required meaningful twelve-hour recipe and is confirmed.

Final testing uses a disjoint 140-block WikiText2 test split. Progress 768/seed 0 scored 19.672606 (sparsity 0.700000649; block SD 0.267917), progress 1536/seed 1 scored 19.777864 (0.700000642; 0.266164), and progress 2304/seed 2 scored 20.379365 (0.700000654; 0.266465). All load; progress labels are cumulative calibration work across independent models, not continued training. Seed ordering survived final evaluation, and all beat the supplied sparse test mean 53.3590. Proxy and test numbers cannot be directly subtracted, and their block SDs do not establish significance between the top two.

Audit and takeaway. Fixed model/data, evaluator integrity, one-GPU isolation, patch hash, and source-to-formal lineage all close. A literal search using ten independent long segments from the evaluator-only test input found zero matches in the complete trajectory/tool results/commands and patch; the agent saw only protocol metadata, did not reconstruct hidden text, and no external data or exploration artifact entered formal replay. Thus observable behavior is confirmed for the two execution breaches, protocol/evaluation-boundary exposure is none found, and platform/scheduling/resource isolation is compliant. The run demonstrates strong diagnosis, implementation, negative controls, and seed replication, but cannot establish global hyperparameter optimality or what a compliant twelve-hour recipe could achieve.

codex__owl_wanda_opt6p7b_70pct__gpt-5.6-luna__max - Full English Analysis (gpt-5.6-luna / Codex CLI 0.146.0 / max)

1. Run identity, attempt selection, and lineage

Task: owl_wanda_opt6p7b_70pct, which asks for a loadable, approximately 70% unstructured-sparse OPT-6.7B model with lower perplexity than the supplied sparse model.

Task family: one-shot large-language-model pruning and artifact construction.

Evaluated coding-agent model: gpt-5.6-luna.

Harness: Codex CLI 0.146.0.

Reasoning effort: max.

Seed: no coding-agent seed is recorded in the run manifest. The submitted pruning recipe uses calibration seeds 0, 1, and 2.

Exploration budget: four hours.

Formal-retraining budget: twelve hours, or 43,200 seconds.

Primary exploration run: exploration attempt 1.

Why this run was selected: it is the only complete exploration attempt identified by the manifest and numbered run tree, and it has a raw trajectory, explicit-submission receipt, lifecycle record, and .explore.complete receipt.

Earlier complete reruns and outcomes: none. Neither the analysis manifest nor the run/control directories contain an earlier exploration attempt for this model and effort setting.

Patch hash and verification: SHA-256 verified private digest. The exploration submission, formal dispatch, formal manifest, and directly hashed formal copy all agree.

Formal-retraining run: formal replay 1. It is the only numbered formal attempt for this exact logical configuration. The authoritative control status records succeeded; there is no applicable correction or exploration attempt 2.

selected-exploration lineage (selected-exploration lineage): exploration attempt 1, confirmed independently by the formal manifest, dispatch, and control status.

Final-evaluation receipts: each checkpoint has both a direct summary.json and a matching .complete, so all three are official final results.

Evidence completeness: the task and baseline, full raw trajectory, candidate patch, submission and lifecycle receipts, formal scheduling and execution records, checkpoint validations, and per-checkpoint final results are available. Formal replay did not evaluate the validation proxy; where the results table gives a proxy reference, it is explicitly the separately constructed exploration artifact with the same recipe. There is no aggregate final-results file, but the three complete per-checkpoint pairs are sufficient under the “best valid of up to three” rule.

Missing or conflicting evidence: the formal manifest retains exploration-lifecycle fields such as termination_reason=agent_explicit_submit. Those copied fields cannot establish why the formal process stopped. The formal outcome.json, retrain-result.json, and control status consistently show a normal, successful formal exit. No other conflict affects score provenance or artifact lineage.

2. Reader-facing overview

The task fixes a dense OPT-6.7B model and C4 calibration data, forbids fine-tuning, and requires roughly 70% of decoder linear weights to be zero. The supplied method combines activation-aware Wanda importance with outlier-weighted layer allocation (OWL): it directly zeros low-scoring weights and never performs gradient optimization. Its reproduced validation perplexity was 57.8382, while the task's three supplied sparse references have a mean test perplexity of 53.3590.

The agent first established that OWL allocation is crucial to Wanda and tuned its two allocation parameters. It then implemented an OPT-compatible version of SparseGPT, a pruning method that estimates local second-order curvature from calibration activations and compensates retained weights for pruning error. Uniform 70% SparseGPT reached 21.3580 validation perplexity with 128 calibration sequences; adding the tuned OWL allocation worsened it to 24.5397. Increasing calibration width improved the result through 768 sequences (20.5280) but regressed at 1024 (20.6880), so the submitted recipe used 768 sequences, damping 0.01, uniform per-layer sparsity, and seeds 0/1/2.

Formal replay independently reconstructed three models from the fixed dense start. All passed loadability and sparsity validation. On 140 blocks of the withheld WikiText2 test split, their perplexities were 19.6726, 19.7779, and 20.3794; the seed-0, progress-768 artifact was best. The seed ordering matched the three 768-sequence exploration proxies. Validation and test are disjoint and use 122 and 140 blocks respectively, so the ordering can be compared, but their absolute scores must not be treated as samples from the same protocol.

The run demonstrates a strong diagnosis-to-implementation loop and produces a large, reproducible improvement over the supplied sparse reference. It also has two independently confirmed protocol breaches. The agent explicitly submitted while idle with 561 seconds left, even though measured runtimes show that a named 128-sequence high-damping control could fit. The formal script then ended normally after a fixed three constructions, using only 10.76% of the twelve-hour budget and having no scientific stopping condition. No hidden test text, external data, exploration weights, evaluator modification, or GPU sharing was found; platform scheduling and artifact lineage were sound.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

Starting model or artifact: the task-mounted dense OPT-6.7B only. Its test perplexity is 10.860456, but zero sparsity makes it ineligible as a candidate.

Available training data and assets: exploration can read the fixed dense model, pinned C4 calibration data, and WikiText2 validation split. Formal replay receives the dense model, C4, and the source patch, but neither WikiText2 validation nor test.

What the agent may change: workspace source, pruning algorithm, calibration sampling, hyperparameters, formal recipe, and the publication policy for at most three retained checkpoints.

Fixed or prohibited changes: the starting weights and data source cannot be replaced; fine-tuning, network access, external data/models, exploration-produced weights, the WikiText2 test split, and evaluator modification are forbidden. Recomputed decoder-linear sparsity must lie in [0.699, 0.701], and the exported model must load with the standard interface.

Proxy evaluator: WikiText2 validation perplexity, lower is better. Actual runs use 122 non-overlapping blocks of 2048 tokens. The receipt's block_nll_per_token_sd is dispersion across fixed blocks, explicitly not a score standard error or confidence interval.

Final evaluator: WikiText2 test perplexity, lower is better, with exactly 140 blocks of 2048 tokens. It independently recomputes sparsity from weights. Its block-level standard deviation is likewise diagnostic dispersion, not a perplexity error bar.

Artifact contract: formal replay may retain up to three accepted checkpoints; if more are published, only the greatest-progress three count. Selection is the best valid final result among at most three. One complete construction can be a valid artifact, but the source task separately requires a formal recipe designed to use the twelve-hour budget meaningfully and forbids a normal stop solely at a short fixed step/epoch endpoint.

The proxy and final implementations use the same tokenization and perplexity arithmetic, but the splits have zero overlap and different block counts. Comparisons within one protocol are valid; direct subtraction across validation and test is not evidence of a performance delta or statistical significance.

3.2 How the baseline works

Fixed dense OPT-6.7B plus 128 C4 calibration sequences of length 2048

→ run sequential layer forwards and collect input activations for every decoder linear module; Wanda scores each weight as |W| × sqrt(mean(x²))

→ OWL estimates layer sensitivity from outlier-weight prevalence and reallocates layer sparsities around the 70% global target; these scores and target densities are the selection signal, with no labels or rewards

→ zero the lowest-scoring weights within each output row, with no loss, gradients, optimizer, or fine-tuning

→ export the complete model with pruned decoder weights and all remaining values unchanged.

The baseline defaults to seed 0, 128 calibration sequences, Lambda=0.08, and Hyper_m=5, and publishes one complete construction. After reproducing 57.8382 validation perplexity, the agent identified two concrete bottlenecks: OWL's layer allocation might be poorly tuned, and Wanda's local magnitude-activation ranking cannot compensate for the output error caused by removed weights. That second diagnosis motivated the eventual method change.

4. Four-hour exploration and decision process

The agent began by reading the task, evaluators, and baseline and establishing a proxy baseline. It used the earlier part of the budget to isolate Wanda allocation and tune OWL, the middle to implement and smoke-test SparseGPT, and the later part to scan calibration width and damping, replicate across seeds, debug the formal seed loop, audit the patch, and submit. Repeated polling and inspections without a new conclusion are consolidated below. Unless stated otherwise, all scores in this section are lower-is-better perplexities on 122 WikiText2 validation blocks.

U-01 - Is Wanda limited by layer allocation or by within-row ranking?

Motivation and hypothesis. The agent first asked whether the supplied method's benefit came from OWL's cross-layer allocation or Wanda's within-row importance measure. It also tested the repository's cumulative-threshold variant as a possible better within-row rule.

Concrete change and experimental setup. With seed 0, 128 C4 sequences, and approximately 70% global sparsity, it compared the default OWL/Wanda path, uniform 70% Wanda without OWL, and uniform Wanda with the cumulative variant.

Observed result. Default OWL/Wanda scored 57.8382 at sparsity 0.699909, block NLL-per-token standard deviation 0.28225, and 156.6 seconds construction time. Uniform Wanda scored 181.7436 and its cumulative variant 325.2246. Both uniform runs remained sparsity-valid and took roughly 61–62 seconds.

Agent interpretation. The agent concluded that OWL allocation is essential for Wanda at this sparsity, while the cumulative variant damages within-row selection.

Report assessment and confounders. The matched seed, sample count, and evaluator make the direction of both negative controls persuasive. A single seed does not support a significance claim, and the reported block deviation is not a score error bar.

Decision and impact. Uniform and variant Wanda were rejected. Default OWL/Wanda became the base for allocation tuning.