{"id":"26bc3fd5-7b79-4d8a-9efd-afb95c0e8beb","arxiv_id":"2602.01869","paper_version":3,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"Skill-Pro learns reusable natural-language procedural skills from LLM agent trajectories via semantic-gradient refinement and PPO-style verification, reporting higher reuse rates and extreme memory compression on ALFWorld and TextArena.","lead":"Skill-Pro lets LLM agents convert past experiences into compact, reusable instruction 'skills'—when to start, what steps to take, and when to stop—without retraining the model. It aims to make agents cheaper and more stable by replacing bulky episodic memory with small procedural units.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"PPO Gate is scored on the same trajectories used to generate candidates, so it can reward skills that restate past successes rather than generalize; its likelihood-ratio counterfactual is unvalidated on held-out episodes. This is the load-bearing risk.","rationale":"I read the paper in good faith: the central claim is that a frozen LLM can autonomously build a compact procedural skill library that transfers across tasks and backbones, and the Key mechanism is Non-Parametric PPO with the PPO Gate as trust-region verification. For that claim to hold, the PPO Gate must be a faithful predictor of whether a candidate skill will improve behavior when reused. The weakest point is exactly the Gate's validity: candidates are generated from the same batch on which they are scored, and the score is a token-likelihood ratio rather than a measure of reliable execution. This is not an accusation of dishonesty; it is a concrete, testable risk. The reader identified the same assumption as weakest, and the proposed test — validating the Gate on held-out trajectories and comparing with realized return — would settle it. The additional observation that Table 2's performance differences are mostly within one standard deviation strengthens the concern but does not change the verdict: the paper should remain conditional pending out-of-sample verification of the Gate and significance testing. I therefore recommend no change to the reader's CONDITIONAL verdict.","tokens_in":22473,"tokens_out":8171,"duration_ms":86221,"concrete_test":"Collect a fresh batch of trajectories under the old skill ω without using them to generate candidates. For each candidate ω′, compute the PPO Gate score J(ω′) on this held-out batch and compare it with J(ω′) computed on the generating batch. Then deploy the top-scoring candidates on new episodes and measure actual return. If the held-out Gate score does not correlate with realized return, or if the Gate accepts candidates that do not outperform ω on held-out episodes, then the PPO Gate is not a valid counterfactual verifier and the central claim loses its key safety mechanism.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central quality-control mechanism is the PPO Gate (Eq. 6, §4.2), yet its score L_CLIP(ω′) is computed on the same batch T(B) from which semantic gradients are extracted and candidates ω′ are generated (Algorithm 1, steps 2–3). This is an in-sample selection step: the LLM that proposes ω′ has already seen the rewards and failure patterns in the batch, so a candidate can score well by restating the action sequence of high-return trajectories rather than by improving the policy. Moreover, ρ_t(ω′) is a next-token likelihood ratio of the frozen LLM given two skill texts; it does not directly measure whether the agent will follow the skill's activation, execution, and termination conditions reliably in a new episode. A positive Gate score can therefore reflect 'sounds consistent with past good actions,' not 'works when reused.' The final evaluations are on held-out episodes, but the ablation claiming the Gate is indispensable (w/o PPO Gate performance drops) only shows that unconstrained candidates are harmful; it does not establish that the Gate selects for true counterfactual improvement. The cross-agent/cross-task results are suggestive, but Table 2 differences are mostly within one standard deviation (e.g., Mastermind-v0: 0.606±0.234 vs G-Memory 0.577±0.052; Qwen3-32B: 0.615±0.290 vs AWM 0.592±0.183), so the empirical 'significant gains' also rest on the same unvalidated verification.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes Skill-Pro, a framework for LLM agents to learn a pool of natural-language procedural skills from interaction trajectories without updating the base model. Skills are formalized as (activation condition, execution procedure, termination condition) and selected at decision time. Skill evolution is driven by 'semantic gradients' extracted from batched trajectories to propose candidate skills, a 'PPO Gate' that scores candidates by a clipped importance-ratio objective computed under the frozen LLM, and score-based pruning. Experiments in ALFWorld and TextArena Mastermind report higher reuse rates, better task performance, and much lower memory cost than episodic-memory baselines, with cross-task and cross-agent transfer.","tokens_in":22906,"tokens_out":6169,"duration_ms":55443,"significance":"If the verification mechanism holds, the paper would demonstrate a meaningful advance: a frozen LLM can autonomously compile compact, reusable procedural knowledge from interaction, with transparent skill lineages and no parameter updates. The Skill-MDP formalization and the explicit, inspectable skill representation are useful contributions; the code availability and the component-wise ablations are assets. However, the central claims of 'superior reuse rates' and 'significant performance gains' are not established by the current evidence because the PPO Gate is scored in-sample on the same trajectories used for candidate generation, and the reuse-rate metric is confounded by pool size. These concerns are addressable within the manuscript's scope, but they are load-bearing for the paper's main conclusions.","major_comments":[{"comment":"The PPO Gate score J(ω') = L_CLIP(ω') is computed on the same batch T^(B) from which the semantic gradients are aggregated and candidate skills ω' are generated. A candidate that simply restates the aggregated gradient's advice will assign higher likelihood to high-advantage actions in that same batch by construction, so a positive Gate score does not demonstrate counterfactual improvement on new episodes. The w/o PPO Gate ablation only shows that unconstrained candidates are harmful; it does not show that the Gate selects for genuine cross-episode gains. Please validate the Gate on a held-out batch (e.g., generate candidates from a training batch and score on a validation batch), or report the correlation between Gate scores and subsequent episodes' returns.","section":"§4.2, Algorithm 1 (steps 2–3)"},{"comment":"The reuse-rate metrics are defined as the fraction of stored units invoked at least once during evaluation. This definition mechanically favors small pools: Skill-Pro stores about 8 skills (816 total tokens / 102 tokens per unit) whereas baselines store hundreds or thousands of episodic units, so even incidental invocation yields much higher reuse rates for Skill-Pro. Table 1's 'superior reuse rates' are therefore not evidence of skill quality. Please add retrieval-precision-style metrics (e.g., precision@k, fraction of episodes with any retrieval, or performance at matched memory budgets) and report the stored-pool size per method.","section":"Appendix B.4, Section 5.1 (Reuse Metrics)"},{"comment":"Several headline differences are within one standard deviation of the best baseline, e.g., Mastermind-v0 0.606±0.234 vs G-Memory 0.577±0.052; ALFWorld OOD 0.909±0.287 vs AWM 0.900±0.042; cross-agent Qwen3-32B 0.615±0.290 vs AWM 0.592±0.183. In Table 4, PPO Gate Pass Rate carries standard deviations of 36–49 percentage points (e.g., 59.49%±49.09%), and Online Score differences are small with overlapping errors. Without paired significance tests (bootstrap or Wilcoxon over the 50 episodes) and effect sizes, the abstract's claim of 'significant performance gains' is unsupported. Please report per-episode paired differences and confidence intervals.","section":"Tables 2 and 4"},{"comment":"The importance ratio ρ_t(ω') = π_LLM(a_t|s_t,ω') / π_LLM(a_t|s_t,ω) is a next-token likelihood ratio of the frozen LLM given two skill texts. A positive L_CLIP score means the candidate text assigns higher likelihood to previously observed good actions, but it does not verify that the agent will reliably follow the candidate's activation, execution, and termination conditions in new episodes. The entire verification mechanism rests on this counterfactual assumption. The paper should include a direct test—for example, admitting candidates with positive versus negative Gate scores and comparing their subsequent episode returns, or otherwise measuring the correlation between Gate scores and held-out performance.","section":"Eq. (6), §4.2"}],"minor_comments":[{"comment":"Several rows contain garbled or inconsistently formatted entries (e.g., '0.111±0. 0.146±0.064' and '1165272675 ±414 (T)'). Please reformat the table so all means and standard deviations are legible and unit-type labels are aligned.","section":"Table 1"},{"comment":"The running baseline <code>\\bar{R}</code> is used to compute advantages and online scores, but its update rule and initialization are never specified. This is needed for reproducibility; please add the exact formula and hyperparameters.","section":"§4.2 and §4.3"},{"comment":"For the 'w/o PPO Gate' variant, the PPO Gate Pass Rate is reported as 100.00%±0.00%. This is true by construction and should be marked as 'N/A' or explained to avoid confusion.","section":"§5.3, Table 3"},{"comment":"The claim that skill selection patterns are 'remarkably stable' across LLM backbones and task difficulties is supported only by visual inspection of Fig. 5. A quantitative measure, such as a distributional distance (e.g., Jensen–Shannon divergence) between invocation distributions, would strengthen the claim.","section":"§5.4, Fig. 5"},{"comment":"The semantic-gradient example is helpful. It would be even more illustrative to show the corresponding aggregated gradient and the resulting candidate skill after the update, not just the per-trajectory gradient.","section":"Appendix D"}],"recommendation":"major_revision","confidential_remarks":"The core idea is promising, but the current evidence does not support the strong claims in the abstract. The in-sample PPO Gate and the confounded reuse-rate metric are load-bearing issues; they can be fixed with a held-out validation split and better metrics, and the statistical reporting can be improved without changing the method's scope. I therefore recommend major revision rather than rejection. I do not see a novelty disclosure problem; the NP-PPO formulation appears new relative to the cited memory-augmented and skill-based work."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe bottom line: Skill-Pro is a plausible framework for adaptive skill libraries for LLM agents, but its central verification gate has an in-sample circularity that the paper never resolves, and the empirical claims are weaker than the summary suggests. Still, the idea is worth engaging, and the cross-agent results keep it alive.\n\nWhat's new: the Skill-MDP formalism and the specific pipeline — semantic gradients aggregated over a batch, PPO-style likelihood-ratio gate, score-based pruning — are not present as a whole in the existing skill-library literature. The paper is also honest about its relation to Voyager, Memp, Expel, etc., even though it doesn't run them as baselines. The ablation study is well structured and shows the components matter.\n\nWhere it gets soft. The PPO Gate (Eq. 6, Algorithm 1) scores each candidate ω' on the same batch T^(B) that generated it via semantic gradients. That is in-sample selection. A candidate that restates the aggregated gradient's advice can assign high likelihood to high-advantage actions by construction, so a positive gate score doesn't establish counterfactual improvement — it may only show the candidate sounds consistent with past good actions. The likelihood ratio ρ_t(ω') is a next-token probability of the frozen LLM, not a measure of whether the agent will actually follow the skill's activation, execution, and termination conditions in a new episode. The paper doesn't validate the gate on held-out trajectories or a separate batch. This is the load-bearing risk, and it needs to be addressed.\n\nThe empirical tables also have large error bars. To pick two: Mastermind-v0 0.606±0.234 vs G-Memory 0.577±0.052; OOD ALFWorld 0.909±0.287. With no significance tests, 'significant gains' is hard to justify. The reuse-rate metric is defined as fraction of stored units invoked, which by construction favors the compact pool: Skill-Pro stores 816 tokens total, so nearly every unit gets invoked; RAG stores a hundred thousand tokens, so most units will never be used. That metric conflates memory design with reuse quality.\n\nOn the other side: the cross-task and cross-agent transfer results are the strongest evidence. The fact that skills learned with Gemma-2-9B transfer to a 70B model is a real signal. Also, the final evaluations are on held-out episodes, so whatever the gate does, the reported performance isn't circular in the strict sense — it's just underpowered.\n\nThe paper deserves a serious referee. The framework is clearly presented, the code is released, and the direction — accumulating procedural knowledge without parameter updates — is useful. But I'd want the PPO Gate validated on held-out batches, significance tests and exact hyperparameters, and at least one or two of the most relevant skill-library baselines (Voyager, Memp) before I'd trust the stronger claims.\n\nRecommendation: send to peer review, but expect heavy revision.","headline":"Skill-Pro is a plausible skill-library framework let down by an in-sample PPO Gate and noisy statistics, but the cross-agent transfer keeps it worth a serious look.","tokens_in":23385,"tokens_out":2596,"would_cite":false,"duration_ms":23009,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that a frozen LLM agent can convert its own interaction experience into a small, reusable library of natural-language procedural skills, and that this procedural memory outperforms raw episodic memory under extreme compress","keywords":["LLM agents","procedural memory","skill reuse","non-parametric optimization","PPO","semantic gradients","memory compression","long-term autonomy"],"falsifier":"Collect candidate skills that receive a positive PPO-Gate score but whose revised text is a near-paraphrase of the old skill plus a vague instruction; run both old and candidate skills on fresh episodes under the same frozen LLM. If candidates are accepted but do not produce better returns, the likelihood-ratio counterfactual is not a faithful predictor of behavioral improvement. A reader could settle this by checking whether PPO-Gate scores correlate positively with actual rollout return differences across many candidates.","tokens_in":22368,"feed_emoji":"🧠","tokens_out":3724,"duration_ms":43265,"temperature":0.7,"pith_summary":"The paper tries to show that an LLM agent can turn raw interaction episodes into reusable procedural skills — each defined by activation, execution, and termination conditions — and evolve that skill library purely by editing text, never updating the model. To do this, it introduces the Skill-MDP as a formal setting and Non-Parametric PPO: semantic gradients propose candidate skill revisions, a PPO-style gate filters candidates using likelihood ratios on past trajectories, and online scores prune low performers. On ALFWorld and TextArena it reports reuse rates above 80% across tasks and agents, better returns than episodic-memory baselines, and a stored memory of only a few hundred tokens. A sympathetic reader would care because if true, long-term agent autonomy could be cheap: a frozen LLM accumulates expertise as a compact, inspectable, transferable skill library instead of growing episodic logs or being fine-tuned.","feed_headline":"One skill pool of 816 tokens beats episodic memory logs","feed_subtitle":"Skill-Pro turns an LLM agent's past episodes into transferable procedural skills without touching model weights.","key_machinery":"The central object is the Skill, a natural-language procedure ω = ⟨Iω, πω, βω⟩ (activation, execution, termination), managed in a Skill-MDP pool. The argument-carrying mechanism is Non-Parametric PPO: semantic-gradient generation (LLM-based hindsight attribution and aggregation) plays the role of the gradient update on skill text, while the PPO Gate — a clipped likelihood-ratio surrogate computed under the frozen LLM on batch trajectories — plays the role of trust-region verification. Online advantage-style scoring governs pruning, sustaining a compact, high-quality skill pool.","core_discovery":"The central claim is that procedural memory is learnable by a frozen LLM through a non-parametric analogue of PPO. Experience is organized as a Skill-MDP, where the agent's memory is a pool of natural-language skills; each skill has activation, execution, and termination conditions. After each batch, hindsight attribution produces per-trajectory semantic gradients, aggregated into an update direction; candidate skills are then admitted only if a clipped-importance-ratio functional (the PPO Gate) is positive; score-based maintenance keeps only skills with positive online advantage. The paper argues this turns passive episodic narratives into executable, reusable procedures, and reports in-dom","pith_inferences":["If the central claim holds, agent memory could be treated as a compressible executable asset rather than raw data; organizations might ship a skill library instead of a fine-tuned model, and the same library could serve as an interface between different model versions.","The PPO Gate's reliance on frozen-LLM likelihood ratios suggests that verification power may scale with the model's instruction-following fidelity: on models that do not reliably follow written skills, a candidate that reads well may not change behavior, so Skill-Pro's gains could concentrate in models that already follow text reliably.","A testable extension not proposed by the paper: use the online score as a stopping signal for skill evolution, halting refinement of a skill lineage once its score plateaus, which could further reduce compute.","The high cross-agent reuse rates hint that the same pipeline could distill expertise from a large model into portable skill prompts for a smaller one, an application the paper only partially explores."],"forward_implications":["If correct, an agent can accumulate procedural memory that is orders of magnitude smaller than episodic alternatives — 816 stored tokens versus hundreds of thousands — while improving returns.","Skills learned on one benchmark task can be reused on harder variants and by different LLM backbones without retraining, suggesting the skills encode task structure rather than model-specific phrasing.","Ablation results indicate that both semantic-gradient candidate generation and PPO-Gate verification are needed; removing either degrades performance and pool quality.","Score-based pruning, not FIFO replacement, is what preserves long-term evolutionary gains, implying that memory quality is maintained by selective retention rather than simple recency.","Because skills are explicit text, the accumulated expertise is inspectable and auditable, which could facilitate debugging and human oversight of agent behavior."],"fun_headline_variants":["Learn skills, not episodes: Skill-Pro beats logs in 816 tokens","Frozen LLM learns skills via non-parametric PPO – no weight updates","Skill-Pro: 816 tokens of procedural memory outperforms full logs","Turn episodic logs into reusable skills with Skill-Pro's PPO Gate"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The verification step only works if re-scoring past actions under a candidate skill's text really tells you how the agent will behave under that skill in the future; if a rewritten skill can sound right without being followed right, a positive gate score proves nothing.","fun_headline_variants_meta":{"raw":{"variants":["Learn skills, not episodes: Skill-Pro beats logs in 816 tokens","Frozen LLM learns skills via non-parametric PPO – no weight updates","Skill-Pro: 816 tokens of procedural memory outperforms full logs","Turn episodic logs into reusable skills with Skill-Pro's PPO Gate"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000186,"raw_usage":{"total_tokens":1140,"prompt_tokens":703,"completion_tokens":437,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":447,"completion_tokens_details":{"reasoning_tokens":370}},"tokens_in":447,"tokens_out":437,"duration_ms":4974,"temperature":1.0,"reasoning_tokens":370,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-03T05:30:59.680985+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Collect candidate skills that receive a positive PPO-Gate score but whose revised text is a near-paraphrase of the old skill plus a vague instruction; run both old and candidate skills on fresh episodes under the same frozen LLM. If candidates are accepted but do not produce better returns, the likelihood-ratio counterfactual is not a faithful predictor of behavioral improvement. A reader could settle this by checking whether PPO-Gate scores correlate positively with actual rollout return differences across many candidates.","supporting_citations":[],"review_version":2}