Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents

Haifeng Zhang; Haoxuan Li; Jun Wang; Mengyue Yang; Qirui Mi; Yisen Wang; Zhijian Ma

arxiv: 2602.01869 · v3 · pith:EOLHNYZRnew · submitted 2026-02-02 · 💻 cs.AI

Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents

Qirui Mi , Zhijian Ma , Mengyue Yang , Haoxuan Li , Yisen Wang , Haifeng Zhang , Jun Wang This is my paper

Pith reviewed 2026-05-16 08:31 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM agentsprocedural skill learningexperience reusenon-parametric PPOSkill-MDPsequential decision makingmemory compression

0 comments

The pith

Skill-Pro lets LLM agents learn reusable procedural skills from past experiences without updating any parameters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

LLM agents often repeat the same reasoning in similar situations, wasting computation and risking inconsistent results. Skill-Pro turns raw interaction histories into compact, executable skills by first casting experiences into a Skill-MDP that explicitly defines when a skill activates, how it runs, and when it stops. Non-Parametric PPO then generates candidate skills via semantic gradients and uses a PPO Gate to verify they remain reliable and non-degrading, keeping only the highest-scoring ones in memory. The result is higher reuse, better task performance, and extreme memory savings across in-domain, cross-task, and cross-agent settings.

Core claim

Skill-Pro formalizes episodic interaction traces into executable Skills through a Skill-MDP that specifies activation, execution, and termination conditions. Non-Parametric PPO generates high-quality skill candidates using semantic gradients and applies a PPO Gate for verification, followed by score-based maintenance that preserves a small, high-quality procedural memory without any model parameter changes or capability loss.

What carries the argument

Skill-MDP formalization paired with Non-Parametric PPO, where semantic gradients propose candidates and the PPO Gate verifies executability and reusability before score-based memory retention.

If this is right

Agents achieve lower computational redundancy by substituting on-the-fly reasoning with direct skill retrieval in familiar situations.
Procedural memory remains compact while supporting performance gains in both in-domain and cross-task transfers.
Long-term autonomy improves because skills accumulate, refine, and transfer transparently across different agents.
Skill distributions become visible and interpretable, revealing how procedural knowledge evolves over time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same mechanism could be combined with external memory stores to handle skills that require persistent state beyond single episodes.
If verification remains reliable at scale, the approach may reduce dependence on ever-larger context windows for repeated tasks.
Cross-agent skill transfer opens the possibility of shared procedural libraries that multiple independent agents contribute to and draw from.

Load-bearing premise

Semantic gradients together with the PPO Gate can reliably produce skills that stay executable and non-degrading across repeated uses without manual filtering or performance drop.

What would settle it

A controlled test in which skills learned by Skill-Pro are reused ten or more times in the same recurring scenario and either execution success rate falls below the baseline or total memory size grows instead of compressing.

read the original abstract

LLM-driven agents excel at sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. This insufficient experience reuse leads to computational redundancy and instability. To bridge this gap, we propose Skill-Pro, a framework enabling agents to autonomously learn reusable procedural skills from interaction experiences without parameter updates. By formalizing a Skill-MDP, Skill-Pro transforms passive episodic narratives into executable Skills defined by activation, execution, and termination conditions to ensure executability. To achieve reliable reusability without capability degradation, we introduce Non-Parametric PPO, which leverages semantic gradients for high-quality candidate generation and a PPO Gate for robust Skill verification. Through score-based maintenance, Skill-Pro sustains compact, high-quality procedural memory. Experimental results across in-domain, cross-task, and cross-agent scenarios demonstrate that Skill-Pro achieves superior reuse rates and significant gains with extreme memory compression. Visualized evolutionary trajectories and Skill distributions further reveal how Skill-Pro transparently accumulates, refines, and reuses procedural knowledge to facilitate long-term autonomy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Skill-Pro's Skill-MDP plus Non-Parametric PPO setup for turning episodes into reusable skills with activation/termination conditions is the actual new piece, but the verification step's robustness against degradation is not yet convincing.

read the letter

The main contribution here is the Skill-MDP formalization that turns raw interaction traces into executable skills defined by activation, execution, and termination conditions, combined with a non-parametric PPO that uses semantic gradients for candidate generation and a PPO Gate for verification. This avoids any LLM parameter updates and aims for a compact, maintainable procedural memory that gets reused across scenarios. That framing and the score-based pruning mechanism do not appear in the cited prior work, so the combination counts as new. The visualizations of skill evolution and distributions are a practical plus; they make the accumulation process transparent without extra machinery. The paper also shows results in in-domain, cross-task, and cross-agent settings, which is the right scope for this kind of claim. The soft spot is the verification guarantee. The PPO Gate is supposed to keep skills executable and non-degrading, but the description relies on semantic similarity for scoring without showing ablations that test whether small condition drifts survive multiple reuses or distribution shifts. If the full experiments only report aggregate reuse rates and gains without isolating the gate's effect or providing error bars and baseline details, the performance claims rest on thinner ground than the formalization. The abstract states superiority but does not quantify it here, so the evidence strength is still hard to judge. This is for researchers building LLM agents that need to reduce repeated reasoning on recurring tasks. A reader focused on experience reuse or procedural memory libraries would get value from the MDP setup and the maintenance rule even if the empirical numbers need tightening. I would send it to peer review because the core idea is coherent enough to deserve detailed checking on the verification mechanics and reproducibility.

Referee Report

2 major / 2 minor

Summary. The paper introduces Skill-Pro, a framework for LLM agents to autonomously learn reusable procedural skills from interaction experiences without parameter updates. It formalizes a Skill-MDP to convert episodic narratives into executable skills defined by activation, execution, and termination conditions. Non-Parametric PPO is proposed, employing semantic gradients for candidate generation and a PPO Gate for verification, combined with score-based maintenance to sustain compact procedural memory. Experiments across in-domain, cross-task, and cross-agent scenarios claim superior reuse rates, performance gains, and extreme memory compression, with visualizations of evolutionary trajectories and skill distributions.

Significance. If the empirical claims hold with rigorous validation, Skill-Pro could advance LLM agent autonomy by enabling reliable experience reuse and reducing on-the-fly reasoning redundancy. The non-parametric approach and transparent skill accumulation represent a constructive direction for long-term procedural memory in agents.

major comments (2)

[Non-Parametric PPO description and verification mechanism] The central reusability claim depends on the PPO Gate and score-based maintenance preventing skill degradation and executability loss over repeated reuses (including under distribution shift in cross-task/cross-agent settings). However, no formal guarantee, proof sketch, or ablation isolating the gate's effect on subtle semantic-similarity errors is provided; this is load-bearing for the reported gains and must be strengthened with targeted experiments or analysis.
[Experimental results and evaluation] The abstract and framework description assert 'superior reuse rates and significant performance gains' with 'extreme memory compression,' yet supply no quantitative metrics, error bars, baseline comparisons, or statistical details. The results section must include these (e.g., reuse-rate tables, performance deltas, memory-size reductions) with explicit controls for post-hoc exclusions.

minor comments (2)

[Skill-MDP and Non-Parametric PPO] Clarify the precise definition of 'semantic gradients' and how they differ from standard gradient or similarity-based methods in the Skill-MDP formalization section.
[Figures and visualizations] The visualizations of evolutionary trajectories and skill distributions are mentioned but lack description of axes, metrics, or interpretation; add captions or a dedicated figure section explaining what they demonstrate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and commit to revisions that strengthen the empirical and analytical support for our claims.

read point-by-point responses

Referee: [Non-Parametric PPO description and verification mechanism] The central reusability claim depends on the PPO Gate and score-based maintenance preventing skill degradation and executability loss over repeated reuses (including under distribution shift in cross-task/cross-agent settings). However, no formal guarantee, proof sketch, or ablation isolating the gate's effect on subtle semantic-similarity errors is provided; this is load-bearing for the reported gains and must be strengthened with targeted experiments or analysis.

Authors: We agree that the absence of a dedicated ablation isolating the PPO Gate and the lack of a proof sketch constitute a genuine gap for the reusability claims, especially under distribution shift. In the revised manuscript we will add (i) an ablation study that directly compares Skill-Pro with and without the PPO Gate across in-domain, cross-task, and cross-agent settings, reporting executability rates and semantic-similarity error rates; (ii) a concise proof sketch explaining how the verification step combined with score-based maintenance bounds degradation; and (iii) targeted analysis of subtle semantic-similarity failures. These additions will be placed in a new subsection of the experiments and will be accompanied by the corresponding figures and tables. revision: yes
Referee: [Experimental results and evaluation] The abstract and framework description assert 'superior reuse rates and significant performance gains' with 'extreme memory compression,' yet supply no quantitative metrics, error bars, baseline comparisons, or statistical details. The results section must include these (e.g., reuse-rate tables, performance deltas, memory-size reductions) with explicit controls for post-hoc exclusions.

Authors: We acknowledge that the current results section presents only high-level claims without the requested quantitative detail. In the revision we will expand the experimental section to include: (a) tables reporting reuse rates, task success rates, and memory sizes with means, standard deviations, and error bars; (b) explicit baseline comparisons with numerical deltas; (c) statistical significance tests; and (d) a clear statement of any post-hoc exclusion criteria together with the full unfiltered results. These tables and controls will be added to the main results and the appendix. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper introduces Skill-MDP as a formalization that converts episodic narratives into skills with explicit activation/execution/termination conditions, then defines Non-Parametric PPO (semantic gradients + PPO Gate + score-based maintenance) as the mechanism for generation and verification. These are presented as novel constructs whose effectiveness is asserted via experimental results on reuse rates, performance gains, and memory compression across in-domain, cross-task, and cross-agent scenarios. No equations, fitted parameters, or self-citations are shown reducing a claimed prediction or result to the inputs by construction. The central claims rest on the proposed framework plus empirical validation rather than tautological equivalence or load-bearing self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on the domain assumption that episodic interaction traces contain extractable procedural structure and that semantic similarity can serve as a reliable gradient signal for skill generation; the paper introduces Skill-MDP and Non-Parametric PPO as new constructs without independent evidence outside the framework itself.

axioms (2)

domain assumption Episodic narratives from LLM agent interactions contain reusable procedural structure that can be formalized into executable skills with activation, execution, and termination conditions.
Invoked when the paper transforms passive experiences into Skills via Skill-MDP.
domain assumption Semantic gradients can generate high-quality skill candidates and a PPO Gate can verify them without causing capability degradation.
Central to the Non-Parametric PPO component described in the abstract.

invented entities (2)

Skill-MDP no independent evidence
purpose: Formalization that converts episodic narratives into executable skills defined by activation, execution, and termination conditions.
New construct introduced to ensure executability of learned skills.
Non-Parametric PPO no independent evidence
purpose: Variant of PPO that uses semantic gradients for candidate generation and a PPO Gate for skill verification without parameter updates.
Core algorithmic contribution claimed to enable reliable reusability.

pith-pipeline@v0.9.0 · 5504 in / 1729 out tokens · 38209 ms · 2026-05-16T08:31:09.441739+00:00 · methodology

discussion (0)

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