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arxiv: 2605.09359 · v1 · submitted 2026-05-10 · 💻 cs.LG · cs.AI

Recognition: no theorem link

Skill-R1: Agent Skill Evolution via Reinforcement Learning

Yash Vishe , Rohan Surana , Xunyi Jiang , Zihan Huang , Xintong Li , Nikki Lijing Kuang , Tong Yu , Ryan A. Rossi
show 3 more authors
Jingbo Shang Julian McAuley Junda Wu
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:57 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords reinforcement learningskill optimizationagentic LLMsbi-level optimizationpolicy optimizationfrozen modelsverifiable rewardsrecurrent optimization
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The pith

Skill-R1 trains a lightweight generator with bi-level RL to evolve skills that improve a frozen task LLM over generations.

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

The paper establishes that recurrent skill optimization for language agents can be achieved by training only a small skill generator instead of the main model. This generator learns to produce and revise natural language skills using feedback from verified rollout outcomes across multiple generations. It employs a bi-level objective that rewards both good rollouts under a skill and skills that lead to better performance in later rounds. This approach matters if true because it enables low-cost adaptation even for closed-source models and targets gains on hard multi-step tasks where skills provide the most leverage.

Core claim

Skill-R1 is a reinforcement learning framework for instance-level recurrent skill optimization from verifiable rewards. Rather than updating the task LLM, it trains a lightweight skill generator conditioned on the task context, prior rollouts, and verified outcomes to produce skills that steer a frozen task LLM. The optimization uses a bi-level group-relative policy optimization objective with intra-generation advantages comparing rollouts under the same skill and inter-generation advantages rewarding revisions that improve behavior across successive generations.

What carries the argument

the lightweight skill generator that conditions on task context, prior rollouts, and verified outcomes to produce revisions for a frozen task LLM, optimized via bi-level GRPO

If this is right

  • Consistent performance gains over no-skill baselines and standard GRPO on benchmarks with verifiable rewards.
  • Particularly strong improvements on complex, multi-step tasks.
  • Preserves black-box compatibility allowing use with closed-source models.
  • Adaptation is substantially cheaper than updating the task LLM itself.

Where Pith is reading between the lines

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

  • Skills developed this way might accumulate into reusable libraries if the generator is applied across multiple tasks.
  • The separation of skill generation from task execution could make it easier to debug or inspect agent behavior.
  • This recurrent setup suggests potential for online, lifelong skill improvement during deployment.

Load-bearing premise

That revisions from the skill generator, informed only by task context and verified outcomes, will reliably produce better skills that improve the frozen task LLM's behavior in future generations.

What would settle it

If task performance fails to improve or declines after several generations of applying revised skills on a benchmark with verifiable rewards, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2605.09359 by Jingbo Shang, Julian McAuley, Junda Wu, Nikki Lijing Kuang, Rohan Surana, Ryan A. Rossi, Tong Yu, Xintong Li, Xunyi Jiang, Yash Vishe, Zihan Huang.

Figure 1
Figure 1. Figure 1: Overview of Skill-R1. Recurrent Skill Evolution iteratively generates skills, rolls out a frozen task LLM, and stores verifier-scored outcomes in an evolutionary history. Bi￾level GRPO Optimization computes GRPO advantages from both intra-generation relative performance and inter-generation progress, and uses it to update the skill generator while keeping the task LLM frozen. Thus, the instance-level perfo… view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy and reward curves across 5 generations. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
read the original abstract

Agentic large language models often rely on skills, reusable natural language procedures that guide planning, action, and tool use. In practice, skills are typically improved through prompt engineering or by aligning the task LLM itself, which is costly, model-specific, and often infeasible for closed-source models. Skill optimization is not a one-step problem but a recurrent process with two coupled levels of credit assignment: a useful skill must improve rollout quality under current conditioning, while a useful revision must turn observed outcomes into a better skill for the next round. We propose Skill-R1, a reinforcement learning framework for instance-level recurrent skill optimization from verifiable rewards. Rather than updating the task LLM, Skill-R1 trains a lightweight skill generator that conditions on the task context, prior rollouts, and their verified outcomes to produce skills that steer a frozen task LLM. This preserves black-box compatibility with both open- and closed-source models while making adaptation substantially cheaper than model-level updates. Skill-R1 proceeds over multiple generations: at each step, the current skill induces rollouts whose verified outcomes are fed back to produce the next revision. To optimize this recurrent process, we introduce a bi-level group-relative policy optimization objective combining intra-generation and inter-generation advantages. The intra-generation term compares rollouts under shared skill conditioning, while the inter-generation term rewards revisions that improve behavior across successive generations. Together, these provide a principled objective for directional skill evolution rather than one-shot self-refinement. Empirically, Skill-R1 achieves consistent gains over no-skill baselines and standard GRPO across benchmarks with verifiable rewards, with particularly strong improvements on complex, multi-step tasks.

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.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces Skill-R1, a reinforcement learning framework for recurrent, instance-level skill optimization in agentic LLMs. A lightweight skill generator is trained to produce natural-language skills conditioned on task context, prior rollouts, and verified outcomes; these skills steer a frozen task LLM without updating its parameters. Optimization uses a bi-level group-relative policy optimization (GRPO) objective that combines intra-generation advantages (comparing rollouts under the same skill) with inter-generation advantages (rewarding revisions that improve behavior across successive generations). The central claim is that this yields directional skill evolution and consistent empirical gains over no-skill baselines and standard GRPO, with larger benefits on complex multi-step tasks.

Significance. If the bi-level objective demonstrably enables multi-generation improvement beyond one-shot GRPO and the empirical gains are reproducible, the approach would offer a low-cost, black-box-compatible route to adapting agent behavior. Separating skill evolution from task-model updates is a useful architectural distinction for closed-source models and resource-constrained settings.

major comments (3)
  1. [Bi-level objective (abstract and method description)] The bi-level GRPO objective is presented as providing directional credit assignment across generations, yet no ablation isolating the inter-generation advantage term (versus intra-generation GRPO alone) is reported. This is load-bearing for the recurrent-evolution claim; without it, the necessity of the inter-generation component remains unverified.
  2. [Empirical evaluation] The empirical section asserts consistent gains over baselines with particularly strong results on complex tasks, but supplies no quantitative metrics, benchmark names, rollout counts, or ablation tables. This prevents assessment of effect sizes and reproducibility.
  3. [Skill generator architecture and conditioning] The skill generator is conditioned only on task context, prior rollouts, and verified outcomes; the manuscript does not analyze or experiment on whether this featurization avoids trajectory memorization or policy collapse over generations.
minor comments (1)
  1. [Abstract] The term 'standard GRPO' is used without a brief definition or citation, which may hinder readers unfamiliar with the baseline.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript introducing Skill-R1. We appreciate the recognition of the bi-level optimization approach and its potential for low-cost adaptation in agentic settings. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation of the bi-level objective, empirical results, and analyses of the skill generator.

read point-by-point responses

  1. Referee: [Bi-level objective (abstract and method description)] The bi-level GRPO objective is presented as providing directional credit assignment across generations, yet no ablation isolating the inter-generation advantage term (versus intra-generation GRPO alone) is reported. This is load-bearing for the recurrent-evolution claim; without it, the necessity of the inter-generation component remains unverified.

    Authors: We agree that an explicit ablation isolating the inter-generation advantage term is necessary to substantiate the recurrent-evolution claim. While the method section motivates the bi-level structure through the coupled credit assignment requirements, we will add a dedicated ablation study in the revised manuscript comparing full bi-level GRPO against intra-generation GRPO alone, reporting performance differences on the evaluated tasks to verify the contribution of the inter-generation term. revision: yes

  2. Referee: [Empirical evaluation] The empirical section asserts consistent gains over baselines with particularly strong results on complex tasks, but supplies no quantitative metrics, benchmark names, rollout counts, or ablation tables. This prevents assessment of effect sizes and reproducibility.

    Authors: We acknowledge that the reviewed version lacks the requested quantitative details. We will revise the empirical section to include specific benchmark names, quantitative metrics with effect sizes, rollout counts per experiment, and expanded ablation tables, enabling full assessment of reproducibility and the magnitude of improvements, particularly on multi-step tasks. revision: yes

  3. Referee: [Skill generator architecture and conditioning] The skill generator is conditioned only on task context, prior rollouts, and verified outcomes; the manuscript does not analyze or experiment on whether this featurization avoids trajectory memorization or policy collapse over generations.

    Authors: We agree that empirical analysis of potential memorization or collapse is important for validating the conditioning design. In the revision, we will add experiments and discussion tracking metrics such as skill diversity across generations, performance stability, and comparisons that detect signs of trajectory memorization or policy collapse, providing evidence on the robustness of the featurization. revision: yes

Circularity Check

0 steps flagged

No significant circularity; bi-level objective is an explicit proposal grounded in standard RL

full rationale

The paper defines the recurrent skill optimization process and then directly introduces the bi-level GRPO objective as a combination of intra-generation (rollouts under shared skill) and inter-generation (revisions improving across generations) advantage terms. This is presented as a new objective to optimize the described process rather than a derivation that reduces to its own inputs by construction. No self-definitional loops, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided derivation chain. The framework relies on external RL principles (policy optimization, verifiable rewards) and empirical benchmarks for validation, with the central claim remaining independent of any tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on standard reinforcement learning assumptions and the availability of verifiable outcome-based rewards; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Verifiable rewards can be obtained from task outcomes to guide skill revisions
    The bi-level optimization depends on these rewards being reliable signals for both intra- and inter-generation comparisons.

pith-pipeline@v0.9.0 · 5631 in / 1054 out tokens · 86399 ms · 2026-05-12T03:57:16.438347+00:00 · methodology

discussion (0)

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Reference graph

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