arxiv: 2604.27488 · v1 · submitted 2026-04-30 · 💻 cs.CL

Recognition: unknown

Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO

Yu Tian , Jiawei Chen , Lifan Zheng , Mingxiang Tao , Xinyi Zeng , Zhaoxia Yin , Hang Su , Xian Sun

Authors on Pith no claims yet

Pith reviewed 2026-05-07 09:51 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM agentsskill optimizationself-evolving skillstraining-freeprompt optimizationbenchmark evaluationagent capabilities

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The pith

Skills-Coach optimizes skills in LLM agents through four automated modules without training, producing gains on a 48-skill benchmark.

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

The paper introduces Skills-Coach as a framework to enhance self-evolution of skills inside LLM-based agents. It targets fragmentation in the skill ecosystem by building comprehensive test coverage and competency. Four modules work together: one generates diverse tasks, one lightly optimizes prompts and code, one runs comparative executions, and one provides traceable evaluations. Experiments on the new Skill-X dataset with 48 skills show measurable improvements across categories, supporting more capable agents.

Core claim

Skills-Coach achieves significant performance improvements in skill capability across a wide range of categories by using its four core modules to systematically enhance skills in LLM-based agents without the need for additional training.

What carries the argument

The four-module framework of Diverse Task Generation, Lightweight Optimization, Comparative Execution, and Traceable Evaluation, which together drive training-free skill refinement and evaluation.

If this is right

LLM agents obtain wider skill coverage for complex intelligent applications.
Skill refinement proceeds without retraining or updating base model weights.
Execution can switch between virtual simulation and real environments as needed.
The approach supports ongoing self-evolution of agent competencies over time.

Where Pith is reading between the lines

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

The modular design could be adapted to optimize skills in non-LLM agent systems.
It might lower the manual effort required to engineer reliable agent behaviors.
Further tests on out-of-distribution real-world tasks would clarify transfer limits.

Load-bearing premise

The performance gains reflect genuine, generalizable skill improvements rather than optimizations tuned only to the benchmark's generated tasks.

What would settle it

Applying the optimized skills to a fresh collection of tasks created independently of the Diverse Task Generation Module and finding no consistent gains would indicate the improvements are benchmark-specific.

read the original abstract

We introduce Skills-Coach, a novel automated framework designed to significantly enhance the self-evolution of skills within Large Language Model (LLM)-based agents. Addressing the current fragmentation of the skill ecosystem, Skills-Coach explores the boundaries of skill capabilities, thereby facilitating the comprehensive competency coverage essential for intelligent applications. The framework comprises four core modules: a Diverse Task Generation Module that systematically creates a comprehensive test suite for various skills; a Lightweight Optimization Module dedicated to optimizing skill prompts and their corresponding code; a Comparative Execution Module facilitating the execution and evaluation of both original and optimized skills; and a Traceable Evaluation Module, which rigorously evaluates performance against specified criteria. Skills-Coach offers flexible execution options through its virtual and real modes. To validate its efficacy, we introduce Skill-X, a comprehensive benchmark dataset consisting of 48 diverse skills. Experimental results demonstrate that Skills-Coach achieves significant performance improvements in skill capability across a wide range of categories, highlighting its potential to advance the development of more robust and adaptable LLM-based agents.

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

Skills-Coach gives a practical four-module loop for training-free skill optimization in LLM agents, but the evaluation risks measuring gains only on tasks the system itself created.

read the letter

The main takeaway is that Skills-Coach automates skill improvement for LLM agents through a cycle of generating diverse tasks, lightly optimizing prompts and code, running original versus updated versions side by side, and scoring them with traceable criteria. They pair this with a new Skill-X benchmark of 48 skills and report performance lifts across categories, all without model training via their GRPO approach and with options for virtual or real execution.

Referee Report

2 major / 2 minor

Summary. The paper introduces Skills-Coach, a training-free framework for self-evolving skills in LLM-based agents. It consists of four modules: Diverse Task Generation (creating a test suite), Lightweight Optimization (tuning prompts/code), Comparative Execution (running original vs. optimized skills), and Traceable Evaluation (assessing against criteria). The work presents Skill-X, a benchmark of 48 skills, and claims that the framework yields significant performance improvements across categories in both virtual and real execution modes.

Significance. If the performance gains prove robust under proper held-out evaluation and external validation, Skills-Coach could provide a practical, training-free method for automated skill optimization, addressing fragmentation in LLM agent capabilities and offering a reusable benchmark in Skill-X. The modular design and dual execution modes are practical strengths that could aid reproducibility if implementation details are supplied.

major comments (2)

[Abstract] Abstract, second paragraph: the central claim of 'significant performance improvements in skill capability across a wide range of categories' rests on an evaluation pipeline in which the Diverse Task Generation Module creates tasks that are subsequently optimized and evaluated by the Lightweight Optimization, Comparative Execution, and Traceable Evaluation Modules. No mention is made of a disjoint held-out task set, external agent benchmarks, or real-world transfer metrics; if gains are measured on the identical generated tasks used for optimization, they may reflect prompt overfitting rather than genuine skill evolution. This is load-bearing for the paper's primary result.
[Title and Abstract] Title and Abstract: the title invokes 'Training-Free GRPO' as the optimization mechanism, yet the abstract and module descriptions supply no definition of GRPO, no equations or pseudocode for its training-free application, and no indication of how it differs from standard prompt tuning. Without this, the Lightweight Optimization Module cannot be assessed for correctness or novelty.

minor comments (2)

[Abstract] Abstract: quantitative results, baseline comparisons, error bars, and specific metrics (e.g., success rates before/after optimization) are entirely absent, which is a presentation issue that should be remedied even if the full paper contains them.
The manuscript should clarify the exact criteria and scoring rubric used in the Traceable Evaluation Module and whether Skill-X tasks are released with the paper for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects of evaluation rigor and clarity. We address each major comment point by point below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

Referee: [Abstract] Abstract, second paragraph: the central claim of 'significant performance improvements in skill capability across a wide range of categories' rests on an evaluation pipeline in which the Diverse Task Generation Module creates tasks that are subsequently optimized and evaluated by the Lightweight Optimization, Comparative Execution, and Traceable Evaluation Modules. No mention is made of a disjoint held-out task set, external agent benchmarks, or real-world transfer metrics; if gains are measured on the identical generated tasks used for optimization, they may reflect prompt overfitting rather than genuine skill evolution. This is load-bearing for the paper's primary result.

Authors: We acknowledge the importance of distinguishing optimization from evaluation to substantiate genuine skill evolution. The framework generates diverse tasks via the first module to form the Skill-X benchmark, with optimization focused on prompt/code refinement through comparative execution against traceable criteria rather than task-specific memorization. That said, the current manuscript does not explicitly detail a held-out split or external benchmarks. We will revise the abstract and experimental section to clarify the task generation process, add a description of any internal task partitioning used, and include discussion of robustness across virtual/real modes. If feasible within the revision timeline, we will also report results on a small held-out subset to directly address overfitting concerns. revision: yes
Referee: [Title and Abstract] Title and Abstract: the title invokes 'Training-Free GRPO' as the optimization mechanism, yet the abstract and module descriptions supply no definition of GRPO, no equations or pseudocode for its training-free application, and no indication of how it differs from standard prompt tuning. Without this, the Lightweight Optimization Module cannot be assessed for correctness or novelty.

Authors: We agree that the abstract lacks sufficient detail on GRPO, limiting immediate assessment of the Lightweight Optimization Module. GRPO is the training-free optimization procedure employed in that module, relying on iterative refinement via execution feedback rather than gradient-based updates. We will revise the abstract to include a concise definition of GRPO, note its training-free character, and briefly contrast it with standard prompt tuning (e.g., via the use of comparative execution and traceable evaluation). We will also ensure the main text supplies the requested equations or pseudocode for the GRPO procedure to support reproducibility and novelty evaluation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical framework only

full rationale

The paper introduces an engineering framework (Skills-Coach) with four descriptive modules and reports empirical gains on a newly introduced benchmark (Skill-X). No equations, parameter-fitting procedures, mathematical derivations, or self-referential predictions appear in the abstract or module descriptions. Performance claims are before/after comparisons on generated tasks rather than any derivation that reduces to its own inputs by construction. Absent load-bearing self-citations, ansatzes, or uniqueness theorems, the work is self-contained as an applied system description.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the framework is presented at a high level only.

pith-pipeline@v0.9.0 · 5493 in / 1014 out tokens · 43253 ms · 2026-05-07T09:51:23.349414+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

74 extracted references · 11 canonical work pages · 10 internal anchors

[1]

React: Synergizing reasoning and acting in language models

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. InThe eleventh international conference on learning representations, 2022

2022
[2]

Toolformer: Language models can teach themselves to use tools.Advances in neural information processing systems, 36:68539–68551, 2023

Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools.Advances in neural information processing systems, 36:68539–68551, 2023

2023
[3]

Voyager: An Open-Ended Embodied Agent with Large Language Models

Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anand- kumar. Voyager: An open-ended embodied agent with large language models.arXiv preprint arXiv:2305.16291, 2023

work page internal anchor Pith review arXiv 2023
[4]

Metagpt: Meta programming for a multi-agent collaborative framework

Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, et al. Metagpt: Meta programming for a multi-agent collaborative framework. InThe twelfth international conference on learning representations, 2023

2023
[5]

Autogen: Enabling next-gen llm applications via multi-agent conversations

Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al. Autogen: Enabling next-gen llm applications via multi-agent conversations. InFirst conference on language modeling, 2024

2024
[6]

Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.Advances in Neural Information Processing Systems, 36: 38154–38180, 2023

Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.Advances in Neural Information Processing Systems, 36: 38154–38180, 2023

2023
[7]

Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024

Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh J Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024

2024
[8]

ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023.URL https://arxiv. org/abs/2307.16789, 2, 2024

work page internal anchor Pith review arXiv 2023
[9]

Gorilla: Large language model connected with massive apis.Advances in Neural Information Processing Systems, 37:126544–126565, 2024

Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. Gorilla: Large language model connected with massive apis.Advances in Neural Information Processing Systems, 37:126544–126565, 2024

2024
[10]

Api-bank: A comprehensive benchmark for tool-augmented llms

Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. Api-bank: A comprehensive benchmark for tool-augmented llms. InProceedings of the 2023 conference on empirical methods in natural language processing, pages 3102–3116, 2023. 10

2023
[11]

Self-instruct: Aligning language models with self-generated instructions

Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self-generated instructions. InProceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers), pages 13484–13508, 2023

2023
[12]

WizardLM: Empowering large pre-trained language models to follow complex instructions

Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. Wizardlm: Empowering large language models to follow complex instructions.arXiv preprint arXiv:2304.12244, 2023

work page internal anchor Pith review arXiv 2023
[13]

Self-Rewarding Language Models

Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. Self-rewarding language models.arXiv preprint arXiv:2401.10020, 2024

work page internal anchor Pith review arXiv 2024
[14]

Proximal Policy Optimization Algorithms

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms.arXiv preprint arXiv:1707.06347, 2017

work page internal anchor Pith review arXiv 2017
[15]

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024

work page internal anchor Pith review arXiv 2024
[16]

Training-free group relative policy optimization.arXiv preprint arXiv:2510.08191,

Yuzheng Cai, Siqi Cai, Yuchen Shi, Zihan Xu, Lichao Chen, Yulei Qin, Xiaoyu Tan, Gang Li, Zongyi Li, Haojia Lin, et al. Training-free group relative policy optimization.arXiv preprint arXiv:2510.08191, 2025

work page arXiv 2025
[17]

gradient descent

Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, and Michael Zeng. Automatic prompt optimization with “gradient descent” and beam search. InProceedings of the 2023 conference on empirical methods in natural language processing, pages 7957–7968, 2023

2023
[18]

TextGrad: Automatic "Differentiation" via Text

Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. Textgrad: Automatic" differentiation" via text.arXiv preprint arXiv:2406.07496, 2024

work page internal anchor Pith review arXiv 2024
[19]

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. Dspy: Compiling declarative language model calls into self-improving pipelines.arXiv preprint arXiv:2310.03714, 2023

work page internal anchor Pith review arXiv 2023
[20]

Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023

Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023

2023
[21]

Self-refine: Iterative refinement with self-feedback.Advances in neural information processing systems, 36:46534–46594, 2023

Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback.Advances in neural information processing systems, 36:46534–46594, 2023

2023
[22]

Teaching Large Language Models to Self-Debug

Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug. arXiv preprint arXiv:2304.05128, 2023

work page internal anchor Pith review arXiv 2023
[23]

Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information processing systems, 36:46595–46623, 2023

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information processing systems, 36:46595–46623, 2023

2023
[24]

AgentBench: Evaluating LLMs as Agents

Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al. Agentbench: Evaluating llms as agents.arXiv preprint arXiv:2308.03688, 2023. 11 Appendix A Evaluation Dimensions with 51 Discrete Evaluation Metrics As shown in Table 3, the generated samples encompass eight evaluation dimensions, compr...

work page internal anchor Pith review arXiv 2023
[25]

Structural Complete- ness & Organization (7 points)
[26]

Clear introduction/overview at document start explaining purpose and goals
[27]

Installation/setup instructions with complete environment configuration
[28]

Comprehensive usage section detailing all commands and functions
[29]

Multiple concrete examples with at least 3 different real-world scenarios
[30]

Configuration/parameters section listing all configurable options
[31]

Troubleshooting/error handling with dedicated section for FAQs
[32]

Logical progression from basic to advanced concepts
[33]

Beginner step-by-step guide with clear guidance keywords (first, then, next)

Practical Usability & Learnability (6 points) 1. Beginner step-by-step guide with clear guidance keywords (first, then, next)
[34]

Copy-paste ready examples with actual commands ($, python, bash, etc.)
[35]

Explicit prerequisites clearly listing dependencies and required knowledge
[36]

Common pitfalls documentation with warning/note/important markers
[37]

Progressive complexity from simple to advanced examples
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Quick start guide or minimal working example section
[39]

At least 3 different real examples with complete executable code blocks

Example Quality & Coverage (6 points) 1. At least 3 different real examples with complete executable code blocks
[40]

Diverse use cases covering different scenarios, not just task variations
[41]

Expected output demonstration using output:/result:/=>/-> markers
[42]

Boundary condition examples showing edge cases and extreme scenarios
[43]

Error handling scenarios demonstrating exception and failure handling
[44]

Complex multi-step workflow showing complete real-world application
[45]

All parameters/options documented with parameter/option/flag keywords

Technical Depth & Ac- curacy (6 points) 1. All parameters/options documented with parameter/option/flag keywords
[46]

Return values and output format specification (types, JSON structure)
[47]

Performance characteristics mentioned when relevant
[48]

Clear limitations and constraints explicitly listed
[49]

Integration with other systems explained and demonstrated
[50]

Correct use of 2+ professional technical terms (API, CLI, SDK, etc.)
[51]

Clear concise language with average sentence length < 30 words

Clarity & Readability (6 points) 1. Clear concise language with average sentence length < 30 words
[52]

Consistent formatting and style with unified header levels
[53]

Proper use of at least 3 headers, lists (- or *), and code blocks
[54]

Unambiguous statements avoiding vague or misleading expressions
[55]

Appropriate detail level (500-15000 characters, not too brief or verbose)
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Good visual hierarchy using secondary headers (\##) or tertiary headers (\###)
[57]

Every command in examples explained in documentation

Command Coverage Completeness (6 points) 1. Every command in examples explained in documentation
[58]

All flags/options for each command documented
[59]

Command syntax clearly demonstrated with correct format
[60]

Usage context explained for when to use each command
[61]

Relationships between multiple commands clarified
[62]

No undocumented or hidden functionality
[63]

Common errors and solutions listed with fixes

Error Handling & Trou- bleshooting (6 points) 1. Common errors and solutions listed with fixes
[64]

Error message explanations clarifying meaning and context
[65]

Debugging tips provided with diagnostic methods and commands
[66]

Known issues and workarounds documented
[67]

Support and bug reporting instructions provided
[68]

Verification steps to check configuration correctness
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Advanced use cases and patterns with advanced/complex/production examples

Advanced Scenarios & Best Practices (6 points) 1. Advanced use cases and patterns with advanced/complex/production examples
[70]

Best practices and recommendations using best practice/recommended/tip keywords
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Performance optimization tips when applicable
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Security considerations mentioned and explained when relevant
[73]

Integration patterns showing how to combine with other tools
[74]

Real-world workflow examples demonstrating complete practical scenarios Table 38 Evaluation Dimensions with 51 Specific Assessment Criteria 12 B Skill Sources in Skill-X skill name source rank in source self-improving-agent clawhub.ai 1 ontology clawhub.ai 3 self-improving-proactive-agent clawhub.ai 4 weather clawhub.ai 8 multi-search-engine clawhub.ai 9 ...