mlsanityData to 7 Oct 2026

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

arXiv 2604.27488 ↗First posted 30 Apr 2026cs.CLcited by 8

On the map: Synthetic data and self-improvement for LLMs → Self-evolving LLM agents

Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Self-evolving LLM agents, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 23 of its 24 references that are in the map. First posted on arXiv 30 Apr 2026.

Builds on

23 of 24 references.

References · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
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  7. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  8. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  9. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  10. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  11. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  12. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
11 more
  1. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  3. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  4. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
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  6. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
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  10. 2024TextGrad: Automatic "Differentiation" via Textcited by 250
  11. 2025Training-Free Group Relative Policy Optimizationcited by 53

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 8.

8 papers.

  1. 2026Agent Skill Evaluation and Evolution: Frameworks and Benchmarkscited by 6
  2. 2026SkillCAT: Contrastive, Assessment-Augmented and Topology-AwareSkill Self-Evolution for LLM Agentscited by 5
  3. 2026Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Roundscited by 3
  4. 2026Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Miningcited by 2
  5. 2026On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecificationcited by 2
  6. 2026Self-Supervised Skill Optimizationcited by 0
  7. 2026VCE-Skill: Enhancing Skill Self-Evolution with Version-Change Experiencecited by 0
  8. 2026Trajectory-Guided Fault Localization for Agent Skill Evolutioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories
  2. 2026MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
  3. 2026SkillEvoReg: Regularizing Agent Skill Evolution Against Overfitting
  4. 2026SkillX: Automatically Constructing Skill Knowledge Bases for Agents
  5. 2026SkillAdam: Stable and Efficient Skill Evolution for Agents
  6. 2026ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
  7. 2026SkillMaster: Toward Autonomous Skill Mastery in LLM Agents
  8. 2026SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion