SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

arXiv:2602.08234 · cs.LG · Submitted 2026-02-09 · Read on arXiv

cs.LG

Submitted: 2026-02-09

Updated: 2026-10-07

Code: https://github.com/aiming-lab/SkillRL

Terminology

• GRPO — 18× in this paper · explained in 6 episodes
• Reinforcement Learning — 18× in this paper · explained in 4 episodes
• Distillation — 17× in this paper · explained in 2 episodes
• Core — 16× in this paper · explained in 2 episodes
• Abstraction — 10× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Large Language Model — 8× in this paper
• Benchmark — 6× in this paper · explained in 2 episodes
• Failure Modes — 6× in this paper · explained in 3 episodes
• Hyperparameters — 6× in this paper · explained in 2 episodes
• Papers — 6× in this paper · explained in 3 episodes
• Ablation Study — 4× in this paper · explained in 3 episodes
• Self-Evolving Agents — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• In-Context Learning — 3× in this paper · explained in 5 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 3× in this paper · explained in 3 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Verification — 3× in this paper · explained in 2 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Agent Memory — 2× in this paper · explained in 1 episode
• Embodied AI — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Institutional Review Board — 2× in this paper
• RAG — 2× in this paper · explained in 3 episodes
• Supervised Fine-Tuning — 2× in this paper
• Supervised Fine-Tuning (SFT) — 2× in this paper · explained in 8 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Agent Skills — 1× in this paper · explained in 1 episode
• Agentic Problem — 1× in this paper · explained in 1 episode
• Agentic Reasoning — 1× in this paper · explained in 1 episode
• Agentic Search — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Generator — 1× in this paper · explained in 2 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Qwen Technical Report
• Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Memp: Exploring Agent Procedural Memory
• Group-in-Group Policy Optimization for LLM Agent Training
• A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Memory in the Age of AI Agents
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
• Search-o1: Agentic Search-Enhanced Large Reasoning Models
• Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
• SimpleMem: Efficient Lifelong Memory for LLM Agents
• ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• ZeroSearch: Incentivize the Search Capability of LLMs without Searching
• Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
• Kimi K2.5: Visual Agentic Intelligence
• Tongyi DeepResearch Technical Report

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