https://arxiv.org/abs/2602.08234 SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement LearningLarge Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past experiences. Existing memory-based methods primarily store raw trajectories, which are often redundant and noisearxiv.org에이전트가 과거의 긴 행동 궤적을 그대로 ..
