alfworld

Skill-DisCo is a framework that distills reusable procedural skills from successful agent traces and compiles them into callable, executable programs. Experiments on ALFWorld and WebArena show improved success rates and reduced agent turns.

Introduces SkillDAG, a self-evolving typed directed graph for LLM skill selection at scale that models inter-skill relationships and allows agents to query and evolve the graph during execution, outperforming baselines on ALFWorld and SkillsBench.

This paper presents the first systematic study of credit assignment in multi-turn LLM agents, introducing SERL, a selective environment-reweighted learning framework. SERL uses environment feedback to sharpen the RL objective on causally relevant actions, achieving 90.0% and 80.1% success rates on ALFWorld and WebShop respectively.