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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.