SERL: Selective Environment-Reweighted Learning Boosts LLM Agent Performance

Researchers have introduced a novel reinforcement learning framework known as SERL (Selective Environment-Reweighted Learning), which enhances credit assignment for multi-turn LLM agents by utilizing feedback from the environment at each step. SERL determines the direction of updates based on task rewards, while environmental feedback fine-tunes both the placement and intensity, emphasizing essential actions. In benchmarks like ALFWorld and WebShop, SERL records success rates of 90.0% and 80.1%, respectively, surpassing robust RL and distillation baselines. The approach examines five sources of feedback and two levels of insertion granularity, tackling the issue of distributing sparse success-or-failure signals across numerous actions in lengthy tasks. The research paper can be found on arXiv with the identifier 2605.19447.