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AHEAD: Adaptive Hindsight with Environment-Augmented Distillation for Agentic RL

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Training multi-turn LLM agents with reinforcement learning typically relies on trajectory-level rewards, which assign a uniform advantage to every step and cannot identify which decisions led to success or failure. Self-distillation methods can provide finer-grained supervision by augmenting RL with privileged information. However, existing approaches usually apply the same type of privileged information to every step in an indistinguishable manner, ignoring a key asymmetry: routine steps need little additional guidance, while critical error steps require corrective direction that environment

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