HISR: Hindsight Information Modulated Segmental Process Rewards For Multi-turn Agentic Reinforcement Learning

2026-03-19

AI Summary

This work addresses the unreliable credit assignment in multi-turn agent reinforcement learning caused by delayed propagation of sparse episodic rewards and coarse-grained step-level process rewards. To mitigate this issue, the authors propose a hindsight-informed segmented process reward mechanism that aligns rewards with subgoals. By computing dynamic action importance through the likelihood ratio between the policy model and a hindsight model, the method modulates rewards for critical trajectory segments accordingly. The approach integrates three key components: segment-level reward modeling, hindsight preference modeling, and importance-weighted reward aggregation, collectively enhancing the fidelity of credit assignment. Experimental results on three public benchmarks demonstrate that the proposed method significantly improves both sample efficiency and final task performance in multi-turn agent settings.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

While large language models excel in diverse domains, their performance on complex longhorizon agentic decision-making tasks remains limited. Most existing methods concentrate on designing effective reward models (RMs) to advance performance via multi-turn reinforcement learning. However, they suffer from delayed propagation in sparse outcome rewards and unreliable credit assignment with potentially overly fine-grained and unfocused turnlevel process rewards. In this paper, we propose (HISR) exploiting Hindsight Information to modulate Segmental process Rewards, which closely aligns rewards with sub-goals and underscores significant segments to enhance the reliability of credit assignment. Specifically, a segment-level process RM is presented to assign rewards for each sub-goal in the task, avoiding excessively granular allocation to turns. To emphasize significant segments in the trajectory, a hindsight model is devised to reflect the preference of performing a certain action after knowing the trajectory outcome. With this characteristic, we design the ratios of sequence likelihoods between hindsight and policy model to measure action importance. The ratios are subsequently employed to aggregate segment importance scores, which in turn modulate segmental process rewards, enhancing credit assignment reliability. Extensive experimental results on three publicly benchmarks demonstrate the validity of our method.

Problem

• credit assignment
• sparse rewards
• multi-turn reinforcement learning
• process rewards
• long-horizon decision-making

Innovation

• Hindsight Information
• Segmental Process Rewards
• Credit Assignment
• Multi-turn Reinforcement Learning
• Reward Modeling