RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models 事件

RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models arXiv:2603.18859v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) shows promise for enhancing LLM agentic reasoning, yet sparse terminal rewards hinder fine-grained optimization. Process reward modeling offers an alternative but incurs high computational costs, reward hacking risks, and annotation bottlenecks. We introduce RewardFlow, a lightweight method for estimating st