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 state-level rewards in agentic reasoning. By constructing state graphs that capture the intrinsic topological structure of trajectories, RewardFlow performs topology-aware propagation to estimate each state'
The rapid advancement of LLMs has made agentic RL a critical frontier, prompting demand for more efficient and scalable reward mechanisms to overcome current limitations.
Efficient reward propagation directly addresses a core bottleneck in scaling LLM agents, potentially accelerating their development and deployment into complex real-world tasks.
The proposed 'RewardFlow' method offers a lighter, more scalable approach to state-level reward estimation compared to prior costly methods, enhancing the feasibility of complex agentic systems.
- · AI research labs
- · Developers of LLM agents
- · Companies adopting agentic AI
- · Machine learning infrastructure providers
- · Companies reliant on expensive, high-computational reward modeling
- · Inefficient RL training methodologies
Improved efficiency and performance of AI agents in abstract reasoning tasks.
Faster development cycles and deployment of autonomous AI systems across various industries.
Enhanced automation of complex white-collar workflows, leading to broader economic shifts.
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Read at arXiv cs.LG