Agent-RRM: A Multi-Faceted Reward Model for Agentic Reinforcement Learning
The paper arXiv:2601.22154 presents the Agent Reasoning Reward Model (Agent-RRM), a comprehensive reward system designed to offer structured feedback for agentic paths in reinforcement learning. Agent-RRM produces three distinct signals: a clear reasoning trace, a targeted critique that identifies reasoning errors, and a cumulative process score. The research explores three methods of integration: Reagent-C (text-enhanced refinement), Reagent-R (reward-enhanced guidance), and Reagent-U (integrated feedback). Testing across 12 varied benchmarks reveals that the model significantly enhances intermediate reasoning quality compared to traditional sparse outcome-based rewards. This study tackles the shortcomings of existing agentic RL approaches that do not adequately recognize intermediate reasoning steps, resulting in less effective training.
Key facts
- Agent-RRM produces explicit reasoning trace, focused critique, and overall score.
- Three integration strategies: Reagent-C, Reagent-R, Reagent-U.
- Evaluated on 12 diverse benchmarks.
- Addresses sparse outcome-based reward limitations in agentic RL.
- Published on arXiv with ID 2601.22154.
Entities
Institutions
- arXiv