bunny127/Agent-RRM
Agent-RRM by bunny127 is an 8 billion parameter Agent Reasoning Reward Model designed to provide structured feedback for agentic trajectories. It generates explicit reasoning traces, focused critiques highlighting flaws, and an overall process performance score. This model is specifically developed to improve agentic reinforcement learning by differentiating intermediate reasoning quality, leading to enhanced training results for complex reasoning and tool use tasks.
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Agent Reasoning Reward Model (Agent-RRM)
Agent-RRM is an 8 billion parameter model developed by bunny127, focusing on providing multi-faceted, structured feedback for agentic trajectories. Unlike traditional sparse outcome-based rewards, Agent-RRM aims to differentiate the quality of intermediate reasoning steps, which is crucial for effective agentic Reinforcement Learning (RL).
Key Capabilities
- Structured Feedback Generation: Produces three distinct types of feedback for agent actions:
- An explicit reasoning trace.
- A focused critique that identifies reasoning flaws and offers refinement guidance.
- An overall score evaluating the process performance.
- Enhanced Agentic RL: By leveraging these detailed signals, Agent-RRM facilitates more effective training for agents performing complex reasoning and tool use.
- Integration Strategies: The model supports various integration strategies, including text-augmented refinement (Reagent-C), reward-augmented guidance (Reagent-R), and unified feedback integration (Reagent-U).
Performance Highlights
Extensive evaluations across 12 diverse benchmarks demonstrate significant performance improvements. Notably, the Reagent-U strategy achieved 43.7% on GAIA and 46.2% on WebWalkerQA, validating the effectiveness of the reasoning reward model and its associated training schemes.
Good For
- Developers working on agentic reinforcement learning systems.
- Improving the reasoning capabilities and tool use of AI agents.
- Scenarios requiring detailed, process-oriented feedback beyond simple outcome-based rewards.