Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems
- Published
- Source
- arXiv
- Paper number
- 044
- Field
- RL / Agents
- arXiv ID
- 2502.19328
Key points
- Existing reward models rely too heavily on human preferences, which can introduce bias and accuracy problems.
- Current approaches lack systematic verification of factual accuracy and instruction following.
- Traditional reward models often fail to capture multiple important evaluation criteria at the same time.
- The authors developed a multi-agent reward system that combines specialized verification agents with human preference signals.
- They built a router component that intelligently decides which verification agents to call.
- They also implemented a unified mechanism that combines scores from multiple evaluation sources.
Paper links
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