Each Reward to Its Relevant Tokens: Reward-Specific Token-Level Credit Assignment for Multi-Reward RL

The paper introduces MRTPO, a method for assigning reward advantages at the token level in multi-reward reinforcement learning for language models. MRTPO uses the Jensen‑Shannon distance between next‑token distributions with and without a reward’s verifier to identify which tokens are relevant to each reward, thereby reallocating advantage without auxiliary models or annotations. Experiments on text‑to‑SQL, multi‑constraint instruction following, and tool calling show that MRTPO converges faster and outperforms existing methods.

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• llms
• agents
• reinforcement-learning
• data-engineering