Answer Is Not Enough: Causal Mediation for Reasoning Credit Assignment in GRPO

Group Relative Policy Optimization (GRPO) optimizes reasoning policies using verifiable final answer rewards. However, final answer correctness alone may not provide an adequate learning signal for the reasoning process. Our preliminary investigation shows that, when the answer condition is fixed, changing the reasoning process leads to an accuracy difference of up to 11.8%, indicating that the reasoning process provides information beyond the final answer. Motivated by this observation, we propose CaMeL (Causal Mediation Lens), a reasoning credit assignment method based on causal mediation that models the reasoning process as a mediator to estimate its contribution to the answer decision. CaMeL estimates this contribution with two signals. Reasoning Causal Effect (RCE) measures the effect of the reasoning process on the correct answer probability relative to direct answering, capturing the direction and magnitude of its contribution. Reasoning Causal Specificity (RCS) measures whether the change in the answer distribution induced by the reasoning process exceeds those induced by shuffled and irrelevant reasoning processes, retaining only contributions beyond the control reference. The two signals form a mediator contribution, which is incorporated into rollout scoring by adjusting the Reason credit without altering the final answer reward. Experiments show that CaMeL outperforms Vanilla GRPO on logical reasoning benchmarks and generalizes to out-of-distribution (OOD) settings. Quantitative analyses and human evaluation further show that the mediator contribution aligns well with human judgments of the reasoning process, with over 90% directional agreement among activated signals.