AnchorCredit: Output-Anchored Credit Assignment for Structured Generation with Multiple Verifiable Rewards

In Reinforcement Learning with Verifiable Rewards (RLVR), Group Relative Policy Optimization (GRPO) and related methods typically compute group-relative advantages from response rewards and broadcast the same advantage to every generated token in a response to guide policy updates. However, we identify a systematic problem of cross-component credit misassignment in tasks with multiple verifiable results, such as joint multi-attribute prediction: low rewards for some components can penalize a well-performing component and its associated reasoning. To address this problem, we propose AnchorCredit. The method uses the policy model's attention from output anchors to preceding reasoning sentences to estimate sentence–component associations and assign component-specific advantages to the relevant reasoning, without additional relevance annotations or a separate relevance discriminator. To support the study of credit assignment with multiple verifiable rewards, we construct ProductAttr, a benchmark of 7,200 real product image–text samples. Experiments show that AnchorCredit outperforms existing baselines on this benchmark. Experiments on the public Nutrition5k dataset further support the method's effectiveness in this setting. The complete code and dataset will be released upon acceptance.