Direction-Aware Token Credit Assignment with Reward-Model Gradients
Yikuan HuJifeng ZhuMinle SuJiaqin YueGujie ShaoYanzhe ChenHaoxuan JiaHaoran XuWangzhou Dai
Oct 2026
Machine Learning
Abstract
GRPO shares a response-level advantage across tokens, leaving unresolved how strongly each token should contribute to learning. Updates induced by the shared advantage reinforce or suppress the sampled token at each position, but these updates do not always point toward increasing reward at the token level. This motivates a work-based view of credit: just as work depends on the component of force along a displacement, token credit should reflect the local reward change along the policy update direction. We introduce Direction-Aware Token Credit Assignment (DATC), which maps frozen reward-model gradients into policy logit space and allocates weights by evaluating the alignment between the reward direction and GRPO's signed sampled-action update direction, while preserving the sign and mean of the response-level advantage. DATC outperforms GRPO in all four settings across two model families.
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