BAELAB, Pusan National University, Busan, Korea
Recent advances in sparse reward policy gradient methods have enabled effective reinforcement learning (LR)
fine-tuning for post-training language models. However, for reasoning tasks such as mathematical problem solving,
binarized outcome rewards provide limited feedback on intermediate reasoning steps. While some studies have attempted
to address this issue by estimating overall reasoning quality, it remains unclear whether these rewards are
reliable proxies for the quality of stepwise reasoning. In this study, we consider reasoning as a structured process and
propose TACReward reward model. The model can be seamlessly integrated into sparse reward frameworks without
additional human annotation costs or architectural modifications. TACReward aggregates stepwise structural deviations
between teachers and policy reasoning using process mining techniques, producing a scalar output reward range of $[0, 1]$.
Experiments on multiple mathematical reasoning benchmarks demonstrate that integrating the TACReward into sparse reward
frameworks encourages the policy model to improve the structural quality of reasoning. Consequently, this leads to
consistent performance improvements over existing sparse reward frameworks.
