StructReward: Efficient Structured Process Rewards for Self-Correcting Multimodal Reasoning
Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective approach for improving multimodal reasoning. However, most existing methods evaluate an entire response using a binary reward based only on final-answer correctness, thereby discarding the supervision available in intermediate reasoning steps. Process reward models offer finer-grained feedback, but they typically rely on separately trained verifiers, costly chain-of-thought annotations, or online judging by large language models (LLMs). In this work, we introduce StructReward, a compute-efficient framework that provides dense reinforcement signals through structured step-level reward alignment. StructReward ...
| Authors | Yifan Li, Ruxin Sun, Tongzhou Zhao |
|---|---|
| Primary category | cs.AI |
| Published | 8 August 2026 |
| Last updated at source | 11 August 2026 |
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