Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence
Reinforcement learning with verifiable rewards (RLVR) improves multimodal reasoning, but group-relative policy optimization broadcasts a single sequence-level advantage to every token of a response, although errors usually localize to a few decisive steps. Recent self-distilled variants refine credit with a teacher--student likelihood gap. However, this signal cannot tell whether it relies on the image or on a language shortcut, and is applied uniformly even when the teacher is uninformative. To address this, we propose Pairwise token contrast and Adaptive trajectory ConfidencE (PACE). Pairwise token contrast scores each response under four teacher views, crossing a correct or incorrect a...
| Authors | Yiran Zhang, Hanbo Huang, Jielan Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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