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arXiv AI By Yuhang Han, Dingning Liu, Yujie Chen, Yaojie Zhang, Jingwei Song, Zequn Yang, Xuanming Shang, Li Ding, Zhijiang Guo, Linfeng Zhang

From Visual Affinity to Learning Credit in Multimodal Reinforcement Learning

Read the original on arXiv AI →

The paper introduces Outcome-Conditioned Visual Credit (OVC), a plug‑in framework for multimodal reinforcement learning that separates visual association measurement from token‑level credit assignment. OVC uses Relative Visual Encoding to create a context‑relative visual coordinate and Outcome‑Conditioned Evidence Calibration to adjust token contributions based on outcome‑discriminative evidence. When applied to GSPO-token, OVC improves mean accuracy by 1.86–2.39 percentage points across six benchmarks without requiring additional reward models or inference‑time modules.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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