Work

VIGOR: Outcome-Contrastive Visual Grounding for Multimodal Policy Optimization

Year: 2026

Type: conference-paper

Abstract: While reinforcement learning with verifiable rewards (RLVR) has advanced multimodal reasoning, coarse outcome rewards offer limited guidance for assigning credit to visual evidence.Analyzing same-prompt rollout groups, we observe that different outcomes exhibit distinct temporal patterns of measured visual dependency across reasoning segments.We therefore propose VIGOR (Visual Grounding Outcome-contrastive Reinforcement), a policy optimization method that converts these group-wise contrasts into fine-grained supervision.VIGOR constructs outcome-conditioned prototypes from temporal visual fingerprints to reweight trajectories, uses segment-level contrasts to prioritize token gradients, and allocates the visual budget according to group difficulty.It requires no additional annotations or reward models and introduces no inference-time overhead.Across eight multimodal reasoning benchmarks, VIGOR achieves 57.5% average accuracy with Qwen2.5-VL-7B,improving over VPPO by 1.6 points.Controlled experiments further validate VIGOR on Qwen2.5-VL-3B and InternVL2.5-4B;on the latter, it outperforms VPPO on all eight tasks by 0.96 points on average.Our code is publicly available at https://github.com/ beck83879-bit/VIGOR. 25%

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/2026.emnlp-main.1935