Enhancing the Outcome Reward-based RL Training of MLLMs with Self-Consistency Sampling

Jiahao Wang; Weiye Xu; Aijun Yang; Wengang Zhou; Lewei Lu; Houqiang Li; Xiaohua Wang; Jinguo Zhu

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arXiv · 2511.10648

Enhancing the Outcome Reward-based RL Training of MLLMs with Self-Consistency Sampling

Jiahao Wang·Weiye Xu·Aijun Yang·Wengang Zhou·Lewei Lu·Houqiang Li·Xiaohua Wang·Jinguo Zhu

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Abstract

Outcome-reward reinforcement learning (RL) is a common and increasingly significant way to refine the step-by-step reasoning of multimodal large language models (MLLMs). In the multiple-choice setting - a dominant format for multimodal reasoning benchmarks - the paradigm faces a significant yet often overlooked obstacle: unfaithful trajectories that guess the correct option after a faulty chain of thought receive the same reward as genuine reasoning, which is a flaw that cannot be ignored. We propose Self-Consistency Sampling (SCS) to correct this issue. For each question, SCS (i) introduces small visual perturbations and (ii) performs repeated truncation and resampling of an initial trajectory; agreement among the resulting trajectories yields a differentiable consistency score that down-weights unreliable traces during policy updates. Based on Qwen2.5-VL-7B-Instruct, plugging SCS into RLOO, GRPO, and REINFORCE++ series improves accuracy by up to 7.7 percentage points on six multimodal benchmarks with negligible extra computation. SCS also yields notable gains on both Qwen2.5-VL-3B-Instruct and InternVL3-8B, offering a simple, general remedy for outcome-reward RL in MLLMs.

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Jiahao Wang, Weiye Xu, Aijun Yang, Wengang Zhou, Lewei Lu, Houqiang Li, Xiaohua Wang, Jinguo Zhu. 2025-11-13. Enhancing the Outcome Reward-based RL Training of MLLMs with Self-Consistency Sampling. https://arxiv.org/abs/2511.10648

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