PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911493827592192 |
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| author | Li, Yantao Hui, Qiang Yan, Chenyang Cheng, Kanzhi Zhao, Fang Tan, Chao Gao, Huanling Zhang, Jianbing Wang, Kai Dai, Xinyu Lian, Shiguo |
| author_facet | Li, Yantao Hui, Qiang Yan, Chenyang Cheng, Kanzhi Zhao, Fang Tan, Chao Gao, Huanling Zhang, Jianbing Wang, Kai Dai, Xinyu Lian, Shiguo |
| contents | Reinforcement learning has recently improved the reasoning ability of Large Language Models and Multimodal LLMs, yet prevailing reward designs emphasise final-answer correctness and consequently tolerate process hallucinations--cases where models reach the right answer while misperceiving visual evidence. We address this process-level misalignment with PaLMR, a framework that aligns not only outcomes but also the reasoning process itself. PaLMR comprises two complementary components: a perception-aligned data layer that constructs process-aware reasoning data with structured pseudo-ground-truths and verifiable visual facts, and a process-aligned optimisation layer that constructs a hierarchical reward fusion scheme with a process-aware scoring function to encourage visually faithful chains-of-thought and improve training stability. Experiments on Qwen2.5-VL-7B show that our approach substantially reduces reasoning hallucinations and improves visual reasoning fidelity, achieving state-of-the-art results on HallusionBench while maintaining strong performance on MMMU, MathVista, and MathVerse. These findings indicate that PaLMR offers a principled and practical route to process-aligned multimodal reasoning, advancing the reliability and interpretability of MLLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06652 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment Li, Yantao Hui, Qiang Yan, Chenyang Cheng, Kanzhi Zhao, Fang Tan, Chao Gao, Huanling Zhang, Jianbing Wang, Kai Dai, Xinyu Lian, Shiguo Computer Vision and Pattern Recognition Artificial Intelligence Reinforcement learning has recently improved the reasoning ability of Large Language Models and Multimodal LLMs, yet prevailing reward designs emphasise final-answer correctness and consequently tolerate process hallucinations--cases where models reach the right answer while misperceiving visual evidence. We address this process-level misalignment with PaLMR, a framework that aligns not only outcomes but also the reasoning process itself. PaLMR comprises two complementary components: a perception-aligned data layer that constructs process-aware reasoning data with structured pseudo-ground-truths and verifiable visual facts, and a process-aligned optimisation layer that constructs a hierarchical reward fusion scheme with a process-aware scoring function to encourage visually faithful chains-of-thought and improve training stability. Experiments on Qwen2.5-VL-7B show that our approach substantially reduces reasoning hallucinations and improves visual reasoning fidelity, achieving state-of-the-art results on HallusionBench while maintaining strong performance on MMMU, MathVista, and MathVerse. These findings indicate that PaLMR offers a principled and practical route to process-aligned multimodal reasoning, advancing the reliability and interpretability of MLLMs. |
| title | PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.06652 |