PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment

• URL: http://arxiv.org/abs/2603.06652v1
• Date: Sat, 28 Feb 2026 04:33:11 GMT
• Title: PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment
• Abstract summary: PaLMR is a framework that aligns not only outcomes but also the reasoning process itself.<n> Experiments on Qwen2.5-VL-7B show that our approach substantially reduces reasoning hallucinations and improves visual reasoning fidelity.<n>These findings indicate that PaLMR offers a principled and practical route to process-aligned multimodal reasoning.
• Score: 29.229662966154507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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