AI RESEARCH
PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment
arXiv CS.CV
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ArXi:2603.06652v1 Announce Type: new 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.