Seeing Through the Chain: Understanding and Mitigating Hallucinations in Multimodal Large Reasoning Models

While multimodal large reasoning models (MLRMs) have exhibited impressive capabilities, they remain prone to hallucinations, and effective solutions remain underexplored. In this paper, we investigate the underlying causes of hallucinations in MLRMs and accordingly propose a reasoning-centric training framework for mitigation. Specifically, we find that introducing reasoning mechanisms exacerbates models' reliance on language priors and overlooks visual inputs, leading to CoTs with reduced visual cues but redundant text tokens. Guided by the Information Bottleneck (IB) principle, we propose selectively filtering redundant thinking tokens to obtain a more compact, signal-efficient CoT that preserves task-relevant information while suppressing noise. We also observe that the quality of the reasoning trace largely determines whether hallucination emerges in final answers. To leverage this, we introduce a reasoning-enhanced preference optimization scheme that constructs training pairs using high-quality AI feedback. We further propose generating high-quality negative samples for contrastive optimization via a multimodal hallucination-inducing mechanism that exposes model hallucination behaviors through carefully crafted visual and textual inducers. By modeling CoT chains as intermediate representations between inputs and final answers, we establish an IB-based theoretical justification for our method. Extensive experiments reveal consistent hallucination reduction across diverse MLRMs and benchmarks.