Reasoning Matters: Mitigate Hallucination in Multimodal Large Reasoning Models via Reasoning-Conditioned Preference Optimization
arXiv:2605.27906v1 Announce Type: new Abstract: Multimodal Large Reasoning Models introduce the reasoning paradigm, demonstrating strong capabilities on complex vision-language tasks. However, they still suffer from severe hallucinations. Existing training-based methods typically mitigate hallucinations through response-level direct preference optimization (DPO), where the Chain-of-Thought (CoT) and the final answer are treated as a monolithic output and optimized jointly. We reveal that this formulation performs similarly to answer-only optimization, suggesting that it primarily learns answer
The rapid advancement and deployment of multimodal large reasoning models necessitate immediate solutions for critical issues like hallucination to foster trust and adoption.
Mitigating hallucination is crucial for the reliability and trustworthiness of advanced AI systems, particularly those aimed at complex reasoning tasks, which will impact enterprise and consumer applications.
Approaches to training advanced AI models are evolving to specifically target the reasoning process rather than just the final output, leading to more robust and accurate AI.
- · AI developers
- · Enterprises adopting AI
- · AI researchers
- · Companies relying on unreliable AI
- · Users of hallucinating AI systems
More reliable multimodal AI models become available for complex tasks.
Increased adoption of AI in sensitive fields due to improved trustworthiness.
The development of AI agents accelerates as foundation models become more reliable in their reasoning capabilities.
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Read at arXiv cs.AI