Be Faithful When Response: Returning Fluent and Grounded Answers for Vision-Language Models Reinforcement Learning
arXiv:2606.29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs). However, directly applying RL to rollout multimodal reasoning can lead to instability, due to the exploitation of language priors, the neglect of visual evidence, and the generation of reasoning traces that are fluent yet not visually grounded. The question arises: Can initially steer the policy toward visually faithful reasoning regime before applying reinforcement learning? To this end, we propose a Faithful Warm-Start
Ongoing research into improving the reliability and grounding of Vision-Language Models (VLMs) is critical for their real-world deployment, and this paper presents a novel approach to addressing fundamental stability challenges. The abstract addresses a common problem observed with VLMs today, where model output is not aligned to the visual input.
This development is crucial for advancing the practical application of Vision-Language Models, ensuring their outputs are not just fluent but also factually based on visual evidence, mitigating issues like 'AI hallucinations'. A more reliable VLM can be a key building block for more complex AI systems and agents.
By proposing methods to ensure visual faithfulness before reinforcement learning, this research aims to make VLM outputs more trustworthy and less prone to generating plausible but incorrect information. This can accelerate deployment of VLMs in scenarios requiring high-fidelity interaction with visual data.
- · AI researchers
- · Companies deploying VLMs
- · Sectors using vision-based AI for critical tasks
- · VLM users
- · Developers of unstable VLM applications
- · Trust-poor VLM applications
Improved VLM reliability accelerates deployment in new applications requiring visual grounding.
Greater trust in VLMs could lead to their integration into a wider array of autonomous systems and decision-making processes.
More robust, grounded VLMs could facilitate the development of true multimodal AI agents that interact with and understand the physical world more effectively.
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Read at arXiv cs.AI