IVR-R1: Refining Trajectories through Iterative Visual-Grounded Reasoning in Reinforcement Learning
arXiv:2605.23997v1 Announce Type: cross Abstract: Multimodal large language models via reinforcement learning (RL) have demonstrated remarkable capabilities in complex visual reasoning tasks, yet they remain limited in long-horizon multimodal scenarios, often suffering from visual hallucination and logical error. Current methods typically pre-encode high-dimensional visual scenes into discrete textual proxies to facilitate downstream reasoning. As the reasoning chain unfolds, however, the inherent information asymmetry between text and visual scenes tends to erode visual grounding, resulting i
The proliferation of multimodal large language models (MLLMs) has highlighted their limitations in complex, long-horizon visual reasoning, necessitating new research into robust visual-grounding techniques.
Improving MLLMs' ability to accurately interpret and integrate visual information is critical for their reliability and capability in real-world autonomous applications.
This research outlines a method to reduce visual hallucination and logical errors in MLLMs by refining visual grounding through iterative reasoning, moving towards more trustworthy multimodal AI.
- · AI agents developers
- · Robotics industry
- · Generative AI platforms
- · Computer vision researchers
- · Developers relying on ungrounded MLLMs
- · Applications demanding high visual fidelity without integrated reasoning
- · Models prone to visual hallucinations
Refined visual-grounded reasoning directly leads to more reliable and capable multimodal AI systems.
Enhanced MLLM capabilities could accelerate the development and deployment of autonomous AI agents in various sectors.
Increased robustness in visual reasoning may reduce the cost and complexity of deploying AI in safety-critical applications, expanding its societal footprint.
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Read at arXiv cs.LG