arXiv:2606.17678v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference. The prevailing training paradigm relies on large-scale caption-based pretraining for general alignment, followed by supervised fine-tuning and reinforcement learning to enable instruction following and complex reasoning. However, such pretraining provides only weak visual grounding: short, coarse captions bias model
The continuous evolution of MLLMs and the increasing demand for more precise and reliable AI interactions necessitate novel approaches to visual grounding, leading researchers to explore sophisticated techniques like RL for pre-alignment.
Improving the effective utilization of visual evidence in MLLMs is critical for advancing their capabilities, moving beyond superficial understanding to deep, contextually relevant reasoning, which has implications for various AI applications.
The proposed 'See First, Answer Later' paradigm suggests a shift from broad caption-based pretraining to sufficiency-driven reinforcement learning, enabling more robust visual-text alignment and reducing inconsistencies.
- · AI researchers
- · Developers of MLLM applications
- · Industries relying on visual AI for complex reasoning
- · MLLMs relying solely on coarse caption-based pretraining
- · Systems with poor visual grounding
More accurate and reliable multimodal AI systems emerge, capable of deeper visual understanding.
Improved MLLMs accelerate advancements in fields like robotics, autonomous driving, and medical imaging by enabling better visual input interpretation.
The enhanced capability for visual reasoning in AI could lead to new forms of human-computer interaction and significantly impact decision-making processes across various sectors.
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Read at arXiv cs.AI