arXiv:2507.06999v2 Announce Type: replace-cross Abstract: Reasoning is essential for large language models (LLMs), especially in complex tasks such as mathematical problem solving. However, multimodal reasoning still faces challenges in modality alignment and training scalability, as many existing methods rely on additional annotations or complex rule-based rewards. To address these issues, we propose the Deliberate-to-Intuitive reasoning framework (D2I), which improves the understanding and reasoning abilities of multimodal LLMs (MLLMs) without extra annotations or complex rewards. During tra
The continuous rapid advancements in AI research, particularly in multimodal models, drive the constant need for more efficient and scalable reasoning frameworks.
This research addresses fundamental challenges in multimodal large language models (MLLMs) by improving reasoning without needing extra annotations or complex rewards, potentially accelerating AI development.
The proposed D2I framework could lead to more robust and scalable MLLMs, reducing annotation overhead and simplifying the training process for complex tasks.
- · AI researchers
- · Multimodal LLM developers
- · SaaS companies leveraging AI agents
- · Industries requiring complex multimodal reasoning
- · Companies reliant on expensive manual data annotation for MLLM training
Improved multimodal reasoning in LLMs will enable more sophisticated AI applications across various domains.
The reduced need for complex annotations could democratize access to advanced MLLM development.
This could contribute to the development of more capable and autonomous AI agents capable of understanding and interacting with the world more comprehensively, potentially collapsing white-collar workflows.
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Read at arXiv cs.LG