arXiv:2605.11651v4 Announce Type: replace-cross Abstract: Recent think-answer approaches in VLMs, such as Qwen3-VL-Thinking, boost reasoning performance by leveraging intermediate thinking steps before the final answer, but their computational cost becomes substantial, especially for larger VLMs. To distill such capabilities into compact think-answer VLMs, a primary objective is to improve the student's ability to utilize visual evidence throughout its reasoning trace, as long think-answer traces suffer from visual forgetting issues. To this end, we introduce a novel think-answer distillation
The proliferation of large vision-language models (VLMs) and the increasing demand for more efficient AI systems necessitates innovations in distillation to reduce computational overhead.
Improving the efficiency of reasoning in VLMs through distillation allows for the deployment of advanced AI capabilities in more constrained environments, broadening their application and accessibility.
The computational cost and 'visual forgetting' issues in VLM reasoning are being directly addressed, paving the way for more compact and effective 'think-answer' models.
- · AI developers
- · Edge AI providers
- · Cloud AI infrastructure
- · Users of VLM applications
- · Inefficient large VLM architectures
More efficient and capable vision-language models become available for a wider range of applications.
The reduced computational burden could accelerate the deployment of advanced AI in smaller devices and real-time systems.
Increased accessibility to powerful 'think-answer' VLMs might lead to new classes of AI agents and automated reasoning systems.
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Read at arXiv cs.CL