5 papers
QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
Kuei-Chun Kao, Hsu Tzu-Yin, Yunqi Hong +2
Recently, Multimodal Large Language Models (MLLMs) encounter two key issues in multi-image contexts: (1) a lack of fine-grained perception across disparate images, and (2) a dimini…
Concepts or Skills? Rethinking Instruction Selection for Multi-modal Models
Andrew Bai, Justin Cui, Ruochen Wang +1
Vision-language instruction tuning achieves two main purposes: learning visual concepts and learning visual skills. In this paper, we found that vision-language benchmarks fall int…
Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models
Sohyun An, Ruochen Wang, Tianyi Zhou +1
While recent success of large reasoning models (LRMs) significantly advanced LLMs' reasoning capability by optimizing the final answer accuracy using reinforcement learning, they m…
Exploring Expert Failures Improves LLM Agent Tuning
Li-Cheng Lan, Andrew Bai, Minhao Cheng +2
Large Language Models (LLMs) have shown tremendous potential as agents, excelling at tasks that require multiple rounds of reasoning and interactions. Rejection Sampling Fine-Tunin…
R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model
Hengguang Zhou, Xirui Li, Ruochen Wang +3
Recently DeepSeek R1 demonstrated how reinforcement learning with simple rule-based incentives can enable autonomous development of complex reasoning in large language models, char…