3 papers
cs.CV2025
Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm
Jingqi Tong, Yurong Mou, Hangcheng Li +11
The "Thinking with Text" and "Thinking with Images" paradigms significantly improve the reasoning abilities of large language models (LLMs) and Vision-Language Models (VLMs). Howev…
cs.CL2025
Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
Jingqi Tong, Jixin Tang, Hangcheng Li +21
Vision-language reinforcement learning (RL) has primarily focused on narrow domains (e.g. geometry or chart reasoning). This leaves broader training scenarios and resources underex…
cs.CL2024
RMB: Comprehensively Benchmarking Reward Models in LLM Alignment
Enyu Zhou, Guodong Zheng, Binghai Wang +11
Reward models (RMs) guide the alignment of large language models (LLMs), steering them toward behaviors preferred by humans. Evaluating RMs is the key to better aligning LLMs. Howe…