← Search

Hangcheng Li

2 accepted papers

2026

Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning

ICLR 2026poster

Vision-language reinforcement learning (RL) has primarily focused on narrow domains (e.g. geometry or chart reasoning). This leaves broader training scenarios and resources underexplored, limiting the exploration and learning of Vision Language Models (VLMs) through RL. We find video games inherentl…

Cited by 0SourcecodeScholar
2026

Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm

CVPR 2026

The "Thinking with Text" and "Thinking with Images" paradigms significantly improve the reasoning abilities of large language models (LLMs) and Vision-Language Models (VLMs). However, these paradigms have inherent limitations. (1) Images capture only single moments and fail to represent dynamic proc

Cited by 0SourcecodeScholar