4 papers
AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection
Peng Lai, He Zhu, Zhiwen Ruan +6
Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets a…
VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation
Yixia Li, Yaqing Shi, Zhiwen Ruan +6
Multimodal large language models have advanced rapidly, yet most remain English-centric, as scaling multilingual multimodal instruction tuning is limited by the scarcity and high c…
From Abstract to Contextual: What LLMs Still Cannot Do in Mathematics
Bowen Cao, Dongdong Zhang, Yixia Li +8
Large language models now solve many benchmark math problems at near-expert levels, yet this progress has not fully translated into reliable performance in real-world applications.…
From Word to World: Can Large Language Models be Implicit Text-based World Models?
Yixia Li, Hongru Wang, Jiahao Qiu +7
Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World…