3 papers
cs.CL2026
Se-DPO: Self-Evolving Token Credit for Direct Preference Optimization
Wenxiao Zhao, Shu Wang, Ying Nian Wu
Direct Preference Optimization (DPO) aggregates token-level log-probability ratios via uniform summation, implicitly treating all tokens as contributing equally to the preference s…
cs.CL2026
Children's Intelligence Tests Pose Challenges for MLLMs? KidGym: A 2D Grid-Based Reasoning Benchmark for MLLMs
Hengwei Ye, Yuanting Guan, Yuxuan Ge +7
Multimodal Large Language Models (MLLMs) combine the linguistic strengths of LLMs with the ability to process multimodal data, enbaling them to address a broader range of visual ta…
cs.CL2024
Explore the Reasoning Capability of LLMs in the Chess Testbed
Shu Wang, Lei Ji, Renxi Wang +4
Reasoning is a central capability of human intelligence. In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabiliti…