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cs.AI2025
When Reasoning Meets Its Laws
Junyu Zhang, Yifan Sun, Tianang Leng +4
Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretica…
cs.AI2025
MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning
Jingyan Shen, Jiarui Yao, Rui Yang +5
Reward modeling is a key step in building safe foundation models when applying reinforcement learning from human feedback (RLHF) to align Large Language Models (LLMs). However, rew…
cs.AI2025
Rethinking Diverse Human Preference Learning through Principal Component Analysis
Feng Luo, Rui Yang, Hao Sun +5
Understanding human preferences is crucial for improving foundation models and building personalized AI systems. However, preferences are inherently diverse and complex, making it…