6 papers · 1 filter
PhysProver: Advancing Automatic Theorem Proving for Physics
Hanning Zhang, Ruida Wang, Rui Pan +3
The combination of verifiable languages and LLMs has significantly influenced both the mathematical and computer science communities because it provides a rigorous foundation for t…
Lean4Physics: Comprehensive Reasoning Framework for College-level Physics in Lean4
Yuxin Li, Minghao Liu, Ruida Wang +6
We present **Lean4PHYS**, a comprehensive reasoning framework for college-level physics problems in Lean4. **Lean4PHYS** includes *LeanPhysBench*, a college-level benchmark for for…
ERA: Transforming VLMs into Embodied Agents via Embodied Prior Learning and Online Reinforcement Learning
Hanyang Chen, Mark Zhao, Rui Yang +15
Recent advances in embodied AI highlight the potential of vision language models (VLMs) as agents capable of perception, reasoning, and interaction in complex environments. However…
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…
Generalizable Geometric Image Caption Synthesis
Yue Xin, Wenyuan Wang, Rui Pan +5
Multimodal large language models have various practical applications that demand strong reasoning abilities. Despite recent advancements, these models still struggle to solve compl…
Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Yifan Hao, Xingyuan Pan, Hanning Zhang +3
Supervised fine-tuning (SFT) on domain-specific data is the dominant approach for adapting foundation models to specialized tasks. However, it has been observed that SFT models ten…