2 citations · 7 across the 31 of their papers we have counts for
15 papers · 1 filter
Decoupled Physical Modeling and Execution for Physics Reasoning
Ye Zhang, Xuehang Guo, Rui Pan +4
Physics reasoning requires constructing a consistent model of the underlying physical system rather than relying solely on symbolic or formula-based manipulation. Although large la…
StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models
Dingzhi Yu, Rui Pan, Yuxing Liu +2
Sign-based optimization algorithms, such as SignSGD, have garnered attention for their performance in distributed learning and training large foundation models. Despite their empir…
Unbiased Gradient Low-Rank Projection
Rui Pan, Yang Luo, Yuxing Liu +2
Memory-efficient optimization is critical for training increasingly large language models (LLMs). A popular strategy involves gradient low-rank projection, storing only the project…
GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Ruida Wang, Jiarui Yao, Rui Pan +2
Solving math problems through verifiable languages such as Lean has significantly impacted both the mathematics and computer science communities. Current state-of-the-art models ar…
Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
Yuxing Liu, Yuze Ge, Rui Pan +2
Learning rate warmup is a popular and practical technique in training large-scale deep neural networks. Despite the huge success in practice, the theoretical advantages of this str…
GUIDE: Towards Scalable Advising for Research Ideas
Yaowenqi Liu, Bingxu Meng, Rui Pan +4
The field of AI research is advancing at an unprecedented pace, enabling automated hypothesis generation and experimental design across diverse domains such as biology, mathematics…