5 papers
MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI
Bohan Lyu, Yucheng Yang, Siqiao Huang +25
Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities i…
On the Nonlinearity of Learning Rate Scaling for LLM Training
Zaiwen Yang, Huaqing Zhang, Jing Xu +1
Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrapolate from smaller runs. Exist…
Task Generalization With AutoRegressive Compositional Structure: Can Learning From Tasks Generalize to Tasks?
Amirhesam Abedsoltan, Huaqing Zhang, Kaiyue Wen +3
Large language models (LLMs) exhibit remarkable task generalization, solving tasks they were never explicitly trained on with only a few demonstrations. This raises a fundamental q…
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency
Kaiyue Wen, Huaqing Zhang, Hongzhou Lin +1
Chain-of-thought (CoT) significantly enhances the reasoning performance of large language models (LLM). While current theoretical studies often attribute this improvement to increa…
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems
Huaqing Zhang, Lesi Chen, Jing Xu +1
This paper studies simple bilevel problems, where a convex upper-level function is minimized over the optimal solutions of a convex lower-level problem. We first show the fundament…