5 papers · 1 filter
Algorithmic Separation between Constant-Depth and Logarithmic-Depth Neural Networks
Yunwei Ren, Zihao Wang, Jason D. Lee
Despite the empirical advantages of deep networks over shallow ones, theoretical depth separations largely concern approximation power, while algorithmic results are mostly limited…
What Makes a Reward Model a Good Teacher? An Optimization Perspective
Noam Razin, Zixuan Wang, Hubert Strauss +3
The success of Reinforcement Learning from Human Feedback (RLHF) critically depends on the quality of the reward model. However, while this quality is primarily evaluated through a…
Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
Jianhao Huang, Zixuan Wang, Jason D. Lee
Chain of Thought (CoT) prompting has been shown to significantly improve the performance of large language models (LLMs), particularly in arithmetic and reasoning tasks, by instruc…
Learning and Transferring Sparse Contextual Bigrams with Linear Transformers
Yunwei Ren, Zixuan Wang, Jason D. Lee
Transformers have excelled in natural language modeling and one reason behind this success is their exceptional ability to combine contextual informal and global knowledge. However…
Learning Orthogonal Multi-Index Models: A Fine-Grained Information Exponent Analysis
Yunwei Ren, Jason D. Lee
The information exponent ([BAGJ21]) and its extensions -- which are equivalent to the lowest degree in the Hermite expansion of the link function (after a potential label transform…