36 citations · 72 across the 25 of their papers we have counts for
27 papers · 1 filter
Teaching Arithmetic to Small Transformers
Nayoung Lee, Kartik Sreenivasan, Jason D. Lee +2
Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks…
Settling the Sample Complexity of Online Reinforcement Learning
Zihan Zhang, Yuxin Chen, Jason D. Lee +1
A central issue lying at the heart of online reinforcement learning (RL) is data efficiency. While a number of recent works achieved asymptotically minimal regret in online RL, the…
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
Qian Yu, Yining Wang, Baihe Huang +2
In stochastic zeroth-order optimization, a problem of practical relevance is understanding how to fully exploit the local geometry of the underlying objective function. We consider…
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
Alex Damian, Eshaan Nichani, Rong Ge +1
We focus on the task of learning a single index model with respect to the isotropic Gaussian distribution in dimensions. Prior work has shown that the samp…
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
Gen Li, Wenhao Zhan, Jason D. Lee +2
This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment.…
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning
Yulai Zhao, Zhuoran Yang, Zhaoran Wang +1
Policy optimization methods with function approximation are widely used in multi-agent reinforcement learning. However, it remains elusive how to design such algorithms with statis…