3 citations · 6 across the 19 of their papers we have counts for
7 papers · 1 filter
Self-Improvement in Language Models: The Sharpening Mechanism
Audrey Huang, Adam Block, Dylan J. Foster +5
Recent work in language modeling has raised the possibility of self-improvement, where a language models evaluates and refines its own generations to achieve higher performance wit…
Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Philip Amortila, Dylan J. Foster, Nan Jiang +2
Real-world applications of reinforcement learning often involve environments where agents operate on complex, high-dimensional observations, but the underlying (''latent'') dynamic…
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy +3
Reinforcement learning from human feedback (RLHF) has emerged as a central tool for language model alignment. We consider online exploration in RLHF, which exploits interactive acc…
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Yuda Song, Lili Wu, Dylan J. Foster +1
Sample-efficiency and reliability remain major bottlenecks toward wide adoption of reinforcement learning algorithms in continuous settings with high-dimensional perceptual inputs.…
Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster +2
We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on…
Scalable Online Exploration via Coverability
Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy
Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We propose exploration objectives -- policy…
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning
Philip Amortila, Tongyi Cao, Akshay Krishnamurthy
A pervasive phenomenon in machine learning applications is distribution shift, where training and deployment conditions for a machine learning model differ. As distribution shift t…