3 papers
cs.LG2025
The Role of Environment Access in Agnostic Reinforcement Learning
Akshay Krishnamurthy, Gene Li, Ayush Sekhari
We study Reinforcement Learning (RL) in environments with large state spaces, where function approximation is required for sample-efficient learning. Departing from a long history…
cs.LG2025
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics
Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy +1
We study computationally and statistically efficient Reinforcement Learning algorithms for the linear Bellman Complete setting. This setting uses linear function approximation to c…
cs.AI2025
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization
Audrey Huang, Wenhao Zhan, Tengyang Xie +4
Language model alignment methods such as reinforcement learning from human feedback (RLHF) have led to impressive advances in language model capabilities, but are limited by a wide…