24 citations · 42 across the 15 of their papers we have counts for
5 papers · 1 filter
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity
Wentao Guo, Jikai Long, Yimeng Zeng +9
Zeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, the application of ZO fine-tuning in memory-…
TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching
Yecheng Jason Ma, Kausik Sivakumar, Jason Yan +2
Standard model-based reinforcement learning (MBRL) approaches fit a transition model of the environment to all past experience, but this wastes model capacity on data that is irrel…
Robust Subtask Learning for Compositional Generalization
Kishor Jothimurugan, Steve Hsu, Osbert Bastani +1
Compositional reinforcement learning is a promising approach for training policies to perform complex long-horizon tasks. Typically, a high-level task is decomposed into a sequence…
PAC Prediction Sets for Meta-Learning
Sangdon Park, Edgar Dobriban, Insup Lee +1
Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles. We study this problem in…
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
Yecheng Jason Ma, Andrew Shen, Osbert Bastani +1
Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reduci…