3 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
Expressive Value Learning for Scalable Offline Reinforcement Learning
Nicolas Espinosa-Dice, Kiante Brantley, Wen Sun
Reinforcement learning (RL) is a powerful paradigm for learning to make sequences of decisions. However, RL has yet to be fully leveraged in robotics, principally due to its lack o…
Prompt Curriculum Learning for Efficient LLM Post-Training
Zhaolin Gao, Joongwon Kim, Wen Sun +4
We introduce Prompt Curriculum Learning (PCL), a lightweight reinforcement learning (RL) algorithm that selects intermediate-difficulty prompts using a learned value model to post-…
Efficient Imitation under Misspecification
Nicolas Espinosa-Dice, Sanjiban Choudhury, Wen Sun +1
We consider the problem of imitation learning under misspecification: settings where the learner is fundamentally unable to replicate expert behavior everywhere. This is often true…
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning
Gokul Swamy, Sanjiban Choudhury, Wen Sun +2
From a first-principles perspective, it may seem odd that the strongest results in foundation model fine-tuning (FT) are achieved via a relatively complex, two-stage training proce…
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds
Zhiyong Wang, Dongruo Zhou, John C. S. Lui +1
Learning a transition model via Maximum Likelihood Estimation (MLE) followed by planning inside the learned model is perhaps the most standard and simplest Model-based Reinforcemen…
Faster Recalibration of an Online Predictor via Approachability
Princewill Okoroafor, Robert Kleinberg, Wen Sun
Predictive models in ML need to be trustworthy and reliable, which often at the very least means outputting calibrated probabilities. This can be particularly difficult to guarante…