382 citations · 978 across the 50 of their papers we have counts for
5 papers · 1 filter
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage
Yuda Song, Gokul Swamy, Aarti Singh +2
Learning from human preference data has emerged as the dominant paradigm for fine-tuning large language models (LLMs). The two most common families of techniques -- online reinforc…
Hybrid Reinforcement Learning from Offline Observation Alone
Yuda Song, J. Andrew Bagnell, Aarti Singh
We consider the hybrid reinforcement learning setting where the agent has access to both offline data and online interactive access. While Reinforcement Learning (RL) research typi…
REBEL: Reinforcement Learning via Regressing Relative Rewards
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan +7
While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) application…
The Virtues of Pessimism in Inverse Reinforcement Learning
David Wu, Gokul Swamy, J. Andrew Bagnell +2
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a comp…
Hybrid Inverse Reinforcement Learning
Juntao Ren, Gokul Swamy, Zhiwei Steven Wu +2
The inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations wi…