5 papers · 1 filter
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…
Policy-Gradient Training of Language Models for Ranking
Ge Gao, Jonathan D. Chang, Claire Cardie +2
Text retrieval plays a crucial role in incorporating factual knowledge for decision making into language processing pipelines, ranging from chat-based web search to question answer…
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
Owen Oertell, Jonathan D. Chang, Yiyi Zhang +2
Reinforcement learning (RL) has improved guided image generation with diffusion models by directly optimizing rewards that capture image quality, aesthetics, and instruction follow…
Dataset Reset Policy Optimization for RLHF
Jonathan D. Chang, Wenhao Zhan, Owen Oertell +4
Reinforcement Learning (RL) from Human Preference-based feedback is a popular paradigm for fine-tuning generative models, which has produced impressive models such as GPT-4 and Cla…
Adversarial Imitation Learning via Boosting
Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang +2
Adversarial imitation learning (AIL) has stood out as a dominant framework across various imitation learning (IL) applications, with Discriminator Actor Critic (DAC) (Kostrikov et…