2 citations · 3 across the 9 of their papers we have counts for
10 papers
KARL: Knowledge Agents via Reinforcement Learning
Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal +23
We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic sea…
LLMs Can Learn to Reason Via Off-Policy RL
Daniel Ritter, Owen Oertell, Bradley Guo +3
Reinforcement learning (RL) approaches for Large Language Models (LLMs) frequently use on-policy algorithms, such as PPO or GRPO. However, policy lag from distributed training arch…
Value-Guided Search for Efficient Chain-of-Thought Reasoning
Kaiwen Wang, Jin Peng Zhou, Jonathan Chang +4
In this paper, we propose a simple and efficient method for value model training on long-context reasoning traces. Compared to existing process reward models (PRMs), our method doe…
: Provably Optimal Distributional RL for LLM Post-Training
Jin Peng Zhou, Kaiwen Wang, Jonathan Chang +5
Reinforcement learning (RL) post-training is crucial for LLM alignment and reasoning, but existing policy-based methods, such as PPO and DPO, can fall short of fixing shortcuts inh…
Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF
Zhaolin Gao, Wenhao Zhan, Jonathan D. Chang +4
Large Language Models (LLMs) have achieved remarkable success at tasks like summarization that involve a single turn of interaction. However, they can still struggle with multi-tur…
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…
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…
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…
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…