4 papers · 1 filter
Provably Efficient Online RLHF with One-Pass Reward Modeling
Long-Fei Li, Yu-Yang Qian, Peng Zhao +1
Reinforcement Learning from Human Feedback (RLHF) has shown remarkable success in aligning Large Language Models (LLMs) with human preferences. Traditional RLHF methods rely on a f…
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
We study episodic linear mixture MDPs with the unknown transition and adversarial rewards under full-information feedback, employing dynamic regret as the performance measure. We s…
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
Long-Fei Li, Yu-Jie Zhang, Peng Zhao +1
We study a new class of MDPs that employs multinomial logit (MNL) function approximation to ensure valid probability distributions over the state space. Despite its significant ben…
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
We study reinforcement learning with linear function approximation, unknown transition, and adversarial losses in the bandit feedback setting. Specifically, we focus on linear mixt…