Illustrating Reinforcement Learning from Human Feedback (RLHF)

Gathering human feedback

RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.

From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial

arXiv:2601. 08662v3 Announce Type: replace Abstract: This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations.

RL²: Fast reinforcement learning via slow reinforcement learning

Prompt-Driven Exploration: Language as an Exploration Space for VLA Reinforcement Learning

arXiv:2607.08837v4 Announce Type: replace-cross Abstract: Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject...

Asymmetric actor critic for image-based robot learning

Stage-Transition Dense Reward Modeling for Reinforcement Learning

arXiv:2606. 31377v1 Announce Type: cross Abstract: Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to changes in environments and object configurations.