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

Confident First, Reflective Second: Reinforcement Learning of Confidence-Preserving Reflection for Video Understanding

The paper introduces CoFire, a reinforcement learning framework for video understanding that addresses the problem of confidence erosion—where initial predictions lose reliability during iterative refinement. CoFire enforces a "confident first, reflective second" principle, ensuring the initial answer remains trustworthy and that subsequent reflection only improves upon it. The method achieves state‑of‑the‑art accuracy on Video‑MME, MLVU, and LongVideoBench, outperforming prior 7B models by 1.7 points and improving both initial and final accuracy over outcome‑reward RL by 4.1 and 3.7 points respectively.

Showing versus Doing: Teaching by Demonstration

arXiv:2610.14004v1 Announce Type: cross Abstract: People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contr...

Confident First, Reflective Second: Reinforcement Learning of Confidence-Preserving Reflection for Video Understanding

Video models answer from sampled frames and may miss the decisive moment. A common remedy is to commit to an answer, re-examine the video, and confirm or revise it, a loop now mainly learned with rein...