Putting RL back in RLHF

The N Implementation Details of RLHF with PPO

Welcome RL Environments to the hub

Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries

vLLM V0 to V1: Correctness Before Corrections in RL

Open R1: Update #4

Open R1: Update #3

RL²: Fast reinforcement learning via slow reinforcement learning

Gotta Learn Fast: A new benchmark for generalization in RL

Open R1: Update #2

Spinning Up in Deep RL

We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.

Representation and Invariance in Reinforcement Learning

arXiv:2112. 07752v4 Announce Type: replace-cross Abstract: Researchers have formalized reinforcement learning (RL) in different ways.

RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control

RLLBC-Lib is an educational code library designed to lower the entry barrier for students learning reinforcement learning (RL) in the context of learning-based control. It offers a comprehensive collection of tabular RL methods to reinforce theoretical foundations, followed by a deep RL library that mirrors the same design principles to highlight parallels between simple and state‑of‑the‑art approaches. The library also includes implementations that illustrate core RL principles, contrast RL with other learning‑based control methods, and serve as a foundation for programming assignments with automated grading.