openrlhf/openrlhf is an RLHF framework that distributes LLM post-training on top of Ray. The latest release in the repository is v0.11.2, and the source is the GitHub repository page. However, the source material does not include the changes in v0.11.2, so what changed in this release cannot be confirmed from this material.
RLHF is a post-training approach that first aligns a model's answers with human feedback and then refines them further with reinforcement learning. openrlhf/openrlhf performs this process on a distributed architecture that uses Ray and vLLM together. It supports SFT and DPO as well as policy-learning algorithms (PPO, DAPO, REINFORCE++, GRPO, RLOO), and single-turn and multi-turn execution are both possible. Custom reward functions and external environments can also be used. Distributed training and inference are supported on the basis of DeepSpeed, HuggingFace Transformers and NCCL/CUDA IPC, and a hybrid engine uses GPU resources efficiently. The README explains that this structure is combined with an integrated agent-based design. It is written in Python, its license is Apache-2.0, it was created on July 30, 2023, and its star count is 10103.
It is still too early to judge. The source material's momentum score is a low 2.8 out of 10, and its novelty, technology and quality scores are not recorded. In other words, the size of the repository is known, but there is no evidence measuring how new this approach is. For teams that want to try combining a post-training stack directly on Ray and vLLM, this is a repository worth checking out, but at this stage there is no remaining basis for claiming superiority.