PaLM-rlhf-pytorch

Why we included this project

Researchers and ML engineers who want to see how a ChatGPT-style system is actually trained rather than just served can trace the full RLHF pipeline here: supervised fine-tuning, reward modeling, and policy optimization with PPO, all in PyTorch on the PaLM transformer architecture. The author is upfront that this is a work in progress and a map more than a finished artifact, so treat it as study-grade reference code rather than something deployable. Its real value is as a readable illustration of how reward models and policy optimization fit together, and the README links the implementation to the original OpenAI ChatGPT blog post and wider open efforts like CarperAI's TRLX and LAION's Open Assistant. No pretrained weights ship with the repo, so it suits learning and experimentation better than production inference, a practical starting point before moving to more mature RLHF frameworks.