lucidrains/PaLM-rlhf-pytorch

Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

What it solves

This project provides a PyTorch implementation of Reinforcement Learning from Human Feedback (RLHF) using the PaLM architecture. It allows developers to align a pretrained language model with human preferences by training a reward model based on human-curated feedback and then optimizing the model's output using reinforcement learning.

How it works

The process is broken down into three main stages:

  1. Base Model Training: A PaLM transformer is trained as a standard autoregressive model.
  2. Reward Model Training: A RewardModel is created (optionally using LoRA for efficient fine-tuning) to predict human ratings for given sequences.
  3. RLHF Optimization: The RLHFTrainer takes both the pretrained PaLM model and the reward model to refine the language model's generation capabilities through reinforcement learning episodes.

Who it’s for

It is designed for AI researchers and developers who want to replicate the RLHF pipeline used in models like ChatGPT using an open-source implementation of the PaLM architecture.

Highlights

  • LoRA Support: Includes the option to fine-tune the reward model using Low-Rank Adaptation to prevent overfitting.
  • Flash Attention: Integrated for faster and more memory-efficient attention mechanisms.
  • Complete Pipeline: Provides a structured workflow from base transformer training to reward modeling and final RLHF training.

Related

  • Dispatch
    Illustrating Reinforcement Learning from Human Feedback (RLHF)

    Hugging Face explains Reinforcement Learning from Human Feedback (RLHF), a three-step process used to align large language models with complex human values by optimizing them using a reward model based on human preferences.

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    FareedKhan-dev/train-llm-from-scratch

    A complete from-scratch PyTorch implementation of an LLM pipeline, covering everything from raw text tokenization and Transformer architecture to SFT and advanced RLHF alignment techniques like PPO and GRPO.

  • Dispatch
    Anthropic: Training a Helpful and Harmless Assistant with RLHF

    Anthropic demonstrates that Reinforcement Learning from Human Feedback (RLHF) improves language model performance across most NLP evaluations while ensuring the assistant remains helpful and harmless.

  • Project
    OpenRLHF/OpenRLHF

    A high-performance RLHF framework that combines Ray and vLLM to scale reinforcement learning from human feedback for LLMs and VLMs.