About PaLM-rlhf-pytorch
An open-source implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM (Pathways Language Model) architecture, analogous to ChatGPT but using PaLM. The repository provides the code for training a PaLM model, training a reward model with human feedback (with optional LoRA fine-tuning), and then using PPO to fine-tune the language model based on the reward model. It also supports Flash Attention from PyTorch 2.0. Note: This is a research implementation; no pre-trained models are provided, and substantial compute and data are required to replicate ChatGPT-like results.
Key Features
Implements RLHF (Reinforcement Learning with Human Feedback) pipeline on PaLM architecture
Includes PaLM autoregressive transformer with configurable parameters (num_tokens, dim, depth)
Reward model with binned output for human preference ratings (e.g., 1-5)
Supports optional LoRA fine-tuning for the reward model to prevent overfitting
Integrates Flash Attention from PyTorch 2.0 for improved training speed
Provides training scripts (train.py) and example usage (examples.py)
Pros & Cons
Pros
- Open-source and modular – can be adapted for different model sizes and datasets
- Includes both PaLM training and RLHF pipeline in a single repository
- Supports Flash Attention for faster training and reduced memory usage
- Optional LoRA fine-tuning for the reward model to improve generalization
- Backed by community efforts like CarperAI and mentioned in RLHF blog posts
Cons
- No pre-trained or fine-tuned models are provided; training requires massive compute and data
- Implementation is experimental and may require significant tuning for real-world use
- Limited documentation beyond code comments and readme (no detailed API docs)
- Dependence on large PyTorch ecosystem and hardware (CUDA) for training
Best For
Training large language models with human feedback alignmentBuilding open-source ChatGPT-like conversational AI systemsResearch on reinforcement learning from human preferences in NLPExperimenting with PaLM-based architectures and RLHF pipelines
FAQ
Does this repository contain a trained model that I can use for inference?
No. The repository only contains the code (the ship and map). No trained models are provided. Training requires millions of dollars of compute and data, plus expertise to achieve production-quality results.