openrlhf-training

What problem does it solve?

This skill provides a high-performance, distributed framework for Reinforcement Learning from Human Feedback (RLHF), overcoming the challenges of training large language models (7B-70B+) by accelerating the process with Ray and vLLM. It makes RLHF training 2x faster than alternatives, significantly reducing compute costs and development cycles.

Core Features & Use Cases

• Accelerated RLHF: Train large models (7B-70B+) with PPO, GRPO, RLOO, and DPO algorithms, leveraging Ray for distributed training and vLLM for inference acceleration.
• Hybrid Engine Optimization: Efficiently share GPU resources across actor, critic, reward, and reference models to minimize idle time and maximize throughput.
• Comprehensive Workflows: Supports the full RLHF pipeline from Supervised Fine-Tuning (SFT) to Reward Model training and final PPO/GRPO optimization, streamlining complex model development.
• Use Case: Fine-tune a 70B parameter language model using PPO on a multi-GPU cluster, achieving state-of-the-art performance and significantly reducing training time compared to traditional methods.

Quick Start

Set up a Ray cluster and start PPO training for a Llama-3-8b model using the OpenRLHF framework.