openrlhf-training

Distributed RLHF training with vLLM acceleration is enabled by combining Ray for multi-GPU computing and vLLM for fast inference, supporting algorithms like PPO and DPO. You can start by installing the required libraries via pip.

Yes, training a 70B large language model using PPO is supported for distributed computing environments. The setup optimizes for speed and stability by leveraging Ray across multiple GPUs and utilizing vLLM for efficient inference.

Supported reinforcement learning algorithms for large language model training include PPO, GRPO, RLOO, and DPO. These algorithms are optimized for high-performance execution using distributed computing and vLLM inference acceleration.

Yes, Ray and vLLM are required dependencies to perform distributed RLHF training. Ray handles the distributed computing across multiple GPUs, while vLLM provides the inference acceleration necessary for high-performance model training.

The best way to accelerate inference during large language model training is using vLLM. It optimizes the generation phase of RLHF algorithms, working in conjunction with Ray and DeepSpeed to maximize training speed and stability.

A limitation of using vLLM for RLHF training is the strict dependency on specific libraries including Ray, vLLM, torch, transformers, and deepspeed. This requires a multi-GPU distributed computing environment to function properly.