openrlhf-training

Distributed RLHF training with PPO is orchestrated across multi-node clusters using Ray and vLLM. The workflow coordinates actor, critic, reward, and reference models while leveraging ZeRO-3 and vLLM acceleration to speed up rollout generation for scalable GPU-efficient experimentation.

Yes, vLLM accelerates rollout generation in RLHF workflows by leveraging fast inference. This integration speeds up the generation phase across PPO, GRPO, RLOO, and DPO algorithms, significantly reducing the overall training time for large language models.

Yes, a hybrid engine enables GPU sharing through colocation and sleep/wake cycles for multiple models during RLHF training. This mechanism maximizes GPU utilization across actor, critic, reward, and reference models in multi-model distributed setups.

Besides PPO, the distributed RLHF training supports GRPO, RLOO, and DPO algorithms. These workflows are orchestrated across Ray and vLLM clusters, enabling scalable and GPU-efficient experimentation for large language model alignment.

Long-running distributed RLHF experiments can be resumed from checkpoints using fault-tolerant checkpointing support. This ensures that multi-node training jobs on Ray clusters can recover from interruptions and continue training without losing progress.

To set up the Ray-based RLHF training environment, you need openrlhf, ray, vllm, torch, transformers, and deepspeed. These dependencies enable distributed orchestration, fast inference, and ZeRO-3 optimization for scalable model training.