verl-rl-training

Distributed RLHF training orchestrates large-scale fine-tuning using verl's HybridFlow architecture. It configures PPO and GRPO workflows across multi-node GPUs, utilizing YAML-driven settings for algorithm selection, trainer parameters, and actor rollout strategies to execute scalable reinforcement learning.

vLLM and SGLang are supported as configurable actor rollout backends. The architecture also integrates HuggingFace Transformers and Megatron-LM, allowing flexible backend selection for rollout and training operations during large-scale reinforcement learning workflows.

HybridFlow architecture is a production-grade framework for orchestrating RL training. It structures end-to-end workflows by separating configurable rollout backends, trainer settings, and actor strategies, enabling efficient distributed GPU execution for RLHF-style fine-tuning.

You configure GRPO workflows through YAML files defining algorithm, trainer, actor_rollout_ref, and rollout parameters. This config-driven approach manages multi-backend rollout and distributed GPU training for scalable reinforcement learning experiments.

Multi-node GPU training is fully supported for RLHF fine-tuning. The system orchestrates production-grade distributed training across large models, managing experiment execution and end-to-end orchestration through config-driven workflows.

Megatron-LM functions as one of multiple actor rollout backend options alongside vLLM, SGLang, and HuggingFace Transformers. Backend selection dictates specific rollout strategy configurations and integration parameters within the HybridFlow architecture.