Megatron RL#

Reinforcement learning library for post-training large language models at scale.

Overview#

Megatron RL adds native reinforcement learning capabilities to Megatron-LM for large-scale RL-based post-training of foundation models.

Note: Megatron RL is under active development and primarily designed for research teams exploring RL post-training on modern NVIDIA hardware. For production deployments, use NeMo RL.

Key Features#

  • Decoupled Design - Clean separation between agent/environment logic and RL implementation

  • Flexible Inference - Support for Megatron, OpenAI, and HuggingFace inference backends

  • Trainer/Evaluator - Manages rollout generation and coordinates with inference systems

  • Megatron Integration - Native integration with Megatron Core inference system

Architecture#

Components#

Agents & Environments

  • Accept inference handles

  • Return experience rollouts with rewards

  • Implement custom RL logic

Trainer/Evaluator

  • Controls rollout generation

  • Coordinates with inference systems

  • Manages training loops

Inference Interface

  • Provides .generate(prompt, **generation_args) endpoint

  • Supports multiple backends (Megatron, OpenAI, HuggingFace)

Use Cases#

  • RLHF (Reinforcement Learning from Human Feedback)

  • Custom reward-based fine-tuning

  • Policy optimization for specific tasks

  • Research on RL post-training techniques

Resources#