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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 - Separates agent and environment logic from the core RL implementation

  • Inference Backends - Megatron, OpenAI, and Hugging Face inference stacks

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

  • Megatron Integration - Native integration with Megatron Core inference system

Architecture#

Components#

Agents and Environments

  • Accept inference handles

  • Return experience rollouts with rewards

  • Implement custom RL logic

Trainer or Evaluator

  • Controls rollout generation

  • Coordinates with inference systems

  • Manages training loops

Inference Interface

  • Exposes a .generate(prompt, **generation_args) endpoint

  • Supports multiple backends (Megatron, OpenAI, Hugging Face)

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#