Detailed Introduction
NeMo RL (NVIDIA NeMo RL) is a scalable post-training reinforcement learning toolkit within the NVIDIA NeMo ecosystem, designed to provide high-performance, reproducible training and evaluation pipelines for large language models (LLMs) and multimodal models. The project supports multiple training and generation backends (DTensor, Megatron, vLLM), and offers modular components (e.g., nemo_rl, examples, research) for research and production deployment.
Main Features
- Post-training support: includes GRPO, DPO, SFT, RM training paradigms with example configurations.
- Multi-backend compatibility: DTensor, Megatron Core, vLLM, and more for efficient training and generation.
- Extensible architecture: modular design to integrate custom environments, algorithms, and parallelism strategies.
- Enterprise documentation and examples: comprehensive docs and practical guides for cluster deployment and performance tuning.
Use Cases
- Reinforcement fine-tuning and post-training on large models to improve performance in multi-turn tasks and tool-use scenarios.
- Running large-scale experiments on clusters or cloud, leveraging Megatron or DTensor for long sequences and large models.
- Research and education: reproduce experiments, compare algorithms, and run performance benchmarks.
Technical Features
- Implemented in Python and compatible with common deep-learning toolchains, supporting advanced parallelisms (TP/PP/CP/SP/FSDP).
- Integrates Ray for scheduling and isolation, enabling multi-environment parallel training and resource isolation.
- Provides command-line and configuration-driven interfaces, with example scripts for quickstart and reproducibility.