
cosmos-rl
Cosmos-RL is a flexible and scalable Reinforcement Learning framework specialized for Physical AI applications.
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Cosmos-RL is a flexible and scalable Reinforcement Learning framework specialized for Physical AI applications. It provides a toolchain for large scale RL training workload with features like parallelism, asynchronous processing, low-precision training support, and a single-controller architecture. The system architecture includes Tensor Parallelism, Sequence Parallelism, Context Parallelism, FSDP Parallelism, and Pipeline Parallelism. It also utilizes a messaging system for coordinating policy and rollout replicas, along with dynamic NCCL Process Groups for fault-tolerant and elastic large-scale RL training.
README:
Cosmos-RL is a flexible and scalable Reinforcement Learning framework specialized for Physical AI applications.
Cosmos-RL provides toolchain to enable large scale RL training workload with following features:
-
Parallelism
- Tensor Parallelism
- Sequence Parallelism
- Context Parallelism
- FSDP Parallelism
- Pipeline Parallelism
-
Fully asynchronous (replicas specialization)
- Policy (Consumer): Replicas of training instances
- Rollout (Producer): Replicas of generation engines
- Low-precision training (FP8) and rollout (FP8 & FP4) support
-
Single-Controller Architecture
- Efficient messaging system (e.g.,
weight-sync
,rollout
,evaluate
) to coordinate policy and rollout replicas - Dynamic NCCL Process Groups for on-the-fly GPU [un]registration to enable fault-tolerant and elastic large-scale RL training
- Efficient messaging system (e.g.,
This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.
NVIDIA Cosmos source code is released under the Apache 2 License.
NVIDIA Cosmos models are released under the NVIDIA Open Model License. For a custom license, please contact [email protected].
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