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lbliii merged 1 commit into from
Dec 10, 2025
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Add Data Designer and links to ecosystem page #462

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17 changes: 9 additions & 8 deletions docs/about/ecosystem.md
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Expand Up @@ -12,15 +12,16 @@ For details on NeMo Gym capabilities, refer to the

## NeMo Gym Within the NeMo Framework

The [NeMo Framework](https://github.com/NVIDIA-NeMo) is NVIDIA's GPU-accelerated platform for training large language models (LLMs), multimodal models, and speech models. It includes multiple specialized components:
NeMo Framework includes modular libraries for end-to-end model training:

* **NeMo Megatron-Bridge**: Pretraining and fine-tuning with Megatron-Core
* **NeMo AutoModel**: PyTorch native training for Hugging Face models
* **NeMo RL**: Scalable reinforcement learning toolkit
* **NeMo Gym**: RL environment infrastructure and rollout collection (this project)
* **NeMo Curator**: Data preprocessing and curation
* **NeMo Evaluator**: Model evaluation and benchmarking
* **NeMo Guardrails**: Programmable safety guardrails
* **[NeMo Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)**: Pretraining and fine-tuning with Megatron-Core
* **[NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel)**: PyTorch native training for Hugging Face models
* **[NeMo RL](https://github.com/NVIDIA-NeMo/RL)**: Scalable and efficient post-training
* **[NeMo Gym](https://github.com/NVIDIA-NeMo/Gym)**: RL environment infrastructure and rollout collection (this project)
* **[NeMo Curator](https://github.com/NVIDIA-NeMo/Curator)**: Data preprocessing and curation
* **[NeMo Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner)**: Synthetic data generation from scratch or seed datasets
* **[NeMo Evaluator](https://github.com/NVIDIA-NeMo/Evaluator)**: Model evaluation and benchmarking
* **[NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails)**: Programmable safety guardrails
* And more...

**NeMo Gym's Role**: Within this ecosystem, Gym focuses on standardizing scalable rollout collection for RL training. It provides unified interfaces to heterogeneous RL environments and curated resource servers with verification logic. This makes it practical to generate large-scale, high-quality training data for NeMo RL and other training frameworks.