docs: ndd added to ecosystem

docs/about/ecosystem.md

@@ -19,8 +19,69 @@ The [NeMo Framework](https://github.com/NVIDIA-NeMo) is NVIDIA's GPU-accelerated
 * **NeMo RL**: Scalable reinforcement learning toolkit
 * **NeMo Gym**: RL environment infrastructure and rollout collection (this project)
 * **NeMo Curator**: Data preprocessing and curation
+* **NeMo Data Designer**: Synthetic data generation for post-training
 * **NeMo Evaluator**: Model evaluation and benchmarking
 * **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.
+
+---
+
+## NeMo Gym and NeMo Data Designer
+
+[NeMo Data Designer](https://nvidia-nemo.github.io/DataDesigner/) is a general framework for generating high-quality synthetic data from scratch or using seed data. Both tools generate training data, but they serve different use cases and employ different generation strategies.
+
+### Key Differences
+
+```{list-table}
+:header-rows: 1
+:widths: 20 40 40
+
+* - Aspect
+  - NeMo Data Designer
+  - NeMo Gym
+* - **Approach**
+  - Synthetic data generation using LLM prompting combined with statistical samplers
+  - Real environment interactions with live tool execution
+* - **Tool Calls**
+  - Simulates tool calling patterns and responses
+  - Executes actual tool calls against real systems
+* - **Verification**
+  - Built-in validation with quality checks
+  - Ground-truth verification from live environments
+* - **Rewards**
+  - Validation-based scoring
+  - Reward signals from genuine environment feedback
+* - **Best For**
+  - Generating diverse post-training datasets at scale, creating data with specific statistical properties, rapid prototyping
+  - Collecting RL rollouts with verified rewards, training agents on real APIs or code execution
+```
+
+### When to Use Each Tool
+
+| Use Case | Recommended Tool |
+|----------|------------------|
+| Generate diverse SFT data with controlled distributions | Data Designer |
+| Simulate tool calling patterns for initial training | Data Designer |
+| Collect RL rollouts with real reward signals | Gym |
+| Execute actual tools (code, APIs, search) during generation | Gym |
+| Create datasets with statistical diversity guarantees | Data Designer |
+| Train agents on verified real-world interactions | Gym |
+| Rapid prototyping of data characteristics | Data Designer |
+| Ground-truth verification from live environments | Gym |
+
+### Complementary Workflows
+
+Data Designer and Gym complement each other in a typical training pipeline:
+
+1. **Bootstrap with Data Designer**: Generate initial synthetic datasets for supervised fine-tuning (SFT). Use statistical samplers to ensure diversity and LLM columns to create realistic tool-calling patterns.
+
+2. **Refine with Gym**: Transition to Gym for reinforcement learning. Collect rollouts from real environment interactions where tool calls execute against actual systems and verification produces ground-truth rewards.
+
+3. **Iterate**: Use insights from Gym rollouts to refine Data Designer configurations for the next training cycle.
+
+:::{seealso}
+- [NeMo Data Designer Documentation](https://nvidia-nemo.github.io/DataDesigner/)
+- [NeMo Data Designer GitHub Repository](https://github.com/NVIDIA-NeMo/DataDesigner)
+:::

docs/conf.py

@@ -84,6 +84,13 @@
 
 html_theme = "nvidia_sphinx_theme"
 html_theme_options = {
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/NVIDIA-NeMo/Gym",
+            "icon": "fa-brands fa-github",
+        }
+    ],
     "switcher": {
         "json_url": "../versions1.json",
         "version_match": release,