docs: home pg, quickstart move, gh icon

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,

docs/get-started/index.md

@@ -2,28 +2,150 @@
 
 # Get Started with NeMo Gym
 
-**Estimated Time**: 25-30 minutes
+This guide helps you install NeMo Gym, run a training environment, and collect your first rollouts. Choose your path:
 
-This guided tutorial is designed for users new to training models with reinforcement learning (RL). These tutorials walk you through the complete journey from installation to generating training data at scale.
-
-**By the end of this tutorial series, you will have:**
-
-✅ A working NeMo Gym installation with servers running
-✅ The ability to generate rollouts for RL training
+- **[Quickstart](#gs-quickstart)**: Get running in under 5 minutes with a single-page walkthrough.
+- **[Detailed Setup Guide](#detailed-setup-guide)**: Step-by-step tutorials with deeper explanations of each component.
 
 ## Before You Start
 
-Make sure you have these prerequisites ready before beginning the tutorials:
+Make sure you have these prerequisites ready:
 
 - **Git** for cloning the repository
 - **OpenAI API key** with available credits (requires ~$0.01-0.10 for all tutorials)
 - Basic command-line familiarity
 
 ---
 
-## Tutorial Path
+(gs-quickstart)=
+## Quickstart
+
+### Steps
+
+Follow the tabs sequentially to install NeMo Gym, start the servers, and collect your first verified rollouts for RL training.
+
+::::{tab-set}
+
+:::{tab-item} 1. Set Up
+
+**Install NeMo Gym**
+
+Get NeMo Gym installed and ready to use:
+
+```bash
+# Clone the repository
+git clone git@github.com:NVIDIA-NeMo/Gym.git
+cd Gym
+
+# Install UV (Python package manager)
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source $HOME/.local/bin/env
+
+# Create virtual environment
+uv venv --python 3.12
+source .venv/bin/activate
+
+# Install NeMo Gym
+uv sync --extra dev --group docs
+```
+
+**Configure Your API Key**
+
+Create an `env.yaml` file that contains your OpenAI API key and the {term}`Policy Model` you want to use. Replace `your-openai-api-key` with your actual key. This file helps keep your secrets out of version control while still making them available to NeMo Gym.
+
+```bash
+echo "policy_base_url: https://api.openai.com/v1
+policy_api_key: your-openai-api-key
+policy_model_name: gpt-4.1-2025-04-14" > env.yaml
+```
+
+> **Note:** We use GPT-4.1 in this quickstart because it provides low latency (no reasoning step) and works reliably out-of-the-box. NeMo Gym is **not limited to OpenAI models**—you can use self-hosted models via vLLM or any OpenAI-compatible inference server that supports function calling. Refer to the [setup guide](setup-installation.md) for details.
+
+:::
+
+:::{tab-item} 2. Start Servers
+
+**Terminal 1** (start servers):
+
+```bash
+# Start servers (this will keep running)
+config_paths="resources_servers/example_simple_weather/configs/simple_weather.yaml,\
+responses_api_models/openai_model/configs/openai_model.yaml"
+ng_run "+config_paths=[${config_paths}]"
+```
+
+**Terminal 2** (interact with agent):
+
+```bash
+# In a NEW terminal, activate environment
+source .venv/bin/activate
+
+# Interact with your agent
+python responses_api_agents/simple_agent/client.py
+```
+
+:::
+
+:::{tab-item} 3. Collect Rollouts
+
+**Terminal 2** (keep servers running in Terminal 1):
+
+```bash
+# Create a simple dataset with one query
+echo '{"responses_create_params":{"input":[{"role":"developer","content":"You are a helpful assistant."},{"role":"user","content":"What is the weather in Seattle?"}]}}' > weather_query.jsonl
+
+# Collect verified rollouts
+ng_collect_rollouts \
+    +agent_name=simple_weather_simple_agent \
+    +input_jsonl_fpath=weather_query.jsonl \
+    +output_jsonl_fpath=weather_rollouts.jsonl
+
+# View the result
+cat weather_rollouts.jsonl | python -m json.tool
+```
+
+This generates training data with verification scores!
+
+:::
+
+:::{tab-item} 4. Clean Up Servers
+
+**Terminal 1** with the running servers: Ctrl+C to stop the `ng_run` process.
+
+:::
+::::
+
+### What's Next?
+
+Now that you can generate rollouts, choose your path:
+
+::::{grid} 1 1 2 2
+:gutter: 3
+
+:::{grid-item-card} {octicon}`package;1.5em;sd-mr-1` Use an Existing Training Environment
+:link: https://github.com/NVIDIA-NeMo/Gym#-available-resource-servers
+
+Browse the available resource servers on GitHub to find a training-ready environment that matches your goals.
++++
+{bdg-secondary}`github` {bdg-secondary}`resource-servers`
+:::
+
+:::{grid-item-card} {octicon}`tools;1.5em;sd-mr-1` Build a Custom Training Environment
+:link: ../tutorials/creating-resource-server
+:link-type: doc
+
+Implement or integrate existing tools and define task verification logic.
++++
+{bdg-secondary}`tutorial` {bdg-secondary}`custom-tools`
+:::
+
+::::
+
+---
+
+## Detailed Setup Guide
 
-Follow these tutorials in sequence to start collecting rollouts with NeMo Gym:
+For a more comprehensive setup experience, follow these tutorials in sequence:
 
 ::::{grid} 1 1 1 1
 :gutter: 3
@@ -48,11 +170,9 @@ Generate your first batch of rollouts and understand how they become training da
 
 ::::
 
----
-
 :::{tip}
 **New to reinforcement learning?** Do not worry—these tutorials introduce RL concepts naturally as you learn rollout collection.
 
 - For deeper conceptual understanding, explore the [About](../about/index.md) section.
 - For quick definitions, refer to the [Glossary](../about/concepts/key-terminology.md).
-:::
+:::

docs/get-started/rollout-collection.md

@@ -184,14 +184,18 @@ Congratulations! You now have a working NeMo Gym installation and understand how
 :::{grid-item-card} {octicon}`package;1.5em;sd-mr-1` Use an Existing Training Environment
 :link: https://github.com/NVIDIA-NeMo/Gym#-available-resource-servers
 
-Browse the available resource servers to find a training-ready environment that matches your goals.
+Browse the available resource servers on GitHub to find a training-ready environment that matches your goals.
++++
+{bdg-secondary}`github` {bdg-secondary}`resource-servers`
 :::
 
 :::{grid-item-card} {octicon}`tools;1.5em;sd-mr-1` Build a Custom Training Environment
 :link: ../tutorials/creating-resource-server
 :link-type: doc
 
 Implement or integrate existing tools and define task verification logic.
++++
+{bdg-secondary}`tutorial` {bdg-secondary}`custom-tools`
 :::
 
 ::::