docs(tutorials): rewrite index as table with Colab badge column

docs/source/tutorials/index.md

@@ -1,21 +1,25 @@
 # Tutorials
 
-Welcome to the OpenEnv tutorials! These guides will help you get started with using and building environments with OpenEnv.
-
-## Getting Started
-
-If you're new to OpenEnv, we recommend starting with the [Getting Started](/auto_getting_started/index) series to understand the core concepts and basic usage patterns.
-
-## Available Tutorials
-
-- **[OpenEnv Tutorial](openenv-tutorial.md)** - A comprehensive introduction to OpenEnv, covering installation, basic usage, and core concepts.
-- **[Wordle GRPO Training](wordle-grpo.md)** - Learn how to train an agent to play Wordle using Group Relative Policy Optimization (GRPO).
-- **[RL Training with 2048](rl-training-2048.md)** - Train a language model to play 2048 using GRPO reinforcement learning. *(GPU Required)*
+| Tutorial | What it covers | GPU | Notebook |
+|----------|---------------|-----|----------|
+| [OpenEnv Tutorial](openenv-tutorial.md) | Full introduction to OpenEnv: install, connect to a hosted environment, step through an episode, define a reward function, and run a basic training loop. Start here if you're new. | No | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/OpenEnv_Tutorial.ipynb) |
+| [Wordle GRPO](wordle-grpo.md) | Train an agent to play Wordle using GRPO via TRL's `environment_factory`. Shows the multi-turn tool-calling loop: the model guesses a word each turn and receives letter-position feedback until it wins or the episode ends. | Yes | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/trl/blob/main/examples/notebooks/openenv_wordle_grpo.ipynb) |
+| [RL Training with 2048](rl-training-2048.md) | Train a language model to play the 2048 tile-sliding game with GRPO. Covers game-state representation and reward shaping for a puzzle environment. | Yes | — |
+| [End-to-end walkthrough](end-to-end-walkthrough.md) | The full pipeline in one page: connect to `reasoning_gym`, wire it into TRL via `environment_factory`, fine-tune with GRPO on `chain_sum`, read the reward delta from training logs, and push the checkpoint to the Hub. | Yes | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/end_to_end_walkthrough.ipynb) |
+| [SFT warm-up for GRPO](sft-warmup.md) | When a base model's initial reward is near zero, use SFT to bootstrap format compliance before starting RL. Covers: collecting teacher rollouts with `CollectRunner`, filtering by reward, training with `SFTTrainer`, and measuring the before/after delta. | Yes | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/sft_warmup.ipynb) |
+| [Rubrics: composable reward computation](rubrics.md) | Build multi-criteria reward functions from reusable primitives (`WeightedSum`, `LLMJudge`, `Gate`). Shows how to compose, weight, and introspect rubrics inside an environment's `step()` method. | No | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/rubrics.ipynb) |
+| [MCP tools in OpenEnv environments](mcp-environment.md) | How to use the Model Context Protocol (MCP) tool interface across training, offline eval, and external serving. Covers `ListToolsAction`, `CallToolAction`, and the MCP lifecycle from reset to close. | No | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/mcp_environment.ipynb) |
+| [Evaluating agents with Inspect AI](evaluation-inspect.md) | Wrap an OpenEnv environment in an Inspect AI `Task` (dataset + solver + scorer), run it via `InspectAIHarness`, and get a structured `EvalResult` with accuracy scores. No training required — useful as a standalone eval pass on any checkpoint. | No | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/evaluation_inspect.ipynb) |
 
 ```{toctree}
-:maxdepth: 2
+:maxdepth: 1
 :hidden:
 openenv-tutorial
 wordle-grpo
 rl-training-2048
+end-to-end-walkthrough
+sft-warmup
+rubrics
+mcp-environment
+evaluation-inspect
 ```