Add sft notebooks (#570)

Author: Kovbo

README.md

@@ -70,6 +70,9 @@ ART is an open-source RL framework that improves agent reliability by allowing L
 | **Tic Tac Toe**     | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/tic_tac_toe/tic-tac-toe.ipynb)     | Qwen 2.5 3B learns to play Tic Tac Toe              | <img src="https://github.com/openpipe/art/raw/main/assets/benchmarks/tic-tac-toe-local/accuracy-training-progress.svg" height="72"> [benchmarks](/examples/tic_tac_toe/display-benchmarks.ipynb)                            |
 | **Codenames**       | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/codenames/Codenames_RL.ipynb)      | Qwen 2.5 3B learns to play Codenames                | <img src="https://github.com/openpipe/art/raw/main/assets/benchmarks/codenames/win_rate_over_time.png" height="72"> [benchmarks](https://github.com/OpenPipe/art-notebooks/blob/main/examples/codenames/Codenames_RL.ipynb) |
 | **AutoRL [RULER]**  | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/auto_rl.ipynb)                     | Train Qwen 2.5 7B to master any task                | [Link coming soon]                                                                                                                                                                                                          |
+| **Distillation (SFT)** | [🏋️ Train model](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/sft/distillation.ipynb)         | Distill text-to-SQL from Qwen 3 235B to Qwen 3 30B  | [Link coming soon]                                                                                                                                                                                                          |
+| **Summarizer (SFT + RL)** | [🏋️ Train model](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/sft/sft-rl.ipynb)            | Train a document summarizer with SFT warmup then RL | [Link coming soon]                                                                                                                                                                                                          |
+| **SFT from a dataset** | [🏋️ Train model](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/sft/train_from_file.ipynb)      | Fine-tune Qwen 3 30B on text-to-SQL from a dataset  | [Link coming soon]                                                                                                                                                                                                          |
 
 ## 📰 ART News