| --- |
| pretty_name: TextCraft-Synth |
| license: mit |
| language: [en] |
| tags: [textcraft, crafting, planning, rl-environment] |
| size_categories: [1K<n<10K] |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.jsonl |
| - split: val |
| path: data/val.jsonl |
| - config_name: recipes |
| data_files: |
| - split: world |
| path: data/recipes.jsonl |
| --- |
| |
| # TextCraft-Synth |
|
|
| The 3,154 TextCraft-Synth tasks of [Recursive Agent Optimization](https://arxiv.org/abs/2605.06639) |
| (Gandhi et al., 2026), as one row per task, for `datasets.load_dataset`. The rows are platoon's task files |
| (`extensions/textcraft/src/platoon_textcraft/data/textcraft_synth_{train,val}.jsonl`, MIT) and the `recipes` config |
| is the world (`recipes.json`) of the published Harbor export [`apurvaga/textcraft-synth`](https://huggingface.co/datasets/apurvaga/textcraft-synth). |
| Content is unchanged; only the layout differs. |
|
|
| | Split | Tasks | easy | medium | hard | extreme | |
| |---|---:|---:|---:|---:|---:| |
| | train | 2,522 | 588 | 852 | 544 | 538 | |
| | val | 632 | 147 | 213 | 136 | 136 | |
|
|
| Columns (`default`): `id`, `goal`, `difficulty`, `max_depth`, `num_craft_steps`, and JSON-encoded `target_items`, |
| `initial_inventory` and `gold_trajectory` (dict fields are strings so the schema stays flat). |
| Columns (`recipes`, split `world`, one row per item): `item`, `is_base`, `depth`, `result_count`, and JSON-encoded |
| `ingredients` ({item: count}); base items have null `result_count` and `ingredients`. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| tasks = load_dataset("<org>/textcraft-synth-flat", revision="<commit>") |
| medium = tasks["train"].filter(lambda r: r["difficulty"] == "medium") # RAO's 852 training tasks |
| world = load_dataset("<org>/textcraft-synth-flat", "recipes", split="world", revision="<commit>") |
| ``` |
|
|
