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
(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.
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.
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>")