| --- |
| license: apache-2.0 |
| pretty_name: "OpenWorld · Coding Worlds (World-Time Compute)" |
| task_categories: |
| - text-generation |
| - other |
| tags: |
| - world-models |
| - world-time-compute |
| - code-generation |
| - program-synthesis |
| - reproducibility |
| - synthetic |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: sft_train.jsonl |
| - split: test |
| path: test_tasks.jsonl |
| - config_name: tasks |
| data_files: tasks.jsonl |
| language: |
| - en |
| --- |
| |
| # OpenWorld · Coding Worlds (World-Time Compute) |
|
|
| **A family of **verified coding worlds** — a function to implement plus a test-suite oracle — for the *world-time-compute* realism check: fine-tune on many worlds, generalize to held-out tasks (and HumanEval/MBPP).** |
|
|
| [](https://github.com/quome-cloud/openworld) |
| [](https://opensource.org/licenses/Apache-2.0) |
| [](https://github.com/quome-cloud/openworld) |
| [](#what-it-is) |
|
|
| ## Load it |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("Quome/openworld-coding") # sft train/test |
| # tasks = load_dataset("Quome/openworld-coding", "tasks") # all 219 verified tasks |
| ``` |
|
|
| --- |
|
|
| A family of **verified coding worlds** — function-implementation tasks, each a world whose |
| **oracle is its test suite** — for the world-time-compute realism check on a *different* use |
| case than diagnosis (OpenWorld experiment E77; paper §"World-time compute"). |
|
|
| ## What it is |
|
|
| Each task is a tiny verified-code world: a function to implement (prompt = signature + |
| docstring) and a set of `assert`-based unit tests that define correctness. A solution is |
| "right" iff it passes **all** tests — the cleanest possible oracle (this is the |
| HumanEval/MBPP setup). The transferable skill is *coding*; held-out tasks measure |
| generalization. |
|
|
| ## Provenance |
|
|
| Tasks are **LLM-authored** (Gemini 2.5 Flash) across 12 topics (strings, arrays, dicts, |
| math, recursion, sorting, parsing, matrices, intervals, stacks/queues, greedy, simple DP), |
| then **verified in a sandboxed subprocess** — the reference solution must pass its own tests |
| before the task is admitted (~58% of generated candidates passed verification and were |
| kept). This contrasts with the synthetic-parametric `openworld-diagnosis` family: here the |
| worlds are *authored by a model* (the "Claude-Code-style" realism check), and the oracle is |
| executable tests rather than a Bayes-optimal classifier. |
|
|
| ## Contents (JSONL) |
|
|
| | File | Rows | Schema | |
| |---|---|---| |
| | `tasks.jsonl` | 219 | `{name, topic, prompt, solution, tests[]}` (all verified) | |
| | `sft_train.jsonl` | 164 | `{prompt, completion}` — `prompt` = instruction + signature/docstring; `completion` = reference solution | |
| | `test_tasks.jsonl` | 55 | `{id, prompt, tests[], kind}` — held-out tasks for pass@k | |
|
|
| Task-level (world-level) train/test split: the 55 test tasks are held out from fine-tuning. |
|
|
| ## How to use |
|
|
| Fine-tune on `sft_train.jsonl`; evaluate **pass@1 / pass@k** on `test_tasks.jsonl` (run the |
| model's code against each task's `tests` in a sandbox). For real-benchmark transfer, also |
| evaluate on **HumanEval / MBPP** (fetched by `experiments/e77_gen.py`'s benchmark step; |
| adapters in `experiments/e77_eval.py`). |
|
|
| ## Reproduce |
|
|
| ``` |
| python experiments/e77_gen.py # author + verify tasks (needs GEMINI_API_KEY in .env) |
| python experiments/e77_data.py # split + SFT |
| ``` |
| Generation uses an LLM, so the exact task set is not bit-reproducible (unlike the seeded |
| diagnosis family); the committed `tasks.jsonl` is the canonical set used in E77. |
|
|
| ## Results (E77, paper §world-time compute) |
|
|
| `experiments/results/e77_coding.json`. Headline: world-time compute helps **in-domain |
| pass@k at every model size** (e.g. 7B pass@5 0.84→0.95) and transfers **positively to |
| HumanEval at pass@5** (7B 0.866→0.909) from just 164 worlds — though it *hurts* greedy |
| pass@1 on HumanEval. Consistent with E76's world-count law (more worlds → more gain), 164 |
| is below the threshold where transfer becomes strong. |
|
|
| ## License |
|
|
| Apache 2.0 (same as the OpenWorld repository). Tasks/tests are LLM-generated; treated as synthetic. |
|
|
|
|
| --- |
|
|
| ## From the OpenWorld project |
|
|
| This dataset is produced by **OpenWorld** — a framework for *verified symbolic world models*, where a world's dynamics are explicit, auditable Python code (no training, no GPU). "World-time compute" is the idea that **traversing many verified worlds** of a domain and fine-tuning on that experience makes a model generalize to **unseen** worlds from fewer real examples. |
|
|
| - 💻 **Code, experiments & paper:** https://github.com/quome-cloud/openworld |
|
|
| ## Citation |
|
|
| ```bibtex |
| @software{openworld_coding_2026, |
| title = {OpenWorld · Coding Worlds (World-Time Compute)}, |
| author = {Schwoebel, Jim}, |
| year = {2026}, |
| url = {https://github.com/quome-cloud/openworld}, |
| note = {Hugging Face dataset: Quome/openworld-coding} |
| } |
| ``` |
|
|
