| --- |
| language: |
| - en |
| pretty_name: MLE World |
| task_categories: |
| - text-generation |
| size_categories: |
| - 100K<n<1M |
| tags: |
| - code |
| - execution-simulation |
| - world-model |
| - mle-bench |
| - sharegpt |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: validation |
| path: data/validation-*.parquet |
| --- |
| |
| # MLE World |
|
|
| The supervised fine-tuning dataset used to train [MLE-World-Qwen3.5-4B, checkpoint 6685](https://huggingface.co/ko120/MLE-World-Qwen3.5-4B), the execution world model used in the Qwen3.5-9B stage-policy experiments recorded in `stage_policy_qwen35_9b_seven_arm_8h_plan_20261011.md`. |
|
|
| This release exports `world_final_low_9_3_exc_token_h100`, built on September 9, 2026. It contains **497,056 training examples** and **1,700 validation examples**, with 100 held-out examples per task across 17 MLE-bench tasks. The validation split is the original `holdout.sharegpt.jsonl`. |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("ko120/MLE-World") |
| example = dataset["train"][0] |
| ``` |
|
|
| For streaming: |
|
|
| ```python |
| train = load_dataset("ko120/MLE-World", split="train", streaming=True) |
| ``` |
|
|
| ## Format |
|
|
| Each example preserves the original ShareGPT columns: |
|
|
| - `system`: the execution-prediction instructions. |
| - `conversations`: messages with `from` and `value` fields. `human` supplies the candidate program and execution context; `gpt` supplies the observed outcome. |
|
|
| The input contains `<code>`, `<environment>`, `<data_overview>`, and `<runtime_budget>`. The output contains, in order, `<exc_type>`, `<term_out>`, `<val_metric>`, and `<submission_exists>`. |
|
|
| The prompt format is `env_v1_reorder_excfirst_token`. The 82 ordinary exception tokens, including `EXC_None` and `EXC_TimeoutError`, are defined in [exception_tokens.json](exception_tokens.json). These are registered as individual, non-special tokens in the matching model tokenizer. |
|
|
| ## Provenance and fidelity |
|
|
| The Parquet shards preserve every parsed field, example, and row order from the original training and holdout JSONL files. The export verifies all rows after reading them back from Parquet. [provenance.json](provenance.json) records source-file SHA-256 hashes, row counts, canonical row hashes, and individual shard hashes. |
|
|
| The corpus was built from real program execution traces, filtered and deduplicated before the train/holdout split. It includes draft, debug, and improve examples from multiple code-generation models. Generated programs and observed outputs are included; the original competition datasets are not bundled. |
|
|
| [metadata/holdout.nodes.jsonl](metadata/holdout.nodes.jsonl) retains the original holdout evaluation records, including task/stage identifiers and execution labels. It is separate from the conversational training schema. |
|
|
| ## Use with LlamaFactory |
|
|
| Download the dataset repository and restore the original JSONL filenames: |
|
|
| ```bash |
| hf download ko120/MLE-World --repo-type dataset --local-dir ./mle-world-data |
| python ./mle-world-data/export_jsonl.py --output-dir ./training_data |
| ``` |
|
|
| The helper requires `pyarrow`. It writes `train.jsonl`, `holdout.sharegpt.jsonl`, the original `dataset_info.json`, and the exception vocabulary into the output directory. JSON serialization whitespace may differ; field values and row order are preserved. |
|
|
| The matching model repository includes the original training configuration. Set its `dataset_dir` to the restored output directory when reproducing training. |
|
|
