ko120/MLE-World

MLE World

The supervised fine-tuning dataset used to train MLE-World-Qwen3.5-4B, checkpoint 6685, 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

from datasets import load_dataset

dataset = load_dataset("ko120/MLE-World")
example = dataset["train"][0]

For streaming:

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. 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 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 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:

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.