---
language:
  - en
task_categories:
  - question-answering
tags:
  - procedural-reasoning
  - rule-reasoning
  - retrieval
  - synthetic-data
  - large-language-models
pretty_name: RuleWorld
size_categories:
  - 1M<n<10M

---

# RuleWorld

RuleWorld is a large-scale benchmark for evaluating whether language models can retrieve and apply a shared repository of externally provided procedural rules. Its rules are abstract, globally reusable, and intentionally non-commonsense, so a model cannot answer reliably from world knowledge alone. Each rule is supplied in aligned natural-language (NL) and first-order logic (FOL) forms.

The benchmark covers three reasoning settings: Single-Rule QA, Parallel Multi-Rule QA, and Multi-Hop QA. Parallel questions combine independent sub-questions, while multi-hop questions require sequential rule application.

<table>
  <tr>
    <td width="70%" align="center"><img src="assets/benchmark.png" alt="RuleWorld overview" width="100%"></td>
    <td width="30%" align="center"><img src="assets/qa_type_statistics_with_rules.png" alt="QA and rule statistics" width="100%"></td>
  </tr>
</table>

## Dataset summary


| Resource                | Records   | Description                                                                        |
| ----------------------- | --------- | ---------------------------------------------------------------------------------- |
| Rule library            | 4,936,113 | Globally shared procedural rules, each in NL and FOL forms.                        |
| Full QA collection      | 3,377,737 | 50,000 Single-Rule, 3,250,000 Parallel Multi-Rule, and 77,737 Multi-Hop instances. |
| Training split          | 111,200   | 25,000 Single-Rule, 49,200 Parallel Multi-Rule, and 37,000 Multi-Hop instances.    |
| Development split       | 3,390     | Development instances.                                                             |
| Official evaluation set | 550       | The evaluation split: 550 instances.                                                |


The full QA collection contains eleven fine-grained subtasks: `single-rule`, `multi-rule-2` through `multi-rule-8`, and `multi-hop-2` through `multi-hop-4`. For Multi-Rule QA, the numeric suffix denotes the number of distinct gold rules (with a minimum label of 2); for Multi-Hop QA, it denotes the reasoning depth.

## Repository layout

```text
all_data/
  rule_library.txt                 # Full rule library; recommended rule source
  rule_library.pkl                 # Serialized generation-time representation
  single_qa.jsonl                  # 50,000 Single-Rule QA instances
  multi_qa_base.jsonl              # 30,000 base Multi-Rule QA instances
  multi_qa_generated_{2,3,4}.jsonl # 3,220,000 composed Parallel Multi-Rule instances
  multi_hop_qa.jsonl               # 77,737 Multi-Hop QA instances
sampled/
  train_111200.jsonl               # Training split
  dev_3390.jsonl                   # Development split
verified/
  test_550.jsonl                   # Official balanced evaluation set
```

`all_data/rule_library.txt` is tab-separated with the columns `rule_id`, `natural_language_rule`, `fol_rule`, and `rule_type`. Use this text file rather than the pickle file when portability and security matter.

## QA format

Every QA instance is a JSON object. Core fields are:


| Field        | Description                                                                           |
| ------------ | ------------------------------------------------------------------------------------- |
| `id`         | Unique QA identifier.                                                                 |
| `Q`          | Natural-language question.                                                            |
| `A`          | Gold answer list, ordered to match the question or its sub-questions.                 |
| `Q_template` | Prompt template containing the `{rules}` and `{question}` placeholders.               |
| `A_template` | Reference answer template. After formatting, the answer is enclosed in `\boxed{...}`. |
| `proofs`     | Gold derivation steps. Integer entries are rule IDs from the rule library.            |
| `rule_info`  | Structured gold rule metadata used for retrieval supervision.                         |
| `task_type`  | One of `single_rule`, `multi_rule`, or `multi_hop`.                                   |
| `qa_type`    | Underlying rule subtype(s) involved in the instance.                                  |


`multi_rule` records additionally contain `rule_width`, and `multi_hop` records contain `rule_depth`. Composed Multi-Rule records include `source_ids`, which preserve the order of their component QA instances. The official evaluation set also stores `final_qa_type`, `selection_seed`, and `subtask`.

## Citation

If you find this dataset helpful in your research, we would kindly appreciate a citation:

```bibtex
@misc{yu2026factualknowledgebenchmarkinglearning,
      title={Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models}, 
      author={Bohan Yu and Pengfei Cao and Chen Han and Chenxi Zhou and Zhiheng Zhang and Zhiyang Xie and Wenhao Teng and Xiangwen Liao and Jun Zhao and Kang Liu},
      year={2026},
      eprint={2608.22753},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2608.22753}, 
}
```