---
pretty_name: OpenJevData-140k
language:
- en
- zh
license: mit
size_categories:
- 100K<n<1M
task_categories:
- question-answering
tags:
- decision-making
- reasoning
- supervised-fine-tuning
- reinforcement-learning
configs:
- config_name: default
  data_files:
  - split: SFT
    path: data/SFT.parquet
  - split: RL
    path: data/RL.parquet
---

# Dataset Card for OpenJevData-140k

## Dataset Summary

**OpenJevData-140k** is a curated release of the data collection used to train [**OpenJev-4B**](https://huggingface.co/shenjunhao/OpenJev-4B). It contains **146,738 decision-making examples** across **19 task categories**, organized into **SFT** and **RL** splits.

Each example presents a state, a question, and a request-specific set of natural-language options. The data include hard answers and soft probability distributions, with candidate sets ranging from **2 to 77 options**. Languages are **English and Chinese**.

## Data Splits

The **SFT** split contains **100,345 examples** for supervised fine-tuning: **86,023 hard** and **14,322 soft** examples. The **RL** split contains **46,393 examples** for reinforcement learning: **28,667 hard** and **17,726 soft** examples. Together they provide **146,738 examples**.

## Task Distribution

![OpenJevData-140k task distribution by SFT and RL split](assets/task-distribution.png)

- **Probabilistic reasoning:** Infer outcome probabilities from uncertainty, conditional information, sampling, or noisy evidence.
- **Deductive reasoning:** Derive conclusions from stated premises, rules, or natural-language entailment.
- **Commonsense context:** Use everyday knowledge to answer a question or select a plausible continuation.
- **Knowledge QA:** Answer factual and subject-specific questions across multiple domains.
- **Policy exceptions:** Apply policies while resolving exceptions, rule precedence, and conflicting conditions.
- **Multi-hop reasoning:** Connect facts across passages, documents, or entities to reach an answer.
- **Temporal / numeric:** Apply rules involving dates, durations, numeric thresholds, or time-dependent conditions.
- **Quantitative reasoning:** Solve numerical questions involving arithmetic, word problems, financial information, or tables.
- **Intent routing:** Identify the user intent and select an appropriate tool, route, or action.
- **Evidence validation:** Check whether evidence supports a claim or whether a response meets stated semantic and format requirements.
- **Reading comprehension:** Answer questions using information stated or implied in a supplied passage.
- **Answer preference:** Choose the response that best satisfies the request among candidate answers.
- **Option logic:** Jointly interpret logical statements and dependencies distributed across the candidate options.
- **Score quality:** Assign a rating for qualities such as helpfulness, coherence, clarity, or preference strength.
- **Score rubrics:** Assign a score by applying explicit criteria for completeness, coverage, compliance, or task completion.
- **Constraint planning:** Choose an action or plan that satisfies interacting constraints and trade-offs.
- **Code / database:** Reason about code behavior or select the database query that answers a request.
- **Emotion recognition:** Identify the emotion conveyed by a piece of text.
- **Score intensity:** Rate the strength of a tone or the severity of an impact on an ordered scale.

## Dataset Creation

The source benchmarks include [ARC](https://huggingface.co/datasets/allenai/ai2_arc), [BANKING77](https://huggingface.co/datasets/PolyAI/banking77), [BoolQ](https://github.com/google-research-datasets/boolean-questions), [CodeContests](https://github.com/google-deepmind/code_contests), [CommonsenseQA](https://www.tau-nlp.sites.tau.ac.il/commonsenseqa), [ConditionalQA](https://github.com/haitian-sun/ConditionalQA), [ContractNLI](https://stanfordnlp.github.io/contract-nli/), [FinQA](https://github.com/czyssrs/FinQA), [GoEmotions](https://github.com/google-research/google-research/tree/master/goemotions), [HellaSwag](https://rowanzellers.com/hellaswag/), [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2), [HoVer](https://hover-nlp.github.io/), [LogiQA 2.0](https://github.com/csitfun/LogiQA2.0), [MASSIVE](https://huggingface.co/datasets/AmazonScience/massive), [MathQA](https://math-qa.github.io/), [MMLU auxiliary data](https://huggingface.co/datasets/cais/mmlu), [MultiDoc2Dial](https://doc2dial.github.io/multidoc2dial/), [MuSiQue](https://github.com/StonyBrookNLP/musique), [OpenBookQA](https://huggingface.co/datasets/allenai/openbookqa), [OR-ShARC](https://github.com/Yifan-Gao/open_retrieval_conversational_machine_reading), [PRM800K](https://github.com/openai/prm800k), [QASC](https://huggingface.co/datasets/allenai/qasc), [RAGTruth](https://github.com/ParticleMedia/RAGTruth), [RuleTaker](https://github.com/allenai/ruletaker), [SNLI](https://nlp.stanford.edu/projects/snli/), [Spider](https://yale-lily.github.io/spider), [TAT-QA](https://github.com/NExTplusplus/TAT-QA), [TempReason](https://github.com/DAMO-NLP-SG/TempReason), [TimeQA](https://github.com/wenhuchen/Time-Sensitive-QA), [ToolBench](https://github.com/OpenBMB/ToolBench), [WANLI](https://github.com/alisawuffles/wanli), and [When2Call](https://huggingface.co/datasets/nvidia/When2Call), together with [instruction-following constraint datasets](https://huggingface.co/datasets/allenai/IF_multi_constraints_upto5) and human-annotated ratings. These sources are converted into a shared decision format and complemented by program-generated tasks and LLM-assisted synthesis. Labels come from source annotations, executable checks, exact probability calculations, or reviewed judgments, depending on the task.

Synthetic data generation uses the **input token-length characteristics of JevBench Public** as a reference to broaden input-length coverage. The collection includes both short, straightforward questions and longer, more demanding decision problems. The harder tasks include multi-step reasoning, interacting rules and exceptions, cross-option logic, temporal and quantitative constraints, and decisions under uncertainty.

## Data Format

Each example contains the following fields:

- `state`: context, evidence, rules, or a scenario.
- `question`: the requested decision.
- `options`: an ordered list of `{id, name, criteria}` objects. Nullable `criteria` may contain essential conditions and is part of the model input.
- `category`: the broad task category.
- `task`: `choice`, `score`, or `noul`; each uses the supplied candidate options.
- `score_values`: numeric values aligned with the options for scoring tasks, otherwise null.
- `answer`: `{kind, probabilities}`, where `kind` is `hard` or `soft`. The probabilities align by position with `options` and sum to one. A hard answer is represented by a one-hot distribution.

## Loading the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("shenjunhao/OpenJevData-140k")
sft = dataset["SFT"]
rl = dataset["RL"]

print(len(sft), len(rl))  # 100345 46393
example = sft[0]
options = example["options"]
gold_probabilities = example["answer"]["probabilities"]
```

## Licensing Information

**MIT** applies to the original contributions in this release. Adapted source material retains its upstream license and attribution requirements; see [SOURCE_NOTICES.md](SOURCE_NOTICES.md).