Release OpenJevData-140k SFT and RL training splits

LICENSE

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+MIT License
+
+Copyright (c) 2026 OpenJev authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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 ---
+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)
+
+## Dataset Creation
+
+The source benchmarks include ARC, BANKING77, BoolQ, CodeContests, CommonsenseQA, ConditionalQA, ContractNLI, FinQA, GoEmotions, HellaSwag, HelpSteer2, HoVer, LogiQA 2.0, MASSIVE, MathQA, MMLU auxiliary data, MultiDoc2Dial, MuSiQue, OpenBookQA, OR-ShARC, PRM800K, QASC, RAGTruth, RuleTaker, SNLI, Spider, TAT-QA, TempReason, TimeQA, ToolBench, WANLI, and When2Call, together with instruction-following constraint datasets 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 tasks include adaptations of **JevBench Public** problem patterns, with difficulty aligned to its public tasks so that the collection covers both straightforward decisions and harder cases. The harder tasks include multi-step reasoning, interacting rules and exceptions, cross-option logic, temporal and quantitative constraints, and decisions under uncertainty.
+
+## Data Format
+
+The release uses the same compact format as [MMDM](https://huggingface.co/datasets/shenjunhao/mmdm):
+
+- `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).

SOURCE_NOTICES.md

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+# Source Notices
+
+OpenJevData-140k adapts source material into state/question/options form. The upstream authors retain rights to their material. The MIT license for OpenJevData-140k original contributions does not relicense third-party text. Source-specific terms and attribution remain applicable.
+
+| Source | License recorded with the source material |
+| --- | --- |
+| [ARC](https://huggingface.co/datasets/allenai/ai2_arc) | See the linked upstream distribution for applicable terms. |
+| [BANKING77](https://huggingface.co/datasets/PolyAI/banking77) | CC-BY-4.0 |
+| [BoolQ](https://github.com/google-research-datasets/boolean-questions) | CC-BY-SA-3.0 |
+| [CodeContests](https://github.com/google-deepmind/code_contests) | See the linked upstream distribution for applicable terms. |
+| [CommonsenseQA](https://www.tau-nlp.sites.tau.ac.il/commonsenseqa) | MIT (upstream dataset repository) |
+| [ContractNLI](https://stanfordnlp.github.io/contract-nli/) | See the linked upstream distribution for applicable terms. |
+| [FinQA](https://github.com/czyssrs/FinQA) | See the linked upstream distribution for applicable terms. |
+| [GoEmotions](https://github.com/google-research/google-research/tree/master/goemotions) | Apache-2.0 |
+| [HellaSwag](https://rowanzellers.com/hellaswag/) | See the linked upstream distribution for applicable terms. |
+| [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) | See the linked upstream distribution for applicable terms. |
+| [Instruction-following rubric adaptations](https://huggingface.co/datasets/allenai/IF_multi_constraints_upto5) | See the linked upstream distribution for applicable terms. |
+| [LogiQA 2.0](https://github.com/csitfun/LogiQA2.0) | CC-BY-NC-SA-4.0 |
+| [MASSIVE](https://huggingface.co/datasets/AmazonScience/massive) | CC-BY-4.0 |
+| [MathQA](https://math-qa.github.io/) | See the linked upstream distribution for applicable terms. |
+| [MMLU auxiliary data](https://huggingface.co/datasets/cais/mmlu) | See the linked upstream distribution for applicable terms. |
+| [MultiDoc2Dial](https://doc2dial.github.io/multidoc2dial/) | See the linked upstream distribution for applicable terms. |
+| [MuSiQue](https://github.com/StonyBrookNLP/musique) | See the linked upstream distribution for applicable terms. |
+| [OpenBookQA](https://huggingface.co/datasets/allenai/openbookqa) | See the linked upstream distribution for applicable terms. |
+| [OR-ShARC](https://github.com/Yifan-Gao/open_retrieval_conversational_machine_reading) | See the linked upstream distribution for applicable terms. |
+| [RuleTaker](https://github.com/allenai/ruletaker) | See the linked upstream distribution for applicable terms. |
+| [SNLI](https://nlp.stanford.edu/projects/snli/) | CC-BY-SA-4.0 |
+| [Spider](https://yale-lily.github.io/spider) | See the linked upstream distribution for applicable terms. |
+| [TAT-QA](https://github.com/NExTplusplus/TAT-QA) | See the linked upstream distribution for applicable terms. |
+| [TempReason](https://github.com/DAMO-NLP-SG/TempReason) | See the linked upstream distribution for applicable terms. |
+
+ In particular, the recorded LogiQA 2.0 terms include a noncommercial restriction; the aggregate release should not be treated as uniformly permissive third-party content.
+
+Additional source benchmarks used in this training release include ConditionalQA, HoVer, PRM800K, QASC, RAGTruth, TimeQA, ToolBench, WANLI, and When2Call. Their upstream terms apply to the adapted material. Human-annotated rating sources recorded CC-BY-4.0 terms.

data/RL.parquet

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+size 24937067

data/SFT.parquet

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+size 43143700

distribution.json

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+{
+  "dataset": "OpenJevData-140k",
+  "total": 146738,
+  "categories": [
+    "answer_preference",
+    "code_database",
+    "commonsense_context",
+    "constraint_planning",
+    "deductive_reasoning",
+    "emotion_recognition",
+    "evidence_validation",
+    "intent_routing",
+    "knowledge_qa",
+    "multi_hop",
+    "option_logic",
+    "policy_exception",
+    "probabilistic_reasoning",
+    "quantitative_reasoning",
+    "reading_comprehension",
+    "score_intensity",
+    "score_quality",
+    "score_rubric",
+    "temporal_numeric"
+  ],
+  "splits": {
+    "SFT": {
+      "count": 100345,
+      "category": {
+        "probabilistic_reasoning": 13817,
+        "knowledge_qa": 14928,
+        "deductive_reasoning": 17410,
+        "answer_preference": 2444,
+        "quantitative_reasoning": 4071,
+        "commonsense_context": 16195,
+        "reading_comprehension": 2218,
+        "intent_routing": 3840,
+        "emotion_recognition": 776,
+        "code_database": 1087,
+        "multi_hop": 5353,
+        "option_logic": 2300,
+        "temporal_numeric": 5401,
+        "constraint_planning": 1312,
+        "policy_exception": 5032,
+        "evidence_validation": 2508,
+        "score_quality": 758,
+        "score_rubric": 870,
+        "score_intensity": 25
+      },
+      "answer_kind": {
+        "soft": 14322,
+        "hard": 86023
+      },
+      "task": {
+        "choice": 98047,
+        "score": 1653,
+        "noul": 645
+      },
+      "option_count_range": [
+        2,
+        77
+      ]
+    },
+    "RL": {
+      "count": 46393,
+      "category": {
+        "intent_routing": 1149,
+        "deductive_reasoning": 5211,
+        "knowledge_qa": 4029,
+        "answer_preference": 680,
+        "quantitative_reasoning": 1153,
+        "reading_comprehension": 971,
+        "emotion_recognition": 220,
+        "commonsense_context": 4383,
+        "policy_exception": 2757,
+        "temporal_numeric": 2097,
+        "constraint_planning": 335,
+        "option_logic": 688,
+        "multi_hop": 2180,
+        "evidence_validation": 1741,
+        "probabilistic_reasoning": 15550,
+        "code_database": 141,
+        "score_quality": 1770,
+        "score_rubric": 1309,
+        "score_intensity": 29
+      },
+      "answer_kind": {
+        "hard": 28667,
+        "soft": 17726
+      },
+      "task": {
+        "choice": 43206,
+        "score": 3108,
+        "noul": 79
+      },
+      "option_count_range": [
+        2,
+        77
+      ]
+    }
+  }
+}