Release OpenThink (43,101 short self-consistency-checked reasoning traces)

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/openthink.jsonl filter=lfs diff=lfs merge=lfs -text
+data/openthink_hybrid.jsonl filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

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+---
+license: apache-2.0
+language:
+- en
+task_categories:
+- text-generation
+- question-answering
+tags:
+- reasoning
+- chain-of-thought
+- distillation
+- synthetic
+- small-language-models
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/openthink.jsonl
+- config_name: hybrid
+  data_files:
+  - split: train
+    path: data/openthink_hybrid.jsonl
+---
+
+# OpenThink
+
+OpenThink is 43,101 **short** reasoning traces for fine-tuning small language models (around 100M parameters). Most reasoning datasets are written for large models and run to thousands of tokens; OpenThink's median trace is **116 tokens**, short enough for a sub-150M model to learn from and reproduce.
+
+Every trace is **self-consistency checked**: the solver answered each question twice, independently, and only questions where both answers agree are kept.
+
+## Example
+
+```json
+{"category": "deduction", "subtopic": "before/after ordering", "solver": "mimo-v2.6-flash",
+ "question": "At a market, Diego buys bread at 8:15, sells a hat at 8:40, buys fruit after selling the hat, and sells a vase before buying the bread. Which transaction happened third?",
+ "trace": "The timeline of transactions:\n\n1. Sells a vase (must be before 8:15)\n2. Buys bread at 8:15\n3. Sells a hat at 8:40\n4. Buys fruit (must be after 8:40)\n\nThe third transaction is selling a hat at 8:40.\n\nFinal answer: Selling a hat",
+ "answer": "selling a hat", "agree": true, "both_finals": ["selling hat", "selling a hat"], "out_tok": 153}
+```
+
+Every trace ends with a `Final answer:` line.
+
+## Contents
+
+| Category | Rows |
+|---|---:|
+| quantitative | 9,623 |
+| everyday-logic | 8,734 |
+| code-trace | 7,742 |
+| deduction | 6,404 |
+| cause-effect | 5,656 |
+| reading-inference | 4,942 |
+| **total** | **43,101** |
+
+| Field | Meaning |
+|---|---|
+| `category`, `subtopic` | cell of the generation grid the question came from |
+| `question` | the question, written by the question model |
+| `trace` | the solver's reasoning, ending in `Final answer: ...` |
+| `answer` | the normalized final answer |
+| `both_finals` | the final answers of the two independent solves |
+| `agree` | always `true` (rows that disagreed were dropped) |
+| `solver`, `out_tok` | solver model and the trace's output token count |
+
+### Configs
+
+- **`default`** (`data/openthink.jsonl`): every row with its full reasoning trace.
+- **`hybrid`** (`data/openthink_hybrid.jsonl`): the same 43,101 questions, but 10,022 rows (almost all cause-effect and reading-inference) keep only the `Final answer:` line. This is the recipe used for [Boris-2-Think](https://huggingface.co/opencerebral/Boris-2-Think).
+
+## How it was made
+
+1. **Question.** GLM-5.3-flash writes one checkable question from a grid of six categories and many subtopics.
+2. **Solve twice.** MiMo-v2.6-flash solves it two times independently, concisely (at most about 120 words), ending with a `Final answer:` line.
+3. **Self-consistency filter.** Keep only questions where both final answers match after normalization (times, numbers, and prefix or substring equality, since an exact string match wrongly dropped about 23% of good pairs). About 74% of questions pass.
+4. **Deduplicate.** Near-duplicate templates (same content words, digits removed) are collapsed.
+5. **Decontaminate.** Every question was 13-gram checked against the evaluation splits of ARC-Easy, ARC-Challenge, HellaSwag, PIQA, SciQ, OpenBookQA and WinoGrande. Nothing was flagged. It was not checked against arithmetic benchmarks such as ArithMark-3 or GSM8K.
+
+Agreement between two solves is a filter, not a proof: some rows will still have wrong answers, especially in the open-ended cause-effect and reading-inference categories.
+
+## Results
+
+Fine-tuning on OpenThink measurably helps small models. Fine-tuning the 144M-parameter [Boris-2](https://huggingface.co/opencerebral/Boris-2) on the hybrid config (2 epochs, about 5 minutes on one V100) gave [Boris-2-Think](https://huggingface.co/opencerebral/Boris-2-Think):
+
+| Benchmark | Boris-2 | Boris-2-Think |
+|---|---:|---:|
+| ARC-Easy | 59.76 | 63.93 |
+| ARC-Challenge | 32.00 | 34.81 |
+| ArithMark-3 | 45.10 | 49.20 |
+| SciQ | 85.10 | 87.10 |
+| Open SLM Index | 34.15 | 36.05 |
+
+An earlier ablation on Boris-2-Preview-2 (two seeds each) separated the effect of the traces from the effect of the questions and answers. Answer-only fine-tuning gave +2.1 Index and full traces +3.0. The traces specifically helped arithmetic (ArithMark-3 +4.3 over answer-only) and slightly hurt HellaSwag (−0.5), which is why the hybrid recipe drops the traces for commonsense-style categories.
+
+## Credits
+
+Built by [OpenCerebral](https://huggingface.co/opencerebral). The generation API credits were donated by **DQN Labs**; thank you.
+
+## License
+
+Apache 2.0.
+
+---
+Action Automated by Claude Code

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