diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dd8f5ed44d3fde6945661517652f740954ff5c25 --- /dev/null +++ b/README.md @@ -0,0 +1,20 @@ +--- +license: apache-2.0 +language: +- en +configs: +- config_name: default + data_files: + - split: train + path: data/train-*.parquet +--- + +# dp_removed_OpenR1-Math-220k + +Derived from [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k), default/train at `e4e141ec9dea9f8326f4d347be56105859b2bd68`. Original source card: [source_card.md](source_card.md). + +15172 problems, 27739 retained traces. One row per problem; `generations` contains ALL retained traces. Parallel arrays refer to those traces; `generation_indices` records original positions. Text is unchanged. Only math-verify-correct traces are included. Explicit length stops and incomplete reasoning are excluded. Missing finish reason policy: `drop`; fallback does not prove the final answer was untruncated and is identified in `completion_evidence`. + +Length <= 20480 using `Qwen/Qwen2.5-3B` at `3aab1f1954e9cc14eb9509a215f9e5ca08227a9b`: tokens(problem) + tokens(generation), without special tokens, chat formatting, padding or truncation. Training templates add overhead. + +Train/eval overlap removal: normalization v2, distinct 8-grams, coverage >= 0.7 per evaluation problem. Short eval problems use normalized exact substring matching. References: AIME 2024/2025/2026, AMC23, MATH-500; exact pins in provenance.json. No within/between-training deduplication or blanket source-label exclusions. 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"test/number_theory/239.json", + "test/prealgebra/1203.json", + "test/intermediate_algebra/1365.json", + "test/intermediate_algebra/1350.json", + "test/prealgebra/1128.json", + "test/precalculus/356.json", + "test/algebra/2779.json", + "test/intermediate_algebra/1930.json", + "test/intermediate_algebra/1981.json", + "test/intermediate_algebra/1232.json", + "test/geometry/561.json", + "test/intermediate_algebra/1508.json", + "test/geometry/615.json" + ], + "content_digest": "b679c245a9ffb69377c4b96676e86d8e26f155dec8cd3f9106e74aaf26534c7e" + } + } + }, + "code_digest": "e8ac3ade13f0f76c9ca3c422ca649c245a6fb1da0d65c725847e1959abced3c6" + }, + "source_revision": "e4e141ec9dea9f8326f4d347be56105859b2bd68", + "tokenizer_revision": "3aab1f1954e9cc14eb9509a215f9e5ca08227a9b", + "signature": "db41f1663b0211f70611b2edc6c5af78357493fc33736403dc54c9f527d9b0b2" +} diff --git a/source_card.md b/source_card.md new file mode 100644 index 0000000000000000000000000000000000000000..4385da6d9e00a1b9b0633680f85cfdc4a4eae922 --- /dev/null +++ b/source_card.md @@ -0,0 +1,171 @@ +--- +license: apache-2.0 +language: +- en +configs: +- config_name: all + data_files: + - split: train + path: all/train-* +- config_name: default + data_files: + - split: train + path: data/train-* +- config_name: extended + data_files: + - split: train + path: extended/train-* +dataset_info: +- config_name: all + features: + - name: problem + dtype: string + - name: solution + dtype: string + - name: answer + dtype: string + - name: problem_type + dtype: string + - name: question_type + dtype: string + - name: source + dtype: string + - name: uuid + dtype: string + - name: is_reasoning_complete + sequence: bool + - name: generations + sequence: string + - name: correctness_math_verify + sequence: bool + - name: correctness_llama + sequence: bool + - name: finish_reasons + sequence: string + - name: correctness_count + dtype: int64 + - name: messages + list: + - name: content + dtype: string + - name: role + dtype: string + splits: + - name: train + num_bytes: 9734110026.0 + num_examples: 225129 + download_size: 4221672067 + dataset_size: 9734110026.0 +- config_name: default + features: + - name: problem + dtype: string + - name: solution + dtype: string + - name: answer + dtype: string + - name: problem_type + dtype: string + - name: question_type + dtype: string + - name: source + dtype: string + - name: uuid + dtype: string + - name: is_reasoning_complete + sequence: bool + - name: generations + sequence: string + - name: correctness_math_verify + sequence: bool + - name: correctness_llama + sequence: bool + - name: finish_reasons + sequence: string + - name: correctness_count + dtype: int64 + - name: messages + list: + - name: content + dtype: string + - name: role + dtype: string + splits: + - name: train + num_bytes: 4964543659 + num_examples: 93733 + download_size: 2149897914 + dataset_size: 4964543659 +- config_name: extended + features: + - name: problem + dtype: string + - name: solution + dtype: string + - name: answer + dtype: string + - name: problem_type + dtype: string + - name: question_type + dtype: string + - name: source + dtype: string + - name: uuid + dtype: string + - name: is_reasoning_complete + sequence: bool + - name: generations + sequence: string + - name: correctness_math_verify + sequence: bool + - name: correctness_llama + sequence: bool + - name: finish_reasons + sequence: string + - name: correctness_count + dtype: int64 + - name: messages + list: + - name: content + dtype: string + - name: role + dtype: string + splits: + - name: train + num_bytes: 4769566550 + num_examples: 131396 + download_size: 2063936457 + dataset_size: 4769566550 +--- + + +# OpenR1-Math-220k + +## Dataset description +OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) for problems from NuminaMath 1.5. +The traces were verified using [Math Verify](https://github.com/huggingface/Math-Verify) for most samples and [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer. + +The dataset consists of two splits: +- `default` with 94k problems and that achieves the best performance after SFT. +- `extended` with 131k samples where we add data sources like `cn_k12`. This provides more reasoning traces, but we found that the performance after SFT to be lower than the `default` subset, likely because the questions from `cn_k12` are less difficult than other sources. + +You can load the dataset as follows: + +```python +from datasets import load_dataset + +ds = load_dataset("open-r1/OpenR1-Math-220k", "default") +``` + +## Dataset curation +To build OpenR1-Math-220k, we prompt [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) model to generate solutions for 400k problems from [NuminaMath 1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5) using [SGLang](https://github.com/sgl-project/sglang), the generation code is available [here](https://github.com/huggingface/open-r1/tree/main/slurm). We follow the model card’s recommended generation parameters and prepend the following instruction to the user prompt: + +`"Please reason step by step, and put your final answer within \boxed{}."` + +We set a 16k token limit per generation, as our analysis showed that only 75% of problems could be solved in under 8k tokens, and most of the remaining problems required the full 16k tokens. We were able to generate 25 solutions per hour per H100, enabling us to generate 300k problem solutions per day on 512 H100s. + +We generate two solutions per problem—and in some cases, four—to provide flexibility in filtering and training. This approach allows for rejection sampling, similar to DeepSeek R1’s methodology, and also makes the dataset suitable for preference optimisation methods like DPO. + +## License +The dataset is licensed under Apache 2.0 +