tvp4

Dataset summary Mixture-of-Thoughts is a curated dataset of 350k verified reasoning traces distilled from DeepSeek-R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. It was used in the Open R1 project to train OpenR1-Distill-7B, an SFT model that replicates the reasoning capabilities of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B from the same base model. To load the dataset, run: Dataset composition Mixture-of-Thoughts is composed of three domains: math, code, and science. Each domain contains reasoning traces that are designed to teach language models to reason step-by-step. The dataset is structured as follows: **math**: 93.7k reasoning traces for mathematical problems, sourced from the `default` subset of open-r1/OpenR1-Math-220k **code**: 83.1k reasoning traces for competitive programming problems in Python and C++, sourced from the `solutions` and `solutions_w_editorials` subsets of open-r1/codeforces-cots **science**: 173k reasoning traces for scientific problems, sourced from the `science` subset of nvidia/Llama-Nemotron-Post-Training-Dataset **all**: Contains all reasoning traces from the three domains, for a total of 350k traces. Curation methodology To optimise the data mixture, we followed the same methodology described in the Phi-4-reasoning tech report, namely that mixtures can be optimised independently per domain, and then combined into a single dataset. For each ablation, we evaluate on AIME 2024, GPQA Diamond, and LiveCodeBench v4 every epoch and take the best performing model checkpoint. The figure below shows the results from post-training open-r1/Qwen2.5-Math-7B-RoPE-300k on each individual domain compared to the final mixture: Overall, we find that training on all domains simultaneously yields the best results. See the subsections below for more details on optimising the data mixture per domain. [!NOTE] We use LiveCodeBench v4 to accelerate evaluation during our ablation