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Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:04
摘要
Tasks such as mathematical problem solving and coding require models to leverage chain-of-thought (CoT) processes, enabling human-like reasoning strategies. However, the advancement of large reasoning models (LRMs) is hindered by the lack of comprehensive CoT datasets. Existing resources often fail to provide extensive reasoning problems with coherent CoT processes distilled from multiple teacher models, and do not account for multifaceted properties describing the internal characteristics of CoTs.To address these challenges, we introduce OmniThought, a large-scale dataset featuring 2 million CoT processes generated and validated by multiple powerful LRMs. Each CoT process in OmniThought is annotated with novel Reasoning Verbosity (RV) and Cognitive Difficulty (CD) scores, which characterize the appropriateness of CoT verbosity and the cognitive difficulty level for models to comprehend these reasoning processes. We further establish a self-reliant pipeline to curate this dataset. Extensive experiments using Qwen2.5 and Qwen3 of various sizes demonstrate the positive impact of our RV and CD scores on LRM training effectiveness. Based on the OmniThought dataset, we train and release a series of high-performing LRMs with enhanced reasoning abilities and optimized CoT output length. Our contributions advance the development of LRMs across different scales for solving complex reasoning tasks.
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记录 #1 · 2026-07 · 最新观测版本
Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
Tasks such as mathematical problem solving and coding require models to leverage chain-of-thought (CoT) processes, enabling human-like reasoning strategies. However, the advancement of large reasoning models (LRMs) is hindered by the lack of comprehensive CoT datasets. Existing resources often fail to provide extensive reasoning problems with coherent CoT processes distilled from multiple teacher models, and do not account for multifaceted properties describing the internal characteristics of CoTs.To address these challenges, we introduce OmniThought, a large-scale dataset featuring 2 million CoT processes generated and validated by multiple powerful LRMs. Each CoT process in OmniThought is annotated with novel Reasoning Verbosity (RV) and Cognitive Difficulty (CD) scores, which characterize the appropriateness of CoT verbosity and the cognitive difficulty level for models to comprehend these reasoning processes. We further establish a self-reliant pipeline to curate this dataset. Extensive experiments using Qwen2.5 and Qwen3 of various sizes demonstrate the positive impact of our RV and CD scores on LRM training effectiveness. Based on the OmniThought dataset, we train and release a series of high-performing LRMs with enhanced reasoning abilities and optimized CoT output length. Our contributions advance the development of LRMs across different scales for solving complex reasoning tasks.
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