MindLoom: 通过组合思维模式进行前沿级推理数据合成
MindLoom: Composing Thought Modes for Frontier-Level Reasoning Data Synthesis
AI总结：
本文提出MindLoom框架，通过组合思维模式工程合成前沿级推理数据，解决了现有方法在问题难度控制和多样性方面的不足，实验表明其在多个基准测试中表现优异。
摘要：
尽管LLMs在推理方面取得了显著进展，系统性地生成前沿级推理数据仍然具有挑战性。现有合成方法往往缺乏对问题难度结构性因素的理解，导致多样性有限和难度控制不稳定。本文将推理问题的难度视为原子知识推理转换的累积，提出MindLoom框架，通过组合思维模式工程合成前沿级推理数据。给定一组具有验证解的难题，MindLoom首先将这些解分解为思维模式链，揭示每个问题的构建逻辑。然后训练一个检索模型，将问题状态匹配到兼容的思维模式，提供合成过程中引入哪些推理挑战的指导。新问题通过迭代应用检索到的思维模式到种子问题，并通过分布对齐采样来鼓励多样化的推理覆盖。最后，基于回放的判断阶段通过难度对生成的问题进行标记，并提供已判断正确的响应用于监督微调。我们在九个基准测试上评估了MindLoom，涵盖五个STEM学科和四个数学推理任务，多个模型家族和大小的模型在微调后均在报告的基准测试中表现出色。消融研究表明了每个组件的贡献，进一步分析表明MindLoom覆盖了广泛的推理模式，同时保持了有用的难度控制。我们已开源实现：https://github.com/EachSheep/MindLoom。
英文摘要：
Although LLMs have made substantial progress in reasoning, systematically producing frontier-level reasoning data remains difficult. Existing synthesis methods often have limited visibility into the structural factors that govern problem difficulty, which can result in narrow diversity and unstable difficulty control. In this work, we view the difficulty of a reasoning problem as arising from the accumulation of atomic knowledge-reasoning transformations, which we term thought modes. Building on this perspective, we propose MindLoom, a framework for synthesizing frontier-level reasoning data through compositional thought mode engineering. Given a collection of hard problems with verified solutions, MindLoom first decomposes those solutions into thought mode chains that reveal each problem's construction logic. It then trains a retrieval model that matches problem states to compatible thought modes, providing guidance on which reasoning challenges to introduce during synthesis. New problems are composed by iteratively applying retrieved thought modes to seed questions, with distribution-aligned sampling to encourage diverse reasoning coverage. Finally, a rollout-based judging stage labels generated questions by difficulty and supplies judged-correct responses for supervised fine-tuning. We evaluate MindLoom on nine benchmarks covering five STEM disciplines and four mathematical reasoning tasks across multiple model families and sizes. Models fine-tuned on MindLoom-generated data achieves favorable performances over base models, distillation, and external-data baselines across the reported benchmarks. Ablation studies indicate the contribution of each component, and further analysis suggests that MindLoom covers a broad range of reasoning patterns while maintaining useful difficulty control. We have open-sourced our implementation at https://github.com/EachSheep/MindLoom.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
