ajibawa-2023/Stitched-Reasoning-Trajectories-7M

Stitched-Reasoning-Trajectories-7M是一个大规模、合成的多跳推理数据集。它通过算法将原始glaiveai/reasoning-v1-20m数据集中的离散推理轨迹缝合成连续、连贯且逻辑结构化的多智能体轨迹。该过程通过从thought块提取内部子问题，并映射高信息关键词重叠，将单轮问答对转化为深入的多步研究计划。为确保高质量并消除主题漂移，每个轨迹都使用密集语义嵌入模型（BAAI/bge-large-en-v1.5）进行了验证。最终数据集包含709个JSONL文件，涵盖超过720万条完全去重、高度连贯的推理链。数据集采用专业的两阶段算法创建，包括高信息关键词提取、子智能体种子生成、局部关键词链接（倒排索引）和语义连贯性过滤，所有流程均在双NVIDIA H100 GPU集群上优化运行。

Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically stitching together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories. By extracting internal sub-questions from `thought` blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate topic drift, every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5). The resulting dataset consists of 709 `.jsonl` files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.