$\pi^2$: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models

We study a pipeline that curates reasoning data from initial structured data for improving long-context reasoning in large language models (LLMs). Our approach, $\pi^2$, constructs high-quality reasoning data through rigorous QA curation: 1) extracting and expanding tables from Wikipedia, 2) from the collected tables and relevant context, generating realistic and multi-hop analytical reasoning questions whose answers are automatically determined and verified through dual-path code execution, and 3) back-translating step-by-step structured reasoning traces as solutions of QA pairs given realistic web-search context. Supervised fine-tuning with gpt-oss-20b and Qwen3-4B-Instruct-2507 on $\pi^2$ yields consistent improvements across four long-context reasoning benchmarks and our alike $\pi^2$-Bench, with average absolute accuracy gains of +4.3\% and +2.7\% respectively. Notably, our dataset facilitates self-distillation, where gpt-oss-20b even improves its average performance by +4.4\% with its own reasoning traces, demonstrating $\pi^2$'s usefulness. Our data pipeline, dataset, and finetuned models \ourGpt and \ourQwen will be fully open-sourced, enabling reproducible research and scaling.