Quyet V. Do

The work introduces π², a QA curation pipeline that extracts and expands tables from Wikipedia, generates complex reasoning questions from those tables plus relevant metadata with answers automatically determined and validated through dual-path code execution, and back-translates chain-of-thought solutions grounded in realistic context; supervised fine-tuning with gpt-oss-20b and Qwen3-4B-Instruct-2507 on π² yields consistent improvements across four long-context reasoning benchmarks and the alike π²-Bench, with average absolute accuracy gains of +6.25% and +3.37% respectively, and analysis indicates reasoning style contributes little while faithful reasoning patterns from back translation and grounded realistic long context are crucial.

The work introduces π², a QA curation pipeline that extracts and expands tables from Wikipedia, generates complex reasoning questions from those tables plus relevant metadata with answers automatically determined and validated through dual-path code execution, and back-translates chain-of-thought solutions grounded in realistic context; supervised fine-tuning with gpt-oss-20b and Qwen3-4B-Instruct-2507 on π² yields consistent improvements across four long-context reasoning benchmarks and the alike π²-Bench, with average absolute accuracy gains of +6.25% and +3.37% respectively, and analysis indicates reasoning style contributes little while faithful reasoning patterns from back translation and grounded realistic long context are crucial.