arXiv AI By Cy Xie

FormulaSPIN: Self-Play Fine-Tuning for Natural Language to Spreadsheet Formula Generation

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arXiv:2607. 19354v1 Announce Type: new Abstract: Spreadsheet applications are used by hundreds of millions worldwide, yet writing formulas remains a significant barrier.

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arXiv AI
Sep 12

Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models

The paper introduces SOLID, a framework that enables operations research language models to self-improve without relying on verified answers or external evaluators. SOLID uses solver-generated artifacts from the model’s own rollouts to create pseudo-references, clustering objectives and applying group-relative advantages for dense self-supervision. Experiments on multiple OR benchmarks show that SOLID enhances solution accuracy for both general-purpose and OR-tuned models compared to outcome-only training.

By Rui Zhu, Minglong Cao, Chenyu Zhou, Jianghao Lin, Dongdong Ge
arXiv Computation and Language
Aug 25

Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models

arXiv:2608.22753v1 Announce Type: new Abstract: Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided proced...

By Bohan Yu, Pengfei Cao, Chen Han, Chenxi Zhou, Zhiheng Zhang, Zhiyang Xie, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu
arXiv AI
3d ago

Prompt2Skill: Unsupervised Skill Optimization From Natural Language Instructions

Prompt2Skill is an unsupervised framework that constructs skills for Large Language Models directly from natural‑language task descriptions. It automatically derives task specifications, discovers or synthesizes datasets, and refines the skill through a reflective editing loop. In experiments across question answering, reading comprehension, spreadsheet manipulation, and mathematical reasoning, Prompt2Skill outperforms direct prompting, improving performance by an average of 10.8 points on both open‑source and frontier models.

By Bo Ni, Li Li, Ryan A. Rossi, Franck Dernoncourt, Tyler Derr