@inproceedings{bsharat-shen-2026-prompting,
    title = "Prompting Test-Time Scaling Is A Strong {LLM} Reasoning Data Augmentation",
    author = "Bsharat, Sondos Mahmoud  and
      Shen, Zhiqiang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.474/",
    pages = "9752--9776",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) exhibit strong reasoning when guided by chain-of-thought exemplars, yet collecting large, high-quality reasoning datasets remains laborious and resource-intensive. We introduce Prompting Test-Time Scaling (P-TTS), a prompt-space data augmentation framework for enhancing LLM reasoning via fine-tuning. In P-TTS, scaling refers to systematic expansion of the prompt space during offline teacher-data generation, not to increased inference-time compute for the deployed student. Rather than collecting thousands of examples, P-TTS starts from a small pool of 90 manually selected reasoning instances and applies principled instruction templates and paraphrased prompt variants to elicit diverse reasoning trajectories from a teacher model, producing a compact synthetic training set. We fine-tune Qwen-2.5 models of multiple sizes on the resulting data. On reasoning benchmarks including AIME25, MATH500, and GPQA-Diamond, P-TTS consistently improves accuracy over competitive small-data baselines such as S1 and S1.1 (1K-shot), with the largest gains on AIME25 while remaining strong on MATH500 and GPQA-Diamond. P-TTS also improves generalization on out-of-domain reasoning evaluations. Ablations show that exemplar diversity and prompt-space scaling are critical drivers of improvement, suggesting that prompt scaling explores the latent space of reasoning patterns, amplifying LLM problem-solving with minimal annotation overhead. P-TTS offers a practical, low-cost way to elicit strong LLM reasoning in resource-constrained or rapidly evolving domains. Our code and data are available at https://github.com/VILA-Lab/PTTS."
}