Reasoning While Asking: Transforming Reasoning LLMs into Proactive Inquirers (PIR)

This organization hosts the official models and datasets for the paper "Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers".

💡 Motivation

Current reasoning LLMs (e.g., GPT-o1, DeepSeek-R1) suffer from blind self-thinking: they perform extensive internal reasoning even when critical information is missing or user intent is ambiguous. This leads to overthinking, hallucinations, and misaligned conclusions.

PIR (Proactive Interactive Reasoning) is a new paradigm that transforms reasoning LLMs from passive solvers into proactive inquirers. Instead of guessing, PIR-enabled models detect uncertainty during reasoning and actively ask users for clarification before proceeding.

Key Features

• User-Intent Alignment: Optimizes interaction through US-GRPO with composite rewards balancing accuracy, efficiency, and helpfulness.
• Significant Improvements: Up to 32.70% higher accuracy, 22.90% higher pass rate, and 41.36 BLEU improvement over baselines.
• Reduced Computation: Nearly halves unnecessary reasoning tokens and interaction turns.

📦 Models

We provide the following models trained with the PIR paradigm:

📚 Datasets

The datasets used to train and evaluate PIR are available here:

• Reasoning-While-Asking-SFT-Dataset: The dataset used for the initial Supervised Fine-Tuning (SFT) phase.
• DeepSeek-R1-Distill-Data-5k: Distilled data used for training.

📜 Citation

If you find this work useful, please cite our paper:

@misc{chen2026reasoningaskingtransformingreasoning,
      title={Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers}, 
      author={Xin Chen and Feng Jiang and Yiqian Zhang and Hardy Chen and Shuo Yan and Wenya Xie and Min Yang and Shujian Huang},
      year={2026},
      eprint={2601.22139},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.22139},
}