Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers

TL;DR

This paper introduces Proactive Interactive Reasoning (PIR), a new paradigm that transforms large language models into proactive inquirers capable of interactive reasoning and clarification, significantly improving accuracy and robustness across multiple tasks.

Contribution

The paper presents PIR, a novel interactive reasoning framework with fine-tuning and user-simulator components, enabling LLMs to actively clarify uncertainties and outperform existing methods.

Findings

Achieves up to 32.70% higher accuracy

Improves pass rate by 22.90%

Reduces reasoning computation and unnecessary interactions

Abstract

Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain fundamentally limited by a \emph{blind self-thinking} paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. We propose Proactive Interactive Reasoning (PIR), a new reasoning paradigm that transforms LLMs from passive solvers into proactive inquirers that interleave reasoning with clarification. Unlike existing search- or tool-based frameworks that primarily address knowledge uncertainty by querying external environments, PIR targets premise- and intent-level uncertainty through direct interaction with the user. PIR is implemented via two core components: (1) an uncertainty-aware supervised fine-tuning procedure that equips models with interactive reasoning capability, and (2) a user-simulator-based policy optimization framework driven by a composite reward that aligns model behavior with user intent. Extensive experiments on mathematical reasoning, code generation, and document editing demonstrate that PIR consistently outperforms strong baselines, achieving up to 32.70\% higher accuracy, 22.90\% higher pass rate, and 41.36 BLEU improvement, while reducing nearly half of the reasoning computation and unnecessary interaction turns. Further reliability evaluations on factual knowledge, question answering, and missing-premise scenarios confirm the strong generalization and robustness of PIR. Model and code are publicly available at: \href{https://github.com/SUAT-AIRI/Proactive-Interactive-R1}