IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning

• URL: http://arxiv.org/abs/2602.03468v1
• Date: Tue, 03 Feb 2026 12:43:09 GMT
• Title: IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning
• Authors: Haohao Luo, Zexi Li, Yuexiang Xie, Wenhao Zhang, Yaliang Li, Ying Shen,
• Abstract summary: Deep Research (DR) agents extend Large Language Models (LLMs) beyond parametric knowledge.<n>Unlike real-time conversational assistants, DR is computationally expensive and time-consuming.<n>We propose IntentRL, a framework that trains proactive agents to clarify latent user intents before starting long-horizon research.
• Score: 54.21689544323704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Research (DR) agents extend Large Language Models (LLMs) beyond parametric knowledge by autonomously retrieving and synthesizing evidence from large web corpora into long-form reports, enabling a long-horizon agentic paradigm. However, unlike real-time conversational assistants, DR is computationally expensive and time-consuming, creating an autonomy-interaction dilemma: high autonomy on ambiguous user queries often leads to prolonged execution with unsatisfactory outcomes. To address this, we propose IntentRL, a framework that trains proactive agents to clarify latent user intents before starting long-horizon research. To overcome the scarcity of open-ended research data, we introduce a scalable pipeline that expands a few seed samples into high-quality dialogue turns via a shallow-to-deep intent refinement graph. We further adopt a two-stage reinforcement learning (RL) strategy: Stage I applies RL on offline dialogues to efficiently learn general user-interaction behavior, while Stage II uses the trained agent and a user simulator for online rollouts to strengthen adaptation to diverse user feedback. Extensive experiments show that IntentRL significantly improves both intent hit rate and downstream task performance, outperforming the built-in clarify modules of closed-source DR agents and proactive LLM baselines.

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