Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning
This addresses the need for more adaptive and exploratory conversational AI systems in information-seeking tasks, representing an incremental improvement over single-turn methods.
The paper tackles the problem of multi-turn conversational search by introducing an agent that interleaves search and reasoning across turns, using reinforcement learning to adapt to evolving user goals, and it surpasses existing baselines on four conversational benchmarks.
Large Language Models (LLMs) have become a popular interface for human-AI interaction, supporting information seeking and task assistance through natural, multi-turn dialogue. To respond to users within multi-turn dialogues, the context-dependent user intent evolves across interactions, requiring contextual interpretation, query reformulation, and dynamic coordination between retrieval and generation. Existing studies usually follow static rewrite, retrieve, and generate pipelines, which optimize different procedures separately and overlook the mixed-initiative action optimization simultaneously. Although the recent developments in deep search agents demonstrate the effectiveness in jointly optimizing retrieval and generation via reasoning, these approaches focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. We introduce a conversational agent that interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. The experimental results across four widely used conversational benchmarks demonstrate the effectiveness of our methods by surpassing several existing strong baselines.