Monte Carlo Tree Search with Reasoning Path Refinement for Small Language Models in Conversational Text-to-NoSQL

• URL: http://arxiv.org/abs/2602.12574v1
• Date: Fri, 13 Feb 2026 03:35:38 GMT
• Title: Monte Carlo Tree Search with Reasoning Path Refinement for Small Language Models in Conversational Text-to-NoSQL
• Authors: Xubang Xiong, Raymond Chi-Wing Wong, Yuanfeng Song,
• Abstract summary: We introduce the Conversational Text-to-No task, which generates queries given a natural language question, a database, and a dialogue history.<n>We propose Stage-MCTS, a framework that endows small language models with query-specific reasoning capabilities.<n>Our approach outperforms state-of-the-art large reasoning models, improving execution value match accuracy by up to 7.93%.
• Score: 20.156191782890797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: NoSQL databases have been widely adopted in big data analytics, geospatial applications, and healthcare services, due to their flexibility and scalability. However, querying NoSQL databases requires specialized technical expertise, creating a high barrier for users. While recent studies have explored text-to-NoSQL problem, they primarily focus on single-turn interactions, ignoring the conversational nature of real-world queries. To bridge this gap, we introduce the Conversational Text-to-NoSQL task, which generates NoSQL queries given a natural language question, a NoSQL database, and the dialogue history. To address this task, we propose Stage-MCTS, a framework that endows small language models (SLMs) with NoSQL-specific reasoning capabilities by formulating query generation as a search problem. The framework employs Monte Carlo Tree Search (MCTS) guided by a rule-based reward to produce stepwise reasoning data, followed by progressive supervised fine-tuning (SFT) and self-training strategies. We further construct CoNoSQL, a cross-domain dataset with over 2,000 dialogues and 150 databases, to support evaluation. Experiments demonstrate that our approach outperforms state-of-the-art large reasoning models, improving execution value match (EVM) accuracy by up to 7.93%.

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