MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search
This addresses the challenge of improving factual accuracy and reducing hallucinations in small-scale models for knowledge-intensive reasoning tasks, representing a novel hybrid approach rather than a foundational breakthrough.
The paper tackles the problem of enhancing reasoning in small language models on knowledge-intensive tasks by integrating retrieval-augmented generation with Monte Carlo Tree Search, resulting in performance comparable to GPT-4o on datasets like ComplexWebQA, GPQA, and FoolMeTwice.
We introduce MCTS-RAG, a novel approach that enhances the reasoning capabilities of small language models on knowledge-intensive tasks by leveraging retrieval-augmented generation (RAG) to provide relevant context and Monte Carlo Tree Search (MCTS) to refine reasoning paths. MCTS-RAG dynamically integrates retrieval and reasoning through an iterative decision-making process. Unlike standard RAG methods, which typically retrieve information independently from reasoning and thus integrate knowledge suboptimally, or conventional MCTS reasoning, which depends solely on internal model knowledge without external facts, MCTS-RAG combines structured reasoning with adaptive retrieval. This integrated approach enhances decision-making, reduces hallucinations, and ensures improved factual accuracy and response consistency. The experimental results on multiple reasoning and knowledge-intensive datasets datasets (i.e., ComplexWebQA, GPQA, and FoolMeTwice) show that our method enables small-scale LMs to achieve performance comparable to frontier LLMs like GPT-4o by effectively scaling inference-time compute, setting a new standard for reasoning in small-scale models.