title: MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree
    Search
summary: 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.
paper_authors._id: 67e4c08fd9b7021d4a600fa4
paper_authors.name: Yunhai Hu
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/fc73c63a4e1f8fbb084ec43ec9af0af0.svg
paper_authors.fullname: Hu Yunhai
paper_authors.type: user
paper_authors.user: AlexCCtop
paper_authors._id: 67e4c08fd9b7021d4a600fa5
paper_authors.name: Yilun Zhao
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-31T03:40:49.669Z
paper_authors._id: 62f662bcc58915315c4eccea
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paper_authors.fullname: Yilun Zhao
paper_authors.type: user
paper_authors.user: yilunzhao
paper_authors._id: 67e4c08fd9b7021d4a600fa6
paper_authors.name: Chen Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T09:58:04.608Z
paper_authors._id: 660103ec4ae78d4ded4633fc
paper_authors.avatarUrl: /avatars/efce106d70f5d092bf44d0638aa49984.svg
paper_authors.fullname: CHEN Zhao
paper_authors.type: user
paper_authors.user: chenzhao
paper_authors._id: 67e4c08fd9b7021d4a600fa7
paper_authors.name: Arman Cohan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T09:57:57.092Z
paper_authors._id: 5f5ba21188f57f65f951f255
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1599840760465-noauth.png
paper_authors.fullname: Arman Cohan
paper_authors.type: user
paper_authors.user: armanc
paper_title: MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree
    Search
paper_summary: 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.
paper_ai_summary: MCTS-RAG improves small language models' reasoning by integrating retrieval-augmented generation and Monte Carlo Tree Search, leading to performance comparable to large models on knowledge-intensive tasks.
paper_ai_keywords: retrieve-augmented generation MCTS Monte Carlo Tree Search reasoning paths decision-making process ComplexWebQA GPQA FoolMeTwice LMs GPT-4o scaling inference-time compute