title: Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo
    Tree Self-refine with LLaMa-3 8B
summary: This paper introduces the MCT Self-Refine (MCTSr) algorithm, an innovative
  integration of Large Language Models (LLMs) with Monte Carlo Tree Search
  (MCTS), designed to enhance performance in complex mathematical reasoning
  tasks. Addressing the challenges of accuracy and reliability in LLMs,
  particularly in strategic and mathematical reasoning, MCTSr leverages
  systematic exploration and heuristic self-refine mechanisms to improve
  decision-making frameworks within LLMs. The algorithm constructs a Monte Carlo
  search tree through iterative processes of Selection, self-refine,
  self-evaluation, and Backpropagation, utilizing an improved Upper Confidence
  Bound (UCB) formula to optimize the exploration-exploitation balance. Extensive
  experiments demonstrate MCTSr's efficacy in solving Olympiad-level mathematical
  problems, significantly improving success rates across multiple datasets,
  including GSM8K, GSM Hard, MATH, and Olympiad-level benchmarks, including Math
  Odyssey, AIME, and OlympiadBench. The study advances the application of LLMs in
  complex reasoning tasks and sets a foundation for future AI integration,
  enhancing decision-making accuracy and reliability in LLM-driven applications.
paper_authors._id: 666917ff2bd3135cc34128a6
paper_authors.name: Di Zhang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-11-28T07:45:01.874Z
paper_authors._id: 64bce15bafd1e46c5504ad38
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paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: di-zhang-fdu
paper_authors._id: 666917ff2bd3135cc34128a7
paper_authors.name: Jiatong Li
paper_authors._id: 666917ff2bd3135cc34128a8
paper_authors.name: Xiaoshui Huang
paper_authors._id: 666917ff2bd3135cc34128a9
paper_authors.name: Dongzhan Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-12T08:33:20.574Z
paper_authors._id: 6538b861613fe158bd581e35
paper_authors.avatarUrl: /avatars/6817dbfe903675721fd227058b0a91ac.svg
paper_authors.fullname: Dongzhan Zhou
paper_authors.type: user
paper_authors.user: schrodingers-tiger
paper_authors._id: 666917ff2bd3135cc34128aa
paper_authors.name: Yuqiang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-12T08:34:18.015Z
paper_authors._id: 65cb61ba0390fce629de99d1
paper_authors.avatarUrl: /avatars/9b4b4556ffd8de215dc37b02366d781b.svg
paper_authors.fullname: Yuqiang Li
paper_authors.type: user
paper_authors.user: yuqiangli
paper_authors._id: 666917ff2bd3135cc34128ab
paper_authors.name: Wanli Ouyang
paper_title: Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo
    Tree Self-refine with LLaMa-3 8B
paper_summary: This paper introduces the MCT Self-Refine (MCTSr) algorithm, an innovative
  integration of Large Language Models (LLMs) with Monte Carlo Tree Search
  (MCTS), designed to enhance performance in complex mathematical reasoning
  tasks. Addressing the challenges of accuracy and reliability in LLMs,
  particularly in strategic and mathematical reasoning, MCTSr leverages
  systematic exploration and heuristic self-refine mechanisms to improve
  decision-making frameworks within LLMs. The algorithm constructs a Monte Carlo
  search tree through iterative processes of Selection, self-refine,
  self-evaluation, and Backpropagation, utilizing an improved Upper Confidence
  Bound (UCB) formula to optimize the exploration-exploitation balance. Extensive
  experiments demonstrate MCTSr's efficacy in solving Olympiad-level mathematical
  problems, significantly improving success rates across multiple datasets,
  including GSM8K, GSM Hard, MATH, and Olympiad-level benchmarks, including Math
  Odyssey, AIME, and OlympiadBench. The study advances the application of LLMs in
  complex reasoning tasks and sets a foundation for future AI integration,
  enhancing decision-making accuracy and reliability in LLM-driven applications.
paper_ai_keywords: Large Language Models (LLMs) Monte Carlo Tree Search (MCTS) Self-Refine Systematic exploration Heuristic self-refine mechanisms Iterative processes Selection Self-refine Self-evaluation Backpropagation Upper Confidence Bound (UCB) Exploration-exploitation balance Olympiad-level mathematical problems GSM8K GSM Hard MATH Olympiad-level benchmarks Math Odyssey AIME OlympiadBench Complex reasoning tasks Decision-making accuracy Decision-making reliability