title: Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
summary: Currently OpenAI o1 has sparked a surge of interest in the study of large
  reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on
  disciplines with standard answers, such as mathematics, physics, and coding --
  which are well-suited for reinforcement learning (RL) -- but also places
  greater emphasis on open-ended resolutions. We aim to address the question:
  "Can the o1 model effectively generalize to broader domains where clear
  standards are absent and rewards are challenging to quantify?" Marco-o1 is
  powered by Chain-of-Thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS),
  reflection mechanisms, and innovative reasoning strategies -- optimized for
  complex real-world problem-solving tasks.
paper_authors._id: 673ff6ff60a79596d3e9aa99
paper_authors.name: Yu Zhao
paper_authors._id: 673ff6ff60a79596d3e9aa9a
paper_authors.name: Huifeng Yin
paper_authors._id: 673ff6ff60a79596d3e9aa9b
paper_authors.name: Bo Zeng
paper_authors._id: 673ff6ff60a79596d3e9aa9c
paper_authors.name: Hao Wang
paper_authors._id: 673ff6ff60a79596d3e9aa9d
paper_authors.name: Tianqi Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:06:18.643Z
paper_authors._id: 66389e004ea0d8fe73853fea
paper_authors.avatarUrl: /avatars/1b9bd9e64b9f3e2660f597ab65f3c15a.svg
paper_authors.fullname: ShiTianqi
paper_authors.type: user
paper_authors.user: shitianqi
paper_authors._id: 673ff6ff60a79596d3e9aa9e
paper_authors.name: Chenyang Lyu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:06:07.311Z
paper_authors._id: 6527d8b077bceabaab382a75
paper_authors.avatarUrl: /avatars/69caacf9153dbf6a3796693a968b363f.svg
paper_authors.fullname: Chenyang Lyu
paper_authors.type: user
paper_authors.user: ChenyangLyu
paper_authors._id: 673ff6ff60a79596d3e9aa9f
paper_authors.name: Longyue Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:05:29.315Z
paper_authors._id: 636b030c328133bdb3a523bc
paper_authors.avatarUrl: /avatars/15d5d5403fef2f1368bb4185b199061d.svg
paper_authors.fullname: Longyue Wang
paper_authors.type: user
paper_authors.user: longyuewang
paper_authors._id: 673ff6ff60a79596d3e9aaa0
paper_authors.name: Weihua Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:05:23.601Z
paper_authors._id: 66b03cedd59c09785e39711e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/N5yfQBSP3oAKPCz4ylR09.png
paper_authors.fullname: Weihua Luo
paper_authors.type: user
paper_authors.user: acecamel1977
paper_authors._id: 673ff6ff60a79596d3e9aaa1
paper_authors.name: Kaifu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:05:18.011Z
paper_authors._id: 63f87ebadf053017d1acbfdd
paper_authors.avatarUrl: /avatars/e497ba5f41a2587837b4a6118d9367bb.svg
paper_authors.fullname: Kaifu Zhang
paper_authors.type: user
paper_authors.user: zhangkaifu314
paper_title: Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
paper_summary: Currently OpenAI o1 has sparked a surge of interest in the study of large
  reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on
  disciplines with standard answers, such as mathematics, physics, and coding --
  which are well-suited for reinforcement learning (RL) -- but also places
  greater emphasis on open-ended resolutions. We aim to address the question:
  "Can the o1 model effectively generalize to broader domains where clear
  standards are absent and rewards are challenging to quantify?" Marco-o1 is
  powered by Chain-of-Thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS),
  reflection mechanisms, and innovative reasoning strategies -- optimized for
  complex real-world problem-solving tasks.
paper_ai_keywords: Chain-of-Thought (CoT) fine-tuning Monte Carlo Tree Search (MCTS) reflection mechanisms