Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
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.
673ff6ff60a79596d3e9aa99
Yu Zhao
673ff6ff60a79596d3e9aa9a
Huifeng Yin
673ff6ff60a79596d3e9aa9b
Bo Zeng
673ff6ff60a79596d3e9aa9c
Hao Wang
673ff6ff60a79596d3e9aa9d
Tianqi Shi
admin_assigned
2024-11-22T13:06:18.643Z
66389e004ea0d8fe73853fea
/avatars/1b9bd9e64b9f3e2660f597ab65f3c15a.svg
ShiTianqi
user
shitianqi
673ff6ff60a79596d3e9aa9e
Chenyang Lyu
admin_assigned
2024-11-22T13:06:07.311Z
6527d8b077bceabaab382a75
/avatars/69caacf9153dbf6a3796693a968b363f.svg
Chenyang Lyu
user
ChenyangLyu
673ff6ff60a79596d3e9aa9f
Longyue Wang
admin_assigned
2024-11-22T13:05:29.315Z
636b030c328133bdb3a523bc
/avatars/15d5d5403fef2f1368bb4185b199061d.svg
Longyue Wang
user
longyuewang
673ff6ff60a79596d3e9aaa0
Weihua Luo
admin_assigned
2024-11-22T13:05:23.601Z
66b03cedd59c09785e39711e
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/N5yfQBSP3oAKPCz4ylR09.png
Weihua Luo
user
acecamel1977
673ff6ff60a79596d3e9aaa1
Kaifu Zhang
admin_assigned
2024-11-22T13:05:18.011Z
63f87ebadf053017d1acbfdd
/avatars/e497ba5f41a2587837b4a6118d9367bb.svg
Kaifu Zhang
user
zhangkaifu314
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
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.
Marco-o1 extends reasoning models to open-ended domains without clear standards using Chain-of-Thought fine-tuning, Monte Carlo Tree Search, reflection mechanisms, and innovative reasoning strategies.
Chain-of-Thought Monte Carlo Tree Search reflection mechanisms