Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Authors: name: Yu Zhao, name: Huifeng Yin, name: Bo Zeng, name: Hao Wang, name: Tianqi Shi, name: Chenyang Lyu, name: Longyue Wang, name: Weihua Luo, name: Kaifu Zhang

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.