Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929603568730112 |
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| author | Zhao, Yu Yin, Huifeng Zeng, Bo Wang, Hao Shi, Tianqi Lyu, Chenyang Wang, Longyue Luo, Weihua Zhang, Kaifu |
| author_facet | Zhao, Yu Yin, Huifeng Zeng, Bo Wang, Hao Shi, Tianqi Lyu, Chenyang Wang, Longyue Luo, Weihua Zhang, Kaifu |
| contents | Currently OpenAI o1 sparks 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions Zhao, Yu Yin, Huifeng Zeng, Bo Wang, Hao Shi, Tianqi Lyu, Chenyang Wang, Longyue Luo, Weihua Zhang, Kaifu Computation and Language Currently OpenAI o1 sparks 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. |
| title | Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2411.14405 |