Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Paper
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2411.14405
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Published
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61
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?"
Currently, Marco-o1 Large Language Model (LLM) 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.
| No | Variant | Cortex CLI command |
|---|---|---|
| 1 | Marco-o1-8b | cortex run marco-o1:8b |
cortexhub/marco-o1
cortex run marco-o1
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