• Fine-Tuning with CoT Data: We develop Marco-o1-CoT by performing full-parameter fine-tuning on the base model using open-source CoT dataset combined with our self-developed synthetic data.
• Solution Space Expansion via MCTS: We integrate LLMs with MCTS (Marco-o1-MCTS), using the model’s output confidence to guide the search and expand the solution space.
• Reasoning Action Strategy: We implement novel reasoning action strategies and a reflection mechanism (Marco-o1-MCTS mini-step), including exploring different action granularities within the MCTS framework and prompting the model to self-reflect, thereby significantly enhancing the model’s ability to solve complex problems.
• Application in Translation Tasks: We are the first to apply Large Reasoning Models (LRM) to Machine Translation task, exploring inference time scaling laws in the multilingual and translation domain.

Usage

ollama run marco-o1 "How many Rs are in strawberry?"

Parse the resulting string between <Output> and </Output>:

...
<Output>
There are 3 Rs in strawberry.
</Output>

References

GitHub

HuggingFace