Tiny DeepSeek Clone BEATS OpenAI's o1 at Math (Fits On Your Phone!)

1. Tiny models can outperform larger models in specific tasks.

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A new 1.5 billion parameter model, DeepScaler, surpasses OpenAI's o1 in math performance, demonstrating the effectiveness of smaller, specialized models.

• DeepScaler uses reinforcement learning with verifiable rewards, making it efficient and effective.
• It was trained for only $4,500, showcasing cost-effective model training.
• The model can run on devices as small as smartphones, making advanced AI accessible.

2. Reinforcement learning enhances model training efficiency.

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DeepScaler employs an outcome reward model, which rewards the model for overall correctness, improving its reasoning capabilities.

• This method contrasts with process reward models that provide feedback on individual steps.
• The approach allows the model to learn from mistakes and improve its performance over time.
• The efficiency of reinforcement learning is evident even in smaller models.

3. Open-sourcing models promotes innovation and accessibility.

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DeepScaler's creators have open-sourced the model, allowing anyone to download and replicate the training process.

• This transparency fosters community engagement and further development of AI technologies.
• Users can experiment with the model on personal devices, enhancing learning opportunities.
• Open-sourcing encourages collaboration and innovation in AI research.