We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥
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We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥
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How? By combining step-wise reward models with tree search algorithms :)
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How? By combining step-wise reward models with tree search algorithms :)
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We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"
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We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"
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We're open sourcing the full recipe and sharing a detailed blog post.
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We're open sourcing the full recipe and sharing a detailed blog post.
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In our blog post we cover:
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In our blog post we cover:
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📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.
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📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.
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🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.
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🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.
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🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM
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🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM
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Here's the links:
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Here's the links:
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- Blog post: 
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- Blog post: 
https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute
HuggingFaceH4/blogpost-scaling-test-time-compute
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https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute
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- Code: 
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- Code: 
https://github.com/huggingface/search-and-learn
https://github.com/huggingface/search-and-learn
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Enjoy!
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Enjoy!
We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥

How? By combining step-wise reward models with tree search algorithms :)

We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"

We're open sourcing the full recipe and sharing a detailed blog post.

In our blog post we cover:

📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.

🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.

🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM

Here's the links:

- Blog post: https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute

- Code: https://github.com/huggingface/search-and-learn

Enjoy!
🔥
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