huggingface/search-and-learn resource

Health v2 · maintenance only

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Adoption not part of the score

1131 stars · 131 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Search and Learn is a collection of Python scripts and YAML recipes from Hugging Face for scaling inference-time (test-time) compute of open LLMs using search algorithms like Best-of-N, beam search, and Diverse Verifier Tree Search. It accompanies a blog post and lets users replicate test-time compute scaling results.

Use cases

• scale test-time compute for open LLMs
• run beam search over LLM generations
• replicate o1-style test-time scaling results
• apply best-of-N sampling with verifiers
• run diverse verifier tree search on math problems
• learn inference-time compute scaling techniques

When to choose

• you want to improve open-model reasoning by spending more compute at inference
• you want to reproduce published test-time compute scaling experiments
• you need ready-made recipes for search strategies like beam search or DVTS

When to avoid

• you need a production inference server or API
• you want training or fine-tuning recipes rather than inference-time methods
• you need a maintained library with stable APIs rather than research scripts

Facets

learning-resource · maturity active

llm-inference machine-learning benchmarking large-language-models machine-learning artificial-intelligence python test-time-compute search-algorithms beam-search best-of-n tree-search recipes open-models gpu linux

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