MATH-500 Best-of-N Weighted Selection Results for LLM Evaluation

500 math problems from the HuggingFaceH4/MATH-500 benchmark were used to evaluate Best-of-N weighted selection. The dataset contains results from an internship exercise exploring how test-time compute scaling with reward models can improve LLM performance. It was authored by cmpatino and last updated on April 23, 2026.

Use Cases

• Benchmarking LLM performance improvements based on Best-of-N weighted selection results.
• Analyzing the relationship between test-time compute scaling and accuracy on math problems.
• Studying the effectiveness of reward models for guiding LLM generation on reasoning tasks.
• Comparing different selection strategies for improving model outputs at inference time.

Strengths

• Based on a known benchmark of 500 math problems (MATH-500).
• Created as part of a structured HuggingFace internship exercise, suggesting methodological rigor.
• Explicitly focuses on the emerging technique of test-time compute scaling with reward models.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Collection Method

Constructed by evaluating Best-of-N weighted selection on a subset of the HuggingFaceH4/MATH-500 benchmark.

Time Range

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Freshness

Last updated 2026-04-23 13:06:53; freshness should be verified.

Geography

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