New theory explains how verifier imperfection impacts LLM test-time scaling

A new paper titled "ROC-n-reroll: How verifier imperfection affects test-time scaling" explores the theoretical underpinnings of improving language model performance through additional compute during inference. The research proves that the accuracy of methods like Best-of-N and Rejection Sampling is directly tied to the geometry of the verifier's ROC curve. Experiments using Qwen and Llama models on GSM8K and MATH500 datasets confirm that Rejection Sampling is more effective than Best-of-N for a fixed amount of compute, though both methods approach similar accuracy with unlimited compute. The study also highlights that low-compute performance does not reliably predict high-compute outcomes for these scaling techniques. AI

IMPACT Provides theoretical grounding for test-time scaling techniques, potentially guiding future research into more efficient LLM inference.

RANK_REASON Academic paper detailing theoretical findings and experimental validation on LLM scaling techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Best of Nollywood Awards
• Florian E. Dorner
• GSM8K
• llama
• MATH500
• Qwen
• rejection sampling
• ROC-n-reroll

• paper
• model release

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