GRACE framework proves a phase transition in optimal verification granularity for TTS and introduces an adaptive strategy that unifies search methods and improves accuracy up to 3.1% over fixed baselines on MATH-500, GSM8K, and AIME.
V-petl bench: A unified visual parameter-efficient transfer learning benchmark.Advances in Neural Information Processing Systems, 37:80522–80535, 2024
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Granularity-Regulated Adaptive Computational Efficiency for Optimal Verification in Test-Time Scaling
GRACE framework proves a phase transition in optimal verification granularity for TTS and introduces an adaptive strategy that unifies search methods and improves accuracy up to 3.1% over fixed baselines on MATH-500, GSM8K, and AIME.