Granularity-Regulated Adaptive Computational Efficiency for Optimal Verification in Test-Time Scaling
Quick Answer
This paper shows that The GRACE framework optimizes verification granularity in test-time scaling for large language models, demonstrating that fine-grained verification excels under high compute budgets or difficult problems, while coarse-grained is better for low budgets and easier tasks.
Quick Take
Empirical results show a 3.1% accuracy improvement over fixed strategies on benchmarks like MATH-500 and GSM8K.
Key Points
- GRACE framework defines optimal verification granularity based on problem difficulty and compute budget.
- Fine-grained verification is preferred for high-complexity tasks with sufficient compute resources.
- Coarse-grained verification is more effective for low-budget, simpler problems.
- Empirical tests on MATH-500, GSM8K, and AIME validate theoretical claims.
- Adaptive strategies outperform fixed-granularity approaches by up to 3.1% in accuracy.
Paper Resources
Source Excerpt
(TTS) has emerged as a powerful paradigm for improving the reasoning performance of (LLMs) by investing additional compute at inference time. A central component of TTS is the \emph{verifier}, which selects or scores candidate solutions to guide the search process. While prior work has explored the benefit of verification, a fundamental question remains underexplored: \emph{what is the optimal granularity of verification under a given compute budget? } Coar
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