AIBullisharXiv – CS AI · 7h ago7/10
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Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling
Researchers introduce stochastic backtracking, a novel test-time scaling method for language models that revisits previously generated solution paths rather than committing irreversibly to frontier candidates. The approach uses subpool selection and power backtrack sequential Monte Carlo to improve reasoning accuracy while reducing token generation, outperforming existing PRM-guided methods across mathematical benchmarks.