Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling
The paper introduces a method called stochastic backtracking for improving test-time scaling in language models. This approach allows models to revisit previously generated states, enhancing accuracy while reducing the number of tokens generated. The authors demonstrate that their method outperforms existing PRM-guided techniques across various benchmarks.
- ▪Stochastic backtracking allows for revisiting historical prefixes during test-time scaling.
- ▪The method includes Subpool Selection and Power Backtrack Sequential Monte Carlo for efficiency.
- ▪Results show higher accuracy per token count compared to strong PRM-guided baselines.
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Computer Science > Artificial Intelligence arXiv:2605.25143 (cs) [Submitted on 24 May 2026] Title:Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling Authors:Dao Tran, Duc Anh Le, Ngoc Luu, Quan Pham, Tung Pham, Hung Bui View a PDF of the paper titled Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling, by Dao Tran and 5 other authors View PDF HTML (experimental) Abstract:Test-time scaling improves language model reasoning by spending additional compute to explore multiple solution trajectories. The key challenge is to maximize accuracy while minimizing the total number of generated tokens during reasoning.
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