Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling
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. Recent PRM-guided methods score intermediate prefixes to steer this search, but most are frontier-only: they keep only the current active prefixes and irreversibly prune or resample away the rest using noisy PRM scores. This can cause premature commitment, diversity collapse, and the loss of prefixes that still admit correct continuations. We introduce stochastic backtracking over a persistent pool of historical prefixes, allowing test-time compute to r...
| Authors | Dao Tran, Duc Anh Le, Ngoc Luu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 May 2026 |
| Last updated at source | 31 May 2026 |
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