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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25143 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | CIqvHsptPMAz |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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