Test-Time Scaling via Error Localization
Scaling inference-time computation has emerged as a reliable method to improve the performance of large language models on complex reasoning and programming tasks. However, standard approaches such as independent sampling and sequential multi-turn refinement operate without token-level credit assignment, resulting in computational inefficiency, since valid reasoning prefixes are frequently discarded. In this work, we introduce Test-Time Scaling via Error Localization (TTEL), an inference-time algorithm that utilizes fixed or environment feedback to perform token-level error localization. By comparing conditional probabilities under informed feedback against a null-context baseline, TTEL i...
| Authors | Rajiv Shailesh Chitale, Rahul Madhavan, Taneesh Gupta et al. |
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| Primary category | cs.LG |
| Published | 23 July 2026 |
| Last updated at source | 24 July 2026 |
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