TTEL能定位推理错误，只重算出错部分，省近一半计算量。Qwen3-8B在编程测试上成绩亮眼，值得关注。
TTEL是一种利用固定或环境反馈进行token级错误定位的推理时算法。它通过比较条件概率隔离出错步骤，截断并重新生成，复用有效前缀。在Qwen3-8B上，TTEL在LiveCodeBench达到pass@64为71.0%，生成token仅约360.4k，远少于独立采样的735.0k。在AIME-2025和HMMT-2025数学基准上，TTEL也优于其他测试时基线。
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 isolates the step at which an error occurred. The algorithm then truncates the trajectory and branches a new generation, maximally reusing the valid prefix. Extensive evaluations demonstrate that TTEL establishes strictly dominating Pareto frontiers across sequential reasoning domains, measured by pass-at-k vs. generated-token cost. With Qwen3-8B on LiveCodeBench, TTEL attains a pass@64 of 71.0% while generating approximately half as many tokens as independent sampling (360.4k vs. 735.0k). Generalizing to math benchmarks AIME-2025 and HMMT-2025, TTEL cleanly outperforms competing test-time baselines across both Qwen3-8B and Qwen3-4B-Thinking-2507.