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Test-Time Scaling via Error Localization

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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.

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arXiv Machine Learning
Jul 24

Test-Time Scaling via Error Localization

arXiv:2607. 21453v1 Announce Type: new Abstract: Scaling inference-time computation has emerged as a reliable method to improve the performance of large language models on complex reasoning and programming tasks.

By Rajiv Shailesh Chitale, Rahul Madhavan, Taneesh Gupta, Deepanway Ghosal, Aravindan Raghuveer
arXiv AI
Aug 5

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

arXiv:2608. 04001v1 Announce Type: cross Abstract: Large language models can solve substantially harder reasoning problems with more inference-time compute.

By Mohsen Hariri, Weicong Chen, Nahal Shahini, Vikash Singh, Kai Ye, Amirhossein Samandar, Debargha Ganguly, Sreehari Sankar, Yanyan Zhang, Shouren Wang, Jerry Peng, Biyao Zhang, Michael Hinczewski, Vipin Chaudhary
arXiv AI
Jun 15

Fractured Chain-of-Thought Reasoning

arXiv:2505. 12992v4 Announce Type: replace-cross Abstract: Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining.

By Baohao Liao, Hanze Dong, Yuhui Xu, Doyen Sahoo, Christof Monz, Junnan Li, Caiming Xiong
arXiv AI
Jun 8

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

arXiv:2606. 06915v1 Announce Type: cross Abstract: Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.

By Vladislav Smirnov (MBZUAI), Chieu Nguyen (MBZUAI), Sergey Senichev (Independent Researcher), Minh Ngoc Ta (MBZUAI), Ekaterina Fadeeva (ETH Z\"urich), Artem Vazhentsev (MBZUAI), Daria Galimzianova (MBZUAI), Nikolai Rozanov (MBZUAI, Imperial College London), Viktor Mazanov (Innopolis University), Jingwei Ni (ETH Z\"urich), Tianyi Wu (NUS), Igor Kiselev (Accenture), Mrinmaya Sachan (ETH Z\"urich), Iryna Gurevych (MBZUAI), Preslav Nakov (MBZUAI), Timothy Baldwin (MBZUAI), Artem Shelmanov (MBZUAI)