arXiv:2606. 03217v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermediate reasoning steps at inference time.
By Kaito Takanami, Cengiz Pehlevan
arXiv:2509. 04027v4 Announce Type: replace Abstract: Test-time scaling, primarily manifested through multi-step Chain-of-Thought (CoT) reasoning via Reinforcement Learning (RL), has emerged as a pivotal paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs).
By Zeyu Gan, Hao Yi, Yong Liu
arXiv:2509. 04027v3 Announce Type: replace Abstract: Test-time scaling, primarily manifested through multi-step Chain-of-Thought (CoT) reasoning via Reinforcement Learning (RL), has emerged as a pivotal paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs).
By Zeyu Gan, Hao Yi, Yong Liu
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:2505. 17315v2 Announce Type: replace Abstract: Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored.
By Wang Yang, Zirui Liu, Hongye Jin, Qingyu Yin, Vipin Chaudhary, Xiaotian Han
arXiv:2604. 07822v2 Announce Type: replace-cross Abstract: We study implicit reasoning, i.
By Harsh Kohli, Srinivasan Parthasarathy, Huan Sun, Yuekun Yao