InternBootcamp is an open‑source framework that offers over 1,000 domain‑diverse task environments for large language model (LLM) reasoning research. It introduces Bootcamp‑Eval, an automatically generated benchmark for comprehensive performance assessment. Experiments show that training on InternBootcamp significantly improves reasoning performance, with a 32B model achieving state‑of‑the‑art results on Bootcamp‑Eval and other established benchmarks, demonstrating that scaling the number of training tasks yields consistent gains.
By Peiji Li, Jiasheng Ye, Yongkang Chen, Linyang Li, Yichuan Ma, Zijie Yu, Ganqu Cui, Haozhan Li, Jiacheng Chen, Chengqi Lyu, Wenwei Zhang, Qipeng Guo, Dahua Lin, Bowen Zhou, Kai Chen
arXiv:2607. 04784v1 Announce Type: cross Abstract: Defining the reasoning boundaries and ensuring the reliability of Large Reasoning Models (LRMs) remains a critical challenge.
By Shide Zhou, Kailong Wang, Ling Shi, Haoyu Wang
arXiv:2505. 24069v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making.
By Yu He, Yingxi Li, Colin White, Ellen Vitercik
arXiv:2609.16055v1 Announce Type: cross
Abstract: Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasonin...
By Zhiren Gong, Yikun Hou, Zihao Zeng, Ming Xiao, Chau Yuen, Wei Yang Bryan Lim
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)
arXiv:2606. 08976v1 Announce Type: new Abstract: LLM-based RTL generation and reasoning is a promising direction for hardware design automation.
By Jing Wang, Shang Liu, Wenji Fang, Yuchao Wu, Yugao Zhu, Zhiyao Xie
arXiv:2605.28006v2 Announce Type: replace-cross
Abstract: Understanding how LLMs reason is hindered by a practical asymmetry: while their generated outputs are observable, the underlying reasoning pa...
By Leonardo Matthew Yauw, Wei-Bin Kou, Yujiu Yang
The paper surveys efficient reasoning in large language models, contrasting fast intuitive (System 1) and slow deep (System 2) reasoning. It analyzes why System 2 is computationally costly yet more accurate, and why System 1 is efficient but less effective. The survey covers causes of inefficiency, patterns of reasoning behavior, and potential solutions to balance performance and computational budgets, offering actionable insights and an open‑source repository for ongoing research.
By Rui Wang, Hongru Wang, Boyang Xue, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Heng Ji, Kam-Fai Wong
arXiv:2607. 20448v1 Announce Type: cross Abstract: We introduce Domyn-Small, a 10-billion-parameter open-weight reasoning language model released under the MIT license.
By Simone Angarano, Francesco Bertolotti, Federico D'Ambrosio, Michele Resta, Alessandro Rognoni, Nicol\`o Ruggeri, Dario Salvati, Andrea Valenti, Alberto Veneri, Martin Cimmino
The paper introduces SWE-Flux, a repository‑level benchmark designed to test large language models’ ability to reason about runtime behavior. It contains 480 execution‑grounded instances from 12 real Python repositories, with gold answers automatically harvested from instrumented test executions. Evaluation of five LLMs shows the task remains difficult, with the best model achieving only 37% accuracy, and the benchmark can generate challenging variants through input perturbation.
By Hamed Taherkhani, Mohammad Abdollahi, Melika Sepidband, Hridya Dhulipala, Tien N. Nguyen, Hadi Hemmati
arXiv:2510. 27544v3 Announce Type: replace Abstract: Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoning, but underperform at counterfactual causal understanding and reasoning.
By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito
arXiv:2509. 03059v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RLVR), particularly in domains like mathematics and programming, where ground-truth correctness can be automatically evaluated.
By Xingyue Huang, Rishabh, Gregor Franke, Ziyi Yang, Jiamu Bai, Weijie Bai, Jinhe Bi, Zifeng Ding, Yiqun Duan, Chengyu Fan, Wendong Fan, Xin Gao, Ruohao Guo, Yuan He, Zhuangzhuang He, Xianglong Hu, Neil Johnson, Bowen Li, Fangru Lin, Siyu Lin, Tong Liu, Yunpu Ma, Hao Shen, Hao Sun, Beibei Wang, Fangyijie Wang, Hao Wang, Haoran Wang, Yang Wang, Yifeng Wang, Zhaowei Wang, Ziyang Wang, Yifan Wu, Zikai Xiao, Chengxing Xie, Fan Yang, Junxiao Yang, Qianshuo Ye, Ziyu Ye, Guangtao Zeng, Yuwen Ebony Zhang, Zeyu Zhang, Zihao Zhu, Bernard Ghanem, Philip Torr, Guohao Li