Open Access
| Issue |
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
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|
|---|---|---|
| Article Number | 01025 | |
| Number of page(s) | 4 | |
| Section | Artificial Intelligence, Big Data and Computer Science | |
| DOI | https://doi.org/10.1051/itmconf/20268801025 | |
| Published online | 27 July 2026 | |
- M. Liu, S. Diao, X. Lu, J. Hu, X. Dong, Y. Choi, J. Kautz, Y. Dong, ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models, NeurIPS (2025). [Google Scholar]
- Z. Hou, X. Lv, R. Lu, J. Zhang, Y. Li, Z. Yao, J. Li, J. Tang, Y. Dong, T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling, ICML (2025). [Google Scholar]
- Y. Wang, Q. Yang, Z. Zeng, L. Ren, L. Liu, B. Peng, H. Cheng, X. He, K. Wang, J. Gao, W. Chen, S. Wang, S. S. Du, Y. Shen, Reinforcement Learning for Reasoning in Large Language Models with One Training Example, NeurIPS (2025). [Google Scholar]
- M. Shen, G. Zeng, Z. Qi, Z.-W. Hong, Z. Chen, W. Lu, G. Wornell, S. Das, D. Cox, C. Gan, Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search, ICML (2025). [Google Scholar]
- Y. Yue, Z. Chen, R. Lu, A. Zhao, Z. Wang, Y. Yue, S. Song, G. Huang, Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model, NeurIPS (2025). [Google Scholar]
- Z. Bi, K. Han, C. Liu, Y. Tang, Y. Wang, Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning, ICML (2025). [Google Scholar]
- M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk, T. Hoefler, Graph of Thoughts: Solving Elaborate Problems with Large Language Models, AAAI (2024). [Google Scholar]
- L. H. Klein, N. Potamitis, R. Aydin, R. West, C. Gulcehre, A. Arora, Fleet of Agents: Coordinated Problem Solving with Large Language Models, ICML (2025). [Google Scholar]
- X. Huang, S. Cheng, S. Huang, J. Shen, Y. Xu, C. Zhang, Y. Qu, QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction, ACL (2024). [Google Scholar]
- Z. Wu, Q. Zeng, Z. Zhang, Z. Tan, C. Shen, M. Jiang, Large Language Models Can Self-Correct with Key Condition Verification, ACL (2024). [Google Scholar]
- Y. Li, Policy Guided Tree Search for Enhanced LLM Reasoning, ICML (2025). [Google Scholar]
- Y. Yuan, T. Xie, Reinforce LLM Reasoning through Multi-Agent Reflection, ICML (2025). [Google Scholar]
- K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, C. Hesse, J. Schulman, Training Verifiers to Solve Math Word Problems, arXiv:2110.14168 (2021). [Google Scholar]
- D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, J. Steinhardt, Measuring Mathematical Problem Solving With the MATH Dataset, NeurIPS (2021). [Google Scholar]
- S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y. Cao, K. Narasimhan, Tree of Thoughts: Deliberate Problem Solving with Large Language Models, NeurIPS (2023). [Google Scholar]
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