Elamur Research⌕
☰
home / papers / 2026 / TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning

TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning

Paper · 2026 · Jinyang Wu, Chonghua Liao, Mingkuan Feng et al.

Summary

RL methods such as GRPO strengthen LLM reasoning, but they learn only from rollouts the model samples freely from its own distribution. This wastes exploration, rarely produces reusable high-level strategies, and can make training collapse on weaker models. TemplateRL addresses this by adding explicit strategic guidance to policy optimization. First, Monte Carlo Tree Search runs over a small seed set of problems using a fixed menu of reasoning actions, such as posing a sub-question or reflecting. For each problem, the best trace is selected by a score that weighs reward against path length, and its action sequence is kept as a template. Matching sequences are merged into a library, with each template indexed by the mean Problem Condition Complexity (PCC) of its source questions. During training, the templates whose PCC is closest to the current question steer rollout generation, and several templates are used per question. The library is human-readable and editable, and it can grow from new successful rollouts, even at inference. This yields large gains over GRPO, steadier training and better transfer to other domains.

Key points

  • Selection and aggregation. Each MCTS trace gets a score of b·R − (1−b)·C, where C is the number of actions. The top-scoring action chain becomes a template, and templates are aggregated by PCC.
  • Results. With a 7B backbone, TemplateRL outperforms GRPO, improving on it by 99% for AIME and 41% for AMC. GRPO collapses on weak models like Llama-3.2-3B, while TemplateRL trains stably.
  • Generalization. The method transfers to out-of-distribution benchmarks: BALROG (agents), GPQA-D (science) and MMLU-Pro (knowledge). It also helps in both text and vision settings.
  • Test-time library updates. Expanding the library during inference, using majority-vote pseudo-labels, gives these changes:
    • AIME24 rises from 33.3 to 36.7.
    • GPQA-D rises from 37.9 to 40.4.
    • AMC stays at 77.5.
    • The average rises from 49.6 to 51.6.
  • Template diversity. In ablations, more diverse template guidance tends to raise training rewards; |g| = 2 templates per question is the default.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Tsinghua UniversityBeijing · China415 works · 94,028 citations🎓Princeton UniversityPrinceton · United States113 works · 48,612 citations🎓Nanyang Technological UniversitySingapore · Singapore184 works · 27,484 citations🔬Shanghai Artificial Intelligence LaboratoryShanghai · China152 works · 13,166 citations🔬Beijing National Research Center for Information Science and TechnologyBeijing · China33 works · 6,706 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,894 worksReasoning · 1,685 works

Cite

Cite as Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Haoran Luo, Ling Yang, Huazhe Xu and Jianhua Tao. 2026. TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.198

@inproceedings{wu2026templaterl,
  title = {{TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning}},
  author = {Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Haoran Luo, Ling Yang, Huazhe Xu and Jianhua Tao},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.198},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.198}
}

References

72 references, 44 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  2. Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, and Sara Hooker. 2024. Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms. Preprint, arXiv:2402.14740. find
  3. Akari Asai, Sewon Min, Zexuan Zhong, and Danqi Chen. 2023. Retrieval-based language models and applications. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts), pages 41–46.
  4. Guillaume Chaslot, Sander Bakkes, Istvan Szita, and Pieter Spronck. 2008. Monte-carlo tree search: A new framework for game ai. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 4, pages 216– 217. find
  5. Ganqu Cui, Yuchen Zhang, Jiacheng Chen, Lifan Yuan, Zhi Wang, Yuxin Zuo, Haozhan Li, Yuchen Fan, Huayu Chen, Weize Chen, and 1 others. 2025b. The entropy mechanism of reinforcement learning for reasoning language models. arXiv preprint arXiv:2505.22617.
  6. C Nicolò De Sabbata, Theodore R Sumers, and Thomas L Griffiths. 2024. Rational metareasoning for large language models. arXiv preprint arXiv:2410.05563. — [arXiv:2410.05563](https://arxiv.org/abs/2410.05563) find
  7. Hao Dong, Zihan Ding, and Shanghang Zhang. 2020. Deep Reinforcement Learning: Fundamentals, Research and Applications, 1 edition, volume 1 of eBook Packages: Mathematics and Statistics. Springer Singapore. find
  8. Susan E Embretson and Robert C Daniel. 2008. Understanding and quantifying cognitive complexity level in mathematical problem solving items. Psychology Science, 50(3):328. find
  9. Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, and Jianhua Tao. 2025. Two-stage regularization-based structured pruning for llms. arXiv preprint arXiv:2505.18232. find
  10. Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei-Chiu Ma, and Ranjay Krishna. 2025. Blink: Multimodal large language models can see but not perceive. In Computer Vision – ECCV 2024, pages 148–166, Cham. Springer Nature Switzerland.
  11. Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, Nathan Lile, and Noah D Goodman. 2025. Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars. arXiv preprint arXiv:2503.01307.
  12. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783.
  13. Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2022. Decomposed prompting: A modular approach for solving complex tasks. arXiv preprint arXiv:2210.02406.
  14. Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, and Mao Yang. 2025. rstar-math: Small llms can master math reasoning with self-evolved deep thinking. arXiv preprint arXiv:2501.04519.
  15. Levente Kocsis and Csaba Szepesvári. 2006a. Bandit based monte-carlo planning. In European conference on machine learning, pages 282–293. Springer. find
  16. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948.
  17. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, and 1 others. 2024. Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems. In Proceedings of the 62nd Annual Meeting…
  18. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the MATH dataset. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
  19. Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. 2025a. Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model. Preprint, arXiv:2503.24290.
  20. Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. 2025b. Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model. arXiv preprint arXiv:2503.24290.
  21. Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, and 1 others. 2024. Openai o1 system card. arXiv preprint arXiv:2412.16720.
  22. P. I. Jaffe, R. A. Poldrack, R. J. Schafer, and others. 2023. Modelling human behaviour in cognitive tasks with latent dynamical systems. Nature Human Behaviour, 7:986–1000. find
  23. Daniel Kahneman. 2011. Thinking, Fast and Slow. Farrar, Straus and Giroux, New York, NY. find
  24. Zilin Kang, Chonghua Liao, Tingqiang Xu, and Huazhe Xu. 2025. Entropy regularizing activation: Boosting continuous control, large language models, and image classification with activation as entropy constraints. arXiv preprint arXiv:2510.08549. — [arXiv:2510.08549](https://arxiv.org/abs/2510.08549) find
  25. Levente Kocsis and Csaba Szepesvári. 2006b. Bandit based monte-carlo planning. In Machine Learning: ECML 2006, pages 282–293, Berlin, Heidelberg. Springer Berlin Heidelberg. find
  26. Samuel Kotz and Norman L. Johnson, editors. 1992. Breakthroughs in Statistics: Methodology and Distribution. Springer New York, New York, NY. find
  27. Fong-Lok Lee and Rex Heyworth. 2000. Problem complexity: A measure of problem difficulty in algebra by using computer. EDUCATION JOURNAL-HONG KONG-CHINESE UNIVERSITY OF HONG KONG-, 28(1):85–108. find
  28. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, and 1 others. 2022. Solving quantitative reasoning problems with language models. Advances in Neural Information Processing Systems, 35:3843–3857…
  29. Jia Li, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Huang, Kashif Rasul, Longhui Yu, Albert Q. Jiang, Ziju Shen, and 1 others. 2024. Numinamath: The largest public dataset in ai4maths with 860k pairs of competition math problems and solutions. https://huggingface. co/datase… find
  30. Chonghua Liao, Ke Wang, Yuchuan Wu, Fei Huang, and Yongbin Li. 2025. Moa: Multi-objective alignment for role-playing agents. arXiv preprint arXiv:2512.09756. find
  31. Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. 2025. Understanding r1-zero-like training: A critical perspective. arXiv preprint arXiv:2503.20783.
  32. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. 2023. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255.
  33. Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, and Song-Chun Zhu. 2021. Inter-GPS: Interpretable geometry problem solving with formal language and symbolic reasoning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Int…
  34. Rui Miao, Yixin Liu, Yili Wang, Xu Shen, Yue Tan, Yiwei Dai, Shirui Pan, and Xin Wang. 2025. Blindguard: Safeguarding llm-based multi-agent systems under unknown attacks. arXiv preprint arXiv:2508.08127.
  35. Davide Paglieri, Bartłomiej Cupiał, and 1 others. 2025. BALROG: Benchmarking agentic LLM and VLM reasoning on games. In The Thirteenth International Conference on Learning Representations. find
  36. Zhenting Qi, Mingyuan Ma, Jiahang Xu, Li Lyna Zhang, Fan Yang, and Mao Yang. 2024. Mutual reasoning makes smaller llms stronger problem-solvers. arXiv preprint arXiv:2408.06195.
  37. Yiwei Qin, Xuefeng Li, Haoyang Zou, Yixiu Liu, Shijie Xia, Zhen Huang, Yixin Ye, Weizhe Yuan, Hector Liu, Yuanzhi Li, and 1 others. 2024. O1 replication journey: A strategic progress report–part 1. arXiv preprint arXiv:2410.18982.
  38. Qwen Team. 2024. Qwen2.5: A party of foundation models. find
  39. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. 2024. GPQA: A graduate-level google-proof q&a benchmark. In First Conference on Language Modeling.
  40. Stuart Russell and Eric Wefald. 1991. Principles of metareasoning. Artificial intelligence, 49(1-3):361– 395. find
  41. Alejandro Salado and Roshanak Nilchiani. 2014. The concept of problem complexity. Procedia Computer Science, 28:539–546. 2014 Conference on Systems Engineering Research. find
  42. John Schulman, Xi Chen, and Pieter Abbeel. 2017. Equivalence between policy gradients and soft qlearning. arXiv preprint arXiv:1704.06440. find
  43. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, and 1 others. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300.
  44. Xu Shen, Yixin Liu, Yiwei Dai, Yili Wang, Rui Miao, Yue Tan, Shirui Pan, and Xin Wang. 2025. Understanding the information propagation effects of communication topologies in llm-based multi-agent systems. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, page…
  45. Yuhao Shen, Tianyu Liu, Junyi Shen, Jinyang Wu, Quan Kong, Li Huan, and Cong Wang. 2026. Double: Breaking the acceleration limit via double retrieval speculative parallelism. arXiv:2601.05524. arXiv preprint Richard S Sutton, Andrew G Barto, and 1 others. 1998. Reinforcement learning: An introductio… find
  46. Xiangru Tang, Tianrui Qin, Tianhao Peng, Ziyang Zhou, Daniel Shao, Tingting Du, Xinming Wei, Peng Xia, Fang Wu, He Zhu, and 1 others. 2025. Agent kb: Leveraging cross-domain experience for agentic problem solving. arXiv preprint arXiv:2507.06229. — [arXiv:2507.06229](https://arxiv.org/abs/2507.06229) find
  47. Zhengyang Tang, Xingxing Zhang, Benyou Wang, and Furu Wei. 2024. Mathscale: Scaling instruction tuning for mathematical reasoning. arXiv preprint arXiv:2403.02884.
  48. Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, and 1 others. 2025. Kimi k1. 5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501.12599. — Kimi k1.5: Scaling Reinforcement Learning with LLMs
  49. Ziyu Wan, Yunxiang LI, Xiaoyu Wen, Yan Song, Hanjing Wang, Linyi Yang, Mark Schmidt, Jun Wang, Weinan Zhang, Shuyue Hu, and Ying Wen. 2025. ReMA: Learning to meta-think for LLMs with multi-agent reinforcement learning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. find
  50. Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. 2024a. Measuring multimodal mathematical reasoning with MATH-vision dataset. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
  51. Shenzhi Wang, Le Yu, Chang Gao, Chujie Zheng, and 1 others. 2025a. Beyond the 80/20 rule: High-entropy minority tokens drive effective reinforcement learning for llm reasoning. arXiv preprint arXiv:2506.01939.
  52. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations.
  53. Yiping Wang, Qing Yang, Zhiyuan Zeng, and 1 others. 2025b. Reinforcement learning for reasoning in large language models with one training example. arXiv preprint arXiv:2504.20571.
  54. Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, and 1 others. 2024b. Mmlu-pro: A more robust and challenging multi-task language understanding benchmark. arXiv preprint arXiv:2406.01574.
  55. Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zengqi Wen, and Jianhua Tao. 2024. Beyond examples: High-level automated reasoning paradigm in in-context learning via mcts. arXiv preprint arXiv:2411.18478. — [arXiv:2411.18478](https://arxiv.org/abs/2411.18478) find
  56. Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, and Gao Huang. 2025. Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? arXiv preprint arXiv:2504.13837.
  57. Jinyang Wu, Shuo Yang, Changpeng Yang, Yuhao Shen, Shuai Zhang, Zhengqi Wen, and Jianhua Tao. 2026a. Spark: Strategic policy-aware exploration via dynamic branching for long-horizon agentic learning. arXiv preprint arXiv:2601.20209.
  58. Weihao Zeng, Yuzhen Huang, Qian Liu, Wei Liu, Keqing He, Zejun Ma, and Junxian He. 2025. Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild. arXiv preprint arXiv:2503.18892.
  59. Jinyang Wu, Guocheng Zhai, Ruihan Jin, Jiahao Yuan, Yuhao Shen, Shuai Zhang, Zhengqi Wen, and Jianhua Tao. 2026b. Atlas: Orchestrating heterogeneous models and tools for multi-domain complex reasoning. arXiv preprint arXiv:2601.03872.
  60. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, and 1 others. 2025. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186. Springer.
  61. Jinyang Wu, Shuai Zhang, Feihu Che, Mingkuan Feng, Pengpeng Shao, and Jianhua Tao. 2025. Pandora’s box or aladdin’s lamp: A comprehensive analysis revealing the role of RAG noise in large language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol…
  62. Fangzhi Xu, Hang Yan, Qiushi Sun, Jinyang Wu, Zixian Huang, Muye Huang, Jingyang Gong, Zichen Ding, Kanzhi Cheng, Yian Wang, and 1 others. 2026. Odysseyarena: Benchmarking large language models for long-horizon, active and inductive interactions. arXiv preprint arXiv:2602.05843. — [arXiv:2602.05843](https://arxiv.org/abs/2602.05843) find
  63. An Yang, Beichen Zhang, Binyuan Hui, and 1 others. 2024a. Qwen2. 5-math technical report: Toward mathematical expert model via self-improvement. arXiv preprint arXiv:2409.12122. — Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
  64. Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E Gonzalez, and Bin Cui. 2024b. Buffer of thoughts: Thought-augmented reasoning with large language models. Advances in Neural Information Processing Systems, 37:113519–113544.
  65. Xiaotian Zhang, Chunyang Li, Yi Zong, Zhengyu Ying, Liang He, and Xipeng Qiu. 2023. Evaluating the performance of large language models on gaokao benchmark. arXiv preprint arXiv:2305.12474.
  66. Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Jie Jiang, and Bin Cui. 2024. Retrieval-augmented generation for ai-generated content: A survey. arXiv preprint arXiv:2402.19473.
  67. Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangchi Feng, Dongdong Kuang, and Yuwen Xiong. 2025. Easyr1: An efficient, scalable, multi-modality rl training framework. https://github.com/hiyouga/ EasyR1. ↗
  68. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. 2024. Language agent tree search unifies reasoning, acting, and planning in language models. In Forty-first International Conference on Machine Learning. find
  69. Yuxin Zuo, Kaiyan Zhang, Shang Qu, Li Sheng, Xuekai Zhu, Biqing Qi, Youbang Sun, Ganqu Cui, Ning Ding, and Bowen Zhou. 2025. Ttrl: Test-time reinforcement learning. arXiv preprint arXiv:2504.16084.
  70. Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao. 2021. Mastering atari games with limited data. In Advances in Neural Information Processing Systems, volume 34, pages 25476–25488. Curran Associates, Inc. find
  71. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, and 1 others. 2025. Dapo: An open-source llm reinforcement learning system at scale. arXiv preprint arXiv:2503.14476.
  72. Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, and 1 others. 2024. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In Proceedings of CVPR.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wu-2026-templaterl-structured-template-guided-reinforcement/