Elamur Research⌕
☰
home / papers / 2026 / PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning

PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning

Paper · 2026 · Rongchuan Mu, Zexin Wang, Qianyu Wang et al.

Summary

Large Reasoning Models handle complex problems well but often ignore explicit constraints in user instructions. Earlier RLVR-based fixes have three weaknesses: they depend on teacher models, they are prone to reward hacking, and they allow final answers that contradict the model's own reasoning. PARIF is a teacher-free, two-stage RLVR curriculum that addresses all three.

Stage I trains only on code-checkable (hard) constraints. Dynamic constraint weighting shifts reward toward constraints the model has not yet mastered. A correctness proxy judges overall answer quality to discourage shortcut exploitation.

Stage II adds Decoupled-GRPO. This method passes outcome rewards back to the reasoning trace according to how well that trace agrees with the answer, measured by posterior probability.

The authors also build a 26,000-instruction dataset with diverse hard and soft constraints, generated automatically from open seed questions. On models from 1.7B to 8B parameters, PARIF substantially improves instruction following while slightly improving general reasoning. It therefore extends the Pareto frontier instead of trading one ability for the other. Code and models are open-sourced.

Key points

  • Dataset: 26,000 constrained instructions built from 52k English seed questions (ShareGPT, Orca-Chat, LMSYS-Chat, Alpaca, WildChat) plus DeepScaleR math problems. It uses 108 hard-constraint templates in six categories with Python verifiers, plus VerIF-style soft constraints with checklists, and is released under CC BY-NC 4.0.
  • Results: The 7B model gains 21.25% (relative) on six IF benchmarks and 5.80% on six general reasoning benchmarks. The 8B model gains 5.47% and 1.58%, and beats DeepSeek-V3 on the IF tasks.
  • Dynamic weighting: Stage I uses a maintenance margin δ = 0.7 so that fully mastered constraints still provide some reward signal.
  • Ablations:
    • Decoupled-GRPO outperforms plain outcome-reward GRPO on most tasks.
    • Gains hold across different verifiers: Qwen3-32B, Qwen2.5-14B-Instruct and Meta-Llama-3-70B-Instruct.
    • Best-of-N scaling on IFBench and AIME25 indicates that generative diversity is preserved.
  • Limitations: Training covers only single-turn instructions, relies on a costly Qwen3-32B verifier, and is tested only at the 1.7B–8B scale.

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

🎓Harbin Institute of TechnologyHarbin · China158 works · 28,841 citations🔬Peng Cheng LaboratoryShenzhen · China38 works · 2,504 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Rongchuan Mu, Zexin Wang, Qianyu Wang, MingHua Ma, Zekun Wang, Ming Liu and Bing Qin. 2026. PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1136

@inproceedings{mu2026parif,
  title = {{PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning}},
  author = {Rongchuan Mu, Zexin Wang, Qianyu Wang, MingHua Ma, Zekun Wang, Ming Liu and Bing Qin},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1136},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1136}
}

References

69 references, 32 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Kaikai An, Li Sheng, Ganqu Cui, Shuzheng Si, Ning Ding, Yu Cheng, and Baobao Chang. 2025. Ultraif: Advancing instruction following from the wild. arXiv preprint arXiv:2502.04153.
  2. Anthropic. 2025. Claude 3.7 sonnet and claude code. Accessed: 2025-05-06. find
  3. Jose A Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter. 2019. Rudder: Return decomposition for delayed rewards. Advances in Neural Information Processing Systems, 32. find
  4. Yapei Chang, Yekyung Kim, Michael Krumdick, Amir Zadeh, Chuan Li, Chris Tanner, and Mohit Iyyer. 2025. Bleuberi: Bleu is a surprisingly effective reward for instruction following. arXiv preprint arXiv:2505.11080. find
  5. Jiale Cheng, Xiao Liu, Cunxiang Wang, Xiaotao Gu, Yida Lu, Dan Zhang, Yuxiao Dong, Jie Tang, Hongning Wang, and Minlie Huang. 2024. Spar: Self-play with tree-search refinement to improve instruction-following in large language models. arXiv preprint arXiv:2412.11605. — [arXiv:2412.11605](https://arxiv.org/abs/2412.11605) find
  6. DeepSeek-AI. 2024. Deepseek-v3 technical report. Preprint, arXiv:2412.19437. find
  7. Guanting Dong, Keming Lu, Chengpeng Li, Tingyu Xia, Bowen Yu, Chang Zhou, and Jingren Zhou. 2024. Self-play with execution feedback: Improving instruction-following capabilities of large language models. arXiv preprint arXiv:2406.13542. find
  8. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and 1 others. 2024. The llama 3 herd of models. arXiv e-prints, pages arXiv±2407.
  9. Ben Elder, Evelyn Duesterwald, and Vinod Muthusamy. 2025. Boosting instruction following at scale. arXiv preprint arXiv:2510.14842. — [arXiv:2510.14842](https://arxiv.org/abs/2510.14842) find
  10. Shahul Es. 2023. Orca-chat: explanation-style chat dataset. find
  11. A high-quality Tingchen Fu, Jiawei Gu, Yafu Li, Xiaoye Qu, and Yu Cheng. 2025. Scaling reasoning, losing control: Evaluating instruction following in large reasoning models. arXiv preprint arXiv:2505.14810.
  12. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 o… — The Llama 3 Herd of Models
  13. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025a. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948.
  14. Xu Guo, Tianyi Liang, Tong Jian, Xiaogui Yang, Ling-I Wu, Chenhui Li, Zhihui Lu, Qipeng Guo, and Kai Chen. 2025b. Ifdecorator: Wrapping instruction following reinforcement learning with verifiable rewards. arXiv preprint arXiv:2508.04632. — [arXiv:2508.04632](https://arxiv.org/abs/2508.04632) find
  15. Qianyu He, Jie Zeng, Qianxi He, Jiaqing Liang, and Yanghua Xiao. 2024a. From complex to simple: Enhancing multi-constraint complex instruction following ability of large language models. arXiv preprint arXiv:2404.15846. find
  16. Yun He, Di Jin, Chaoqi Wang, Chloe Bi, Karishma Mandyam, Hejia Zhang, Chen Zhu, Ning Li, Tengyu Xu, Hongjiang Lv, and 1 others. 2024b. Multi-if: Benchmarking llms on multi-turn and multilingual instructions following. arXiv preprint arXiv:2410.15553. — [arXiv:2410.15553](https://arxiv.org/abs/2410.15553) find
  17. Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Selina Peng, Beibin Li, and 1 others. 2025a. Rubric-based benchmarking and reinforcement learning for advancing llm instruction following. arXiv preprint arXiv:2511.10507. — [arXiv:2511.10507](https://arxiv.org/abs/2511.10507) find
  18. Zirui He, Haiyan Zhao, Yiran Qiao, Fan Yang, Ali Payani, Jing Ma, and Mengnan Du. 2025b. Saif: A sparse autoencoder framework for interpreting and steering instruction following of language models. arXiv preprint arXiv:2502.11356. — [arXiv:2502.11356](https://arxiv.org/abs/2502.11356) find
  19. Juyeon Heo, Christina Heinze-Deml, Oussama Elachqar, Kwan Ho Ryan Chan, Shirley Ren, Udhay Nallasamy, Andy Miller, and Jaya Narain. 2024. Do llms ªknowº internally when they follow instructions? arXiv preprint arXiv:2410.14516. — [arXiv:2410.14516](https://arxiv.org/abs/2410.14516) find
  20. Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, and Hado Van Hasselt. 2019. Multi-task deep reinforcement learning with popart. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 3796±3803. find
  21. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276.
  22. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. 2024. Livecodebench: Holistic and contamination free evaluation of large language models for code. arXiv preprint arXiv:2403.07974.
  23. Daniel Jaroslawicz, Brendan Whiting, Parth Shah, and Karime Maamari. 2025. How many instructions can llms follow at once? arXiv preprint arXiv:2507.11538. — [arXiv:2507.11538](https://arxiv.org/abs/2507.11538) find
  24. Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, and Wei Wang. 2024. Followbench: A multi-level fine-grained constraints following benchmark for large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computa…
  25. Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. 2023. Understanding the effects of rlhf on llm generalisation and diversity. arXiv preprint arXiv:2310.06452.
  26. Xiaoyu Kong, Junguang Jiang, Bin Liu, Ziru Xu, Han Zhu, Jian Xu, Bo Zheng, Jiancan Wu, and Xiang Wang. 2025. Think before recommendation: Autonomous reasoning-enhanced recommender. arXiv preprint arXiv:2510.23077. — [arXiv:2510.23077](https://arxiv.org/abs/2510.23077) find
  27. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 2023. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th Symposium on Operating Systems Principles, pages 611±626.
  28. Hynek Kydlíček. 2025. Math-verify: A library for verifying mathematical answers. https://github.com/huggingface/Math-Verify. ↗
  29. Nathan Lambert, Hamish Ivison, Daeyoung Jung, Sage Oore, Alisa Liu, Jacob Morrison, Nouha Dziri, Ari Holtzman, Noah A. Smith, Hannaneh Hajishirzi, and 1 others. 2024a. Tulu 3: Pushing frontiers in open post-training. arXiv preprint arXiv:2411.15124.
  30. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, and 1 others. 2024b. Tulu 3: Pushing frontiers in open language model post-training. arXiv preprint arXiv:2411.15124.
  31. Xiaomin Li, Zhou Yu, Zhiwei Zhang, Xupeng Chen, Ziji Zhang, Yingying Zhuang, Narayanan Sadagopan, and Anurag Beniwal. 2025. When thinking fails: The pitfalls of reasoning for instruction-following in llms. arXiv preprint arXiv:2505.11423. — [arXiv:2505.11423](https://arxiv.org/abs/2505.11423) find
  32. Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023. Let’s verify step by step. arXiv preprint arXiv:2305.20050.
  33. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others. 2024a. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. find
  34. Yilun Liu, Shimin Tao, Xiaofeng Zhao, Ming Zhu, Wenbing Ma, Junhao Zhu, Chang Su, Yutai Hou, Miao Zhang, Min Zhang, and 1 others. 2024b. Coachlm: Automatic instruction revisions improve the data quality in llm instruction tuning. In 2024 IEEE 40th International Conference on Data Engineering (ICDE),… find
  35. Renze Lou, Kai Zhang, and Wenpeng Yin. 2024. Large language model instruction following: A survey of progresses and challenges. Computational Linguistics, 50(3):1053±1095.
  36. Michael Luo, Sijun Tan, Justin Wong, Xiaoxiang Shi, William Y Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Tianjun Zhang, Li Erran Li, and 1 others. 2025. Deepscaler: Surpassing o1-preview with a 1.5 b model by scaling rl. Notion Blog. math ai. 2025. find
  37. Maxwell-Jia. 2024. Aime 2024 dataset. find
  38. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730±27744.
  39. Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, and Juanzi Li. 2025. Verif: Verification engineering for reinforcement learning in instruction following. arXiv preprint arXiv:2506.09942.
  40. Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, and Hannaneh Hajishirzi. 2025. Generalizing verifiable instruction following. arXiv preprint arXiv:2507.02833.
  41. Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, and Juanzi Li. 2025. Constraint back-translation improves complex instruction following of large language models. Preprint, arXiv:2410.24175. — [arXiv:2410.24175](https://arxiv.org/abs/2410.24175) find
  42. Cheng Qian, Emre Can Acikgoz, Qi He, Hongru Wang, Xiusi Chen, Dilek Hakkani-Tür, Gokhan Tur, and Heng Ji. 2025. Toolrl: Reward is all tool learning needs. arXiv preprint arXiv:2504.13958. find
  43. Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, and Dong Yu. 2024a. Infobench: Evaluating instruction following ability in large language models. arXiv preprint arXiv:2401.03601.
  44. Yulei Qin, Gang Li, Zongyi Li, Zihan Xu, Yuchen Shi, Zhekai Lin, Xiao Cui, Ke Li, and Xing Sun. 2025a. Incentivizing reasoning for advanced instruction-following of large language models. arXiv preprint arXiv:2506.01413. — [arXiv:2506.01413](https://arxiv.org/abs/2506.01413) find
  45. Yulei Qin, Gang Li, Zongyi Li, Zihan Xu, Yuchen Shi, Zhekai Lin, Xiao Cui, Ke Li, and Xing Sun. 2025b. Incentivizing reasoning for advanced instruction-following of large language models. Preprint, arXiv:2506.01413. — [arXiv:2506.01413](https://arxiv.org/abs/2506.01413) find
  46. Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, and Xing Sun. 2024b. Unleashing the power of data tsunami: A comprehensive survey on data assessment and selection for instruction tuning of language models. Preprint, arXiv:2408.02085. — [arXiv:2408.02085](https://arxiv.org/abs/2408.02085) find
  47. Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, and 25 others. 2025. Qwen2.5 technical report. Preprint, arXiv:2412.15115. find
  48. Qwen Team. 2025. Qwen3-coder technical report. arXiv preprint arXiv:2506.05230. — [arXiv:2506.05230](https://arxiv.org/abs/2506.05230) find
  49. Qingyu Ren, Qianyu He, Bowei Zhang, Jie Zeng, Jiaqing Liang, Yanghua Xiao, Weikang Zhou, Zeye Sun, and Fei Yu. 2025. Instructions are all you need: Self-supervised reinforcement learning for instruction following. arXiv preprint arXiv:2510.14420.
  50. ByteDance Seed, Jiaze Chen, Tiantian Fan, Xin Liu, Lingjun Liu, Zhiqi Lin, Mingxuan Wang, Chengyi Wang, Xiangpeng Wei, Wenyuan Xu, and 1 others. 2025. Seed1. 5-thinking: Advancing superb reasoning models with reinforcement learning. arXiv preprint arXiv:2504.13914. — Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
  51. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, and 1 others. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300. shareAI. 2023. Sharegpt-chinese-english-90k b…
  52. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Zhan, Yachu Gao, Ding Xi, Shihan Dou, Yifan Wang, Rui Zheng, Enyu Tao, and 1 others. 2024. Hybridflow: A flexible and efficient rlhf framework. arXiv preprint arXiv:2409.19256.
  53. Haoran Sun, Lixin Liu, Junjie Li, Fengyu Wang, Baohua Dong, Ran Lin, and Ruohui Huang. 2024. Conifer: Improving complex constrained instruction-following ability of large language models. Preprint, arXiv:2404.02823.
  54. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388.
  55. Richard Stuart Sutton. 1984. Temporal credit assignment in reinforcement learning. University of Massachusetts Amherst. find
  56. Dongkeun Yoon, Seungone Kim, Sohee Yang, Sunkyoung Kim, Soyeon Kim, Yongil Kim, Eunbi Choi, Yireun Kim, and Minjoon Seo. 2025. Reasoning models better express their confidence. arXiv preprint arXiv:2505.14489. — [arXiv:2505.14489](https://arxiv.org/abs/2505.14489) find
  57. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca. ↗
  58. Doubao Team. 2025. https://team.doubao.com/en/ special/doubao_1_5_pro. ↗
  59. 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
  60. Qwen Team. 2024. Qwen2.5: A party of foundation models. find
  61. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, and 1 others. 2025. Dapo: An open-source llm reinforcement learning system at scale. arXiv preprint arXiv:2503.14476.
  62. Tao Zhang, Chenglin Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, and 1 others. 2025. Cfbench: A comprehensive constraints-following benchmark for llms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics…
  63. Praveen Venkateswaran and Danish Contractor. 2025. Spotlight your instructions: Instruction-following with dynamic attention steering. arXiv preprint arXiv:2505.12025.
  64. Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. 2024. Wildchat: 1m chatgpt interaction logs in the wild. arXiv preprint arXiv:2405.01470.
  65. Vijay Viswanathan, Yanchao Sun, Shuang Ma, Xiang Kong, Meng Cao, Graham Neubig, and Tongshuang Wu. 2025. Checklists are better than reward models for aligning language models. Preprint, arXiv:2507.18624.
  66. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P Xing, and 1 others. 2023. Lmsys-chat-1m: A large-scale real-world llm conversation dataset. arXiv preprint arXiv:2309.11998.
  67. Chenyang Wang, Liang Wen, Shousheng Jia, Xiangzheng Zhang, and Liang Xu. 2025. Light-if: Endowing llms with generalizable reasoning via preview and self-checking for complex instruction following. arXiv preprint arXiv:2508.03178. — [arXiv:2508.03178](https://arxiv.org/abs/2508.03178) find
  68. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911.
  69. Junqiao Wang, Zeng Zhang, Yangfan He, Zihao Zhang, Xinyuan Song, Yuyang Song, Tianyu Shi, Yuchen Li, Hengyuan Xu, Kunyu Wu, and 1 others. 2024a. Enhancing code llms with reinforcement learning in code generation: A survey. arXiv preprint arXiv:2412.20367. — [arXiv:2412.20367](https://arxiv.org/abs/2412.20367) find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/mu-2026-parif-pushing-pareto-frontier-instruction/