


@inproceedings{agarwal-etal:onpolicy,
  title={On-policy distillation of language models: Learning from self-generated mistakes},
  author={Agarwal, Rishabh and Vieillard, Nino and Zhou, Yongchao and Stanczyk, Piotr and Ramos Garea, Sabela and Geist, Matthieu and Bachem, Olivier},
  booktitle={International Conference on Learning Representations},
  volume={2024},
  pages={21246--21263},
  year={2024}
}

@inproceedings{mcallister-etal:flow,
  title={Flow matching policy gradients},
  author={McAllister, David and Ge, Songwei and Yi, Brent and Kim, Chung Min and Weber, Ethan and Choi, Hongsuk and Feng, Haiwen and Kanazawa, Angjoo},
  booktitle={International Conference on Learning Representations},
  volume={2026},
  pages={36352--36372},
  year={2026}
}

@article{liu-etal:flow,
  title={Flow-grpo: Training flow matching models via online rl},
  author={Liu, Jie and Liu, Gongye and Liang, Jiajun and Li, Yangguang and Liu, Jiaheng and Wang, Xintao and Wan, Pengfei and Zhang, Di and Ouyang, Wanli},
  journal={Advances in neural information processing systems},
  volume={38},
  pages={40783--40818},
  year={2026}
}

@article{xiao-etal:ordinary,
  title={Ordinary Differential Equations in Vision and Language},
  author={Xiao, Tong and Ruan, Junhao and Li, Bei and Yu, Zhengtao and Zhang, Min and Zhu, Jingbo},
  year={2026},
  publisher={TechRxiv}
}


@article{zhou-etal:learning,
  title={Learning implicit credit assignment for cooperative multi-agent reinforcement learning},
  author={Zhou, Meng and Liu, Ziyu and Sui, Pengwei and Li, Yixuan and Chung, Yuk Ying},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={11853--11864},
  year={2020}
}

@book{sutton-etal:temporal,
  title={Temporal credit assignment in reinforcement learning},
  author={Sutton, Richard Stuart},
  year={1984},
  publisher={University of Massachusetts Amherst}
}

@inproceedings{liu-etal:agentic,
  title={Agentic reinforcement learning with implicit step rewards},
  author={Liu, Xiaoqian and Wang, Ke and Wu, Yuchuan and Huang, Fei and Li, Yongbin and Jiao, Jianbin and Zhang, Junge},
  booktitle={International Conference on Learning Representations},
  volume={2026},
  pages={129271--129291},
  year={2026}
}

@misc{huo-etal:learning,
      title={Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning}, 
      author={Yifu Huo and Shunjie Xing and Chenglong Wang and Peinan Feng and Qiaozhi He and Yan Ding and Anxiang Ma and Yuxin Gao and Tongran Liu and Tong Xiao and Jingbo Zhu},
      year={2026},
      journal={arXiv preprint arXiv:2608.08255}
}

@article{xue-etal:dancegrpo,
  title={Dancegrpo: Unleashing grpo on visual generation},
  author={Xue, Zeyue and Wu, Jie and Gao, Yu and Kong, Fangyuan and Zhu, Lingting and Chen, Mengzhao and Liu, Zhiheng and Liu, Wei and Guo, Qiushan and Huang, Weilin and others},
  journal={arXiv preprint arXiv:2505.07818},
  year={2025}
}

@inproceedings{black-etal:training,
  title={Training diffusion models with reinforcement learning},
  author={Black, Kevin and Janner, Michael and Du, Yilun and Kostrikov, Ilya and Levine, Sergey},
  booktitle={International Conference on Learning Representations},
  volume={2024},
  pages={4965--4987},
  year={2024}
}

@inproceedings{sohl-etal:deep,
  title={Deep unsupervised learning using nonequilibrium thermodynamics},
  author={Sohl-Dickstein, Jascha and Weiss, Eric and Maheswaranathan, Niru and Ganguli, Surya},
  booktitle={International conference on machine learning},
  pages={2256--2265},
  year={2015},
  organization={pmlr}
}

@article{croitoru-etal:diffusion,
  title={Diffusion models in vision: A survey},
  author={Croitoru, Florinel-Alin and Hondru, Vlad and Ionescu, Radu Tudor and Shah, Mubarak},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={45},
  number={9},
  pages={10850--10869},
  year={2023},
  publisher={Ieee}
}

@article{yang-etal:diffusion,
  title={Diffusion models: A comprehensive survey of methods and applications},
  author={Yang, Ling and Zhang, Zhilong and Song, Yang and Hong, Shenda and Xu, Runsheng and Zhao, Yue and Zhang, Wentao and Cui, Bin and Yang, Ming-Hsuan},
  journal={ACM computing surveys},
  volume={56},
  number={4},
  pages={1--39},
  year={2023},
  publisher={ACM New York, NY, USA}
}

@article{chang-etal:efficient,
  title={Efficient prompting methods for large language models: A survey},
  author={Chang, Kaiyan and Xu, Songcheng and Wang, Chenglong and Luo, Yingfeng and Liu, Xiaoqian and Xiao, Tong and Zhu, Jingbo},
  journal={arXiv preprint arXiv:2404.01077},
  year={2024}
}

@article{fan-etal:dpok,
  title={Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models},
  author={Fan, Ying and Watkins, Olivia and Du, Yuqing and Liu, Hao and Ryu, Moonkyung and Boutilier, Craig and Abbeel, Pieter and Ghavamzadeh, Mohammad and Lee, Kangwook and Lee, Kimin},
  journal={Advances in neural information processing systems},
  volume={36},
  pages={79858--79885},
  year={2023}
}

@article{lee-etal:aligning,
  title={Aligning text-to-image models using human feedback},
  author={Lee, Kimin and Liu, Hao and Ryu, Moonkyung and Watkins, Olivia and Du, Yuqing and Boutilier, Craig and Abbeel, Pieter and Ghavamzadeh, Mohammad and Gu, Shixiang Shane},
  journal={arXiv preprint arXiv:2302.12192},
  year={2023}
}

@inproceedings{wang-etal:probing,
  title={Probing preference representations: A multi-dimensional evaluation and analysis method for reward models},
  author={Wang, Chenglong and Huo, Yifu and Gan, Yang and Mu, Yongyu and He, Qiaozhi and Yang, Murun and Li, Bei and Zhang, Chunliang and Liu, Tongran and Ma, Anxiang and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={39},
  pages={33404--33412},
  year={2026}
}

@inproceedings{frick-etal:ppe,
  title={How to evaluate reward models for rlhf},
  author={Frick, Evan and Li, Tianle and Chen, Connor and Chiang, Wei-Lin and Angelopoulos, Anastasios and Jiao, Jiantao and Zhu, Banghua and Gonzalez, Joseph E and Stoica, Ion},
  booktitle={International Conference on Learning Representations},
  volume={2025},
  pages={18128--18163},
  year={2025}
}


@inproceedings{tan-etal:judgebench,
  title={Judgebench: A benchmark for evaluating llm-based judges},
  author={Tan, Sijun and Zhuang, Siyuan and Montgomery, Kyle and Tang, William and Cuadron, Alejandro and Wang, Chenguang and Popa, Raluca and Stoica, Ion},
  booktitle={International Conference on Learning Representations},
  volume={2025},
  pages={63277--63303},
  year={2025}
}

@inproceedings{zhou-etal:rmb,
  title={Rmb: Comprehensively benchmarking reward models in llm alignment},
  author={Zhou, Enyu and Zheng, Guodong and Wang, Binghai and Xi, Zhiheng and Dou, Shihan and Bao, Rong and Shen, Wei and Xiong, Limao and Fan, Jessica and Mou, Yurong and others},
  booktitle={International Conference on Learning Representations},
  volume={2025},
  pages={26543--26589},
  year={2025}
}

@inproceedings{liu-etal:rm-bench,
  title={Rm-bench: Benchmarking reward models of language models with subtlety and style},
  author={Liu, Yantao and Yao, Zijun and Min, Rui and Cao, Yixin and Hou, Lei and Li, Juanzi},
  booktitle={International Conference on Learning Representations},
  volume={2025},
  pages={44323--44355},
  year={2025}
}

@inproceedings{malik-etal:rewardbench,
  title={Rewardbench 2: Advancing reward model evaluation},
  author={Malik, Saumya and Pyatkin, Valentina and Land, Sander and Morrison, Jacob and Smith, Noah and Hajishirzi, Hanna and Lambert, Nathan},
  booktitle={International Conference on Learning Representations},
  volume={2026},
  pages={144839--144866},
  year={2026}
}

@inproceedings{lambert-etal:Rewardbench,
  title={Rewardbench: Evaluating reward models for language modeling},
  author={Lambert, Nathan and Pyatkin, Valentina and Morrison, Jacob and Miranda, LJ and Lin, Bill Yuchen and Chandu, Khyathi and Dziri, Nouha and Kumar, Sachin and Zick, Tom and Choi, Yejin and others},
  booktitle={Findings of the Association for Computational Linguistics: NAACL 2025},
  pages={1755--1797},
  year={2025}
}

@misc{wang-etal:rrc,
      title={RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction}, 
      author={Chenglong Wang and Ziming Zhu and Yifu Huo and Bei Li and Qiaozhi He and Yan Ding and Xiaoyang Hao and Yuxin Gao and Tianhua Zhou and Xiaojia Chang and Tongran Liu and Jingbo Zhu},
      year={2026},
      eprint={2608.06310},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2608.06310}, 
}

@article{monea-etal:llms,
  title={Llms are in-context reinforcement learners},
  author={Monea, Giovanni and Bosselut, Antoine and Brantley, Kiant{\'e} and Artzi, Yoav},
  year={2024}
}


@inproceedings{song-etal:trial,
  title={Trial and error: Exploration-based trajectory optimization of LLM agents},
  author={Song, Yifan and Yin, Da and Yue, Xiang and Huang, Jie and Li, Sujian and Lin, Bill Yuchen},
  booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={7584--7600},
  year={2024}
}

@inproceedings{fu-etal:agentrefine,
  title={Agentrefine: Enhancing agent generalization through refinement tuning},
  author={Fu, Dayuan and He, Keqing and Wang, Yejie and Hong, Wentao and Gongque, Zhuoma and Zeng, Weihao and Wang, Wei and Wang, Jingang and Cai, Xunliang and Xu, Weiran},
  booktitle={International Conference on Learning Representations},
  volume={2025},
  pages={65185--65204},
  year={2025}
}

@article{yuan-etal:agent,
  title={Agent-r: Training language model agents to reflect via iterative self-training},
  author={Yuan, Siyu and Chen, Zehui and Xi, Zhiheng and Ye, Junjie and Du, Zhengyin and Chen, Jiecao},
  journal={arXiv preprint arXiv:2501.11425},
  year={2025}
}

@inproceedings{wang-etal:steca,
  title={Steca: Step-level trajectory calibration for llm agent learning},
  author={Wang, Hanlin and Wang, Jian and Leong, Chak Tou and Li, Wenjie},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2025},
  pages={11597--11614},
  year={2025}
}

@inproceedings{liu-etal:pract,
  title={Pract: Optimizing principled reasoning and acting of llm agent},
  author={Liu, Zhiwei and Yao, Weiran and Zhang, Jianguo and Liu, Zuxin and Yang, Liangwei and RN, Rithesh and Lan, Tian and Zhu, Ming and Tan, Juntao and Kokane, Shirley and others},
  booktitle={Proceedings of the 28th Conference on Computational Natural Language Learning},
  pages={442--446},
  year={2024}
}

@article{shinn-etal:reflexion,
  title={Reflexion: Language agents with verbal reinforcement learning},
  author={Shinn, Noah and Cassano, Federico and Gopinath, Ashwin and Narasimhan, Karthik and Yao, Shunyu},
  journal={Advances in neural information processing systems},
  volume={36},
  pages={8634--8652},
  year={2023}
}


@article{fu-etal:self,
      title={Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember}, 
      author={Zenghuang Fu and Zhaoyang Li and Qiuyuan Ai and Haoyu Wu and Minghui Wu and Chenxu Zhao and Ante Wang and Guannan He and Changwei Wang},
      journal={arXiv preprint arXiv:2607.29468},
      year={2026},
}


@inproceedings{wang-etal:reinforcement,
  title={Reinforcement learning for self-improving agent with skill library},
  author={Wang, Jiongxiao and Yan, Qiaojing and Wang, Yawei and Tian, Yijun and Mishra, Soumya Smruti and Xu, Zhichao and Gandhi, Megha and Xu, Panpan and Cheong, Lin Lee},
  booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={1529--1550},
  year={2026}
}

@article{xia-etal:skillrl,
  title={Skillrl: Evolving agents via recursive skill-augmented reinforcement learning},
  author={Xia, Peng and Chen, Jianwen and Wang, Hanyang and Liu, Jiaqi and Zeng, Kaide and Wang, Yu and Han, Siwei and Zhou, Yiyang and Zhao, Xujiang and Chen, Haifeng and others},
  journal={arXiv preprint arXiv:2602.08234},
  year={2026}
}

@article{yu-etal:skill,
  title={Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents},
  author={Yu, Jianxiang and Zhu, Jiapeng and Lin, Bochen and Cui, Qier and Ding, Zichen and Li, Xiang},
  journal={arXiv preprint arXiv:2605.30723},
  year={2026}
}

@article{wang-etal:mem,
  title={Mem-$\{$$\backslash$alpha$\}$: Learning memory construction via reinforcement learning},
  author={Wang, Yu and Takanobu, Ryuichi and Liang, Zhiqi and Mao, Yuzhen and Hu, Yuanzhe and McAuley, Julian and Wu, Xiaojian},
  journal={arXiv preprint arXiv:2509.25911},
  year={2025}
}

@article{xu-etal:a-mem,
  title={A-mem: Agentic memory for llm agents},
  author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao and Zhang, Yongfeng},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  pages={17577--17604},
  year={2026}
}

@inproceedings{yan-etal:memory-r1,
  title={Memory-r1: Enhancing large language model agents to manage and utilize memories via reinforcement learning},
  author={Yan, Sikuan and Yang, Xiufeng and Huang, Zuchao and Nie, Ercong and Ding, Zifeng and Li, Zonggen and Ma, Xiaowen and Bi, Jinhe and Kersting, Kristian and Pan, Jeff Z and others},
  booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={12805--12825},
  year={2026}
}

@article{chhikara-etal:mem0,
  title={Mem0: Building production-ready ai agents with scalable long-term memory},
  author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
  journal={arXiv preprint arXiv:2504.19413},
  year={2025}
}

@inproceedings{li-etal:word2world,
  title={From word to world: Can large language models be implicit text-based world models?},
  author={Li, Yixia and Wang, Hongru and Qiu, Jiahao and Yin, Zhenfei and Zhang, Dongdong and Qian, Cheng and Li, Zeping and Ma, Xiaoteng and Chen, Guanhua and Ji, Heng},
  booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={8084--8111},
  year={2026}
}

@article{sutton-etal:welcome,
  title={Welcome to the era of experience},
  author={Silver, David and Sutton, Richard S},
  journal={Google AI},
  volume={1},
  pages={11},
  year={2025}
}

@inproceedings{chen-etal:failure-attribution,
    title     = {Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents},
    author    = {Chen, Jianming and Wang, Yawen and Wang, Junjie and Xie, Xiaofei and Li, Shoubin and Wang, Qing and Xu, Fanjiang},
    booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
    pages     = {28132--28150},
    address   = {San Diego, California, United States},
    publisher = {Association for Computational Linguistics},
    month     = jul,
    year      = {2026},
    doi       = {10.18653/v1/2026.findings-acl.1402},
    url       = {https://aclanthology.org/2026.findings-acl.1402/}
}

@article{sun-etal:learning-from-failure,
    title   = {Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents},
    author  = {Sun, Xueqiao and Wang, Xiaohan and Schmidt, Ludwig and Yeung-Levy, Serena and Zhang, Yuhui},
    journal = {arXiv preprint arXiv:2606.31270},
    year    = {2026},
    url     = {https://arxiv.org/abs/2606.31270}
}

@article{zhang-etal:autoenv,
  title         = {AutoEnv: Automated Environments for Measuring
                   Cross-Environment Agent Learning},
  author        = {Zhang, Jiayi and Peng, Yiran and Kong, Fanqi and Cheng, Yang
                   and Wu, Yifan and Yu, Zhaoyang and Xiang, Jinyu
                   and Ruan, Jianhao and Wang, Jinlin and Song, Maojia
                   and Liu, HongZhang and Tang, Xiangru and Liu, Bang
                   and Wu, Chenglin and Luo, Yuyu},
  journal       = {arXiv preprint arXiv:2511.19304},
  year          = {2025},
  eprint        = {2511.19304},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI}
}

@article{cai-etal:autoforge,
  title         = {AutoForge: Automated Environment Synthesis for
                   Agentic Reinforcement Learning},
  author        = {Cai, Shihao and Fang, Runnan and Wu, Jialong and Li, Baixuan
                   and Wang, Xinyu and Jiang, Yong and Su, Liangcai
                   and Zhang, Liwen and Yin, Wenbiao and Zhang, Zhen
                   and Feng, Fuli and Xie, Pengjun and Wang, Xiaobin},
  journal       = {arXiv preprint arXiv:2512.22857},
  year          = {2025},
  eprint        = {2512.22857},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI}
}

@article{wang-etal:awm,
  title         = {Agent World Model: Infinity Synthetic Environments for
                   Agentic Reinforcement Learning},
  author        = {Wang, Zhaoyang and Xu, Canwen and Liu, Boyi and Wang, Yite
                   and Han, Siwei and Yao, Zhewei and Yao, Huaxiu
                   and He, Yuxiong},
  journal       = {arXiv preprint arXiv:2602.10090},
  year          = {2026},
  eprint        = {2602.10090},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI}
}

@article{song-etal:envscaler,
  title         = {EnvScaler: Scaling Tool-Interactive Environments for
                   LLM Agent via Programmatic Synthesis},
  author        = {Song, Xiaoshuai and Chang, Haofei and Dong, Guanting
                   and Zhu, Yutao and Dou, Zhicheng and Wen, Ji-Rong},
  journal       = {arXiv preprint arXiv:2601.05808},
  year          = {2026},
  eprint        = {2601.05808},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL}
}

@article{deepseek-ai:deepseekv32,
  title         = {DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models},
  author        = {DeepSeek-AI},
  journal       = {arXiv preprint arXiv:2512.02556},
  year          = {2025},
  eprint        = {2512.02556},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL}
}

@article{fang-etal:agentscaler,
  title         = {Towards General Agentic Intelligence via Environment Scaling},
  author        = {Fang, Runnan and Cai, Shihao and Li, Baixuan and Wu, Jialong
                   and Li, Guangyu and Yin, Wenbiao and Wang, Xinyu
                   and Wang, Xiaobin and Su, Liangcai and Zhang, Zhen
                   and Wu, Shibin and Tao, Zhengwei and Jiang, Yong
                   and Xie, Pengjun and Huang, Fei and Zhou, Jingren},
  journal       = {arXiv preprint arXiv:2509.13311},
  year          = {2025},
  eprint        = {2509.13311},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI}
}

@article{sullivan-etal:randomworld,
  title         = {Procedural Environment Generation for Tool-Use Agents},
  author        = {Sullivan, Michael and Hartmann, Mareike and Koller, Alexander},
  journal       = {arXiv preprint arXiv:2506.11045},
  year          = {2025},
  eprint        = {2506.11045},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL}
}

@article{chen-etal:research,
  title = {ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning},
  author = {Chen, Mingyang and Li, Tianpeng and Sun, Haoze and Zhou, Yijie and Zhu, Chenzheng and Yang, Fan and Zhou, Zenan and Chen, Weipeng and Wang, Haofen and Pan, Jeff Z. and Zhang, Wen and Chen, Huajun},
  journal = {arXiv preprint arXiv:2503.19470},
  year = {2025}
}

@article{jin-etal:searchr1,
  title = {Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning},
  author = {Jin, Bowen and Zeng, Hansi and Yue, Zhenrui and Wang, Dong and Zamani, Hamed and Han, Jiawei},
  journal = {arXiv preprint arXiv:2503.09516},
  year = {2025}
}

@article{singh-etal:artist,
  title = {Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning},
  author = {Singh, Joykirat and Magazine, Raghav and Pandya, Yash and Nambi, Akshay},
  journal = {arXiv preprint arXiv:2505.01441},
  year = {2025}
}

@article{feng-etal:retool,
  title = {ReTool: Reinforcement Learning for Strategic Tool Use in LLMs},
  author = {Feng, Jiazhan and Huang, Shijue and Qu, Xingwei and Zhang, Ge and Qin, Yujia and Zhong, Baoquan and Jiang, Chengquan and Chi, Jinxin and Zhong, Wanjun},
  journal = {arXiv preprint arXiv:2504.11536},
  year = {2025}
}


@article{qian-etal:toolrl,
  title = {ToolRL: Reward is All Tool Learning Needs},
  author = {Qian, Cheng and Acikgoz, Emre Can and He, Qi and Wang, Hongru and Chen, Xiusi and Hakkani-Tr, Dilek and Tur, Gokhan and Ji, Heng},
  journal = {arXiv preprint arXiv:2504.13958},
  year = {2025}
}

@inproceedings{patil-etal:gorilla,
  title = {Gorilla: Large Language Model Connected with Massive APIs},
  author = {Patil, Shishir G. and Zhang, Tianjun and Wang, Xin and Gonzalez, Joseph E.},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2024}
}

@inproceedings{qin-etal:toolllm,
  title = {ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs},
  author = {Qin, Yujia and Liang, Shihao and Ye, Yining and Zhu, Kunlun and Yan, Lan and Lu, Yaxi and Lin, Yankai and Cong, Xin and Tang, Xiangru and Qian, Bill and Zhao, Sihan and Hong, Lauren and Tian, Runchu and Xie, Ruobing and Zhou, Jie and Gerstein, Mark and Li, Dahai and Liu, Zhiyuan and Sun, Maosong},
  booktitle = {The Twelfth International Conference on Learning Representations},
  year = {2024}
}

@inproceedings{li-etal:apibank,
  title = {API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs},
  author = {Li, Minghao and Zhao, Yingxiu and Yu, Bowen and Song, Feifan and Li, Hangyu and Yu, Haiyang and Li, Zhoujun and Huang, Fei and Li, Yongbin},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  year = {2023}
}

@inproceedings{schick-etal:toolformer,
  title = {Toolformer: Language Models Can Teach Themselves to Use Tools},
  author = {Schick, Timo and Dwivedi-Yu, Jane and Dess'i, Roberto and Raileanu, Roberta and Lomeli, Maria and Hambro, Eric and Zettlemoyer, Luke and Cancedda, Nicola and Scialom, Thomas},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2023}
}


@inproceedings{komeili-etal:Internet,
  title={Internet-augmented dialogue generation},
  author={Komeili, Mojtaba and Shuster, Kurt and Weston, Jason},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={8460--8478},
  year={2022}
}

@inproceedings{singh-etal:agentic,
  title={Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning},
  author={Singh, Joykirat and Pandya, Yash and Vajreshwari, Pranav and Magazine, Raghav and Nambi, Akshay},
  booktitle={First Workshop on Foundations of Reasoning in Language Models},
  year={2025}
}

@inproceedings{melnyk:2024distributional,
  title={Distributional Preference Alignment of LLMs via Optimal Transport},
  author={Melnyk, Igor and Mroueh, Youssef and Belgodere, Brian and Rigotti, Mattia and Nitsure, Apoorva and Yurochkin, Mikhail and Greenewald, Kristjan and Navratil, Jiri and Ross, Jerret},
  booktitle={Advances in Neural Information Processing Systems},
  year={2024}
}

@inproceedings{zeng:2024token,
  title={Token-level Direct Preference Optimization},
  author={Zeng, Yongcheng and Liu, Guoqing and Ma, Weiyu and Yang, Ning and Zhang, Haifeng and Wang, Jun},
  booktitle={Proceedings of the 41st International Conference on Machine Learning},
  year={2024}
}

@inproceedings{xiao:2024cal,
  title={Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment},
  author={Xiao, Teng and Yuan, Yige and Zhu, Huaisheng and Li, Mingxiao and Honavar, Vasant G.},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024}
}

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Lifan Yuan and
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Yuan Ni and
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Ruobing Xie and
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Vijay Srinivasan and
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Tristan Naumann and
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 author = {Ganqu Cui and
Lifan Yuan and
Ning Ding and
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S. V. N. Vishwanathan and
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@article{touvron:2023llama,
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@inproceedings{ouyang:2022training,
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Sandhini Agarwal and
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@article{bradley:1952rank,
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@inproceedings{rafailov:2023direct,
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LA, USA, December 10 - 16, 2023},
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Tristan Naumann and
Amir Globerson and
Kate Saenko and
Moritz Hardt and
Sergey Levine},
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 title = {Direct Preference Optimization: Your Language Model is Secretly a
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 author = {Mohammad Gheshlaghi Azar and
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Bilal Piot and
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Stephan Mandt and
Yingzhen Li},
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 timestamp = {Mon, 13 May 2024 01:00:00 +0200},
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@inproceedings{xu:2024contrastive,
 author = {Haoran Xu and
Amr Sharaf and
Yunmo Chen and
Weiting Tan and
Lingfeng Shen and
Benjamin Van Durme and
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Young Jin Kim},
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Vienna, Austria, July 21-27, 2024},
 publisher = {OpenReview.net},
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 title = {Contrastive Preference Optimization: Pushing the Boundaries of {LLM}
Performance in Machine Translation},
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 year = {2024}
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@article{ethayarajh:2024kto,
 author = {Ethayarajh, Kawin and Xu, Winnie and Muennighoff, Niklas and Jurafsky, Dan and Kiela, Douwe},
 journal = {ArXiv preprint},
 title = {Kto: Model alignment as prospect theoretic optimization},
 url = {https://arxiv.org/abs/2402.01306},
 volume = {abs/2402.01306},
 year = {2024}
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@article{hong:2024orpo,
 author = {Hong, Jiwoo and Lee, Noah and Thorne, James},
 journal = {ArXiv preprint},
 title = {Orpo: Monolithic preference optimization without reference model},
 url = {https://arxiv.org/abs/2403.07691},
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 year = {2024}
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@article{gallego:2024refined,
 author = {Gallego, V{\'\i}ctor},
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 title = {Refined direct preference optimization with synthetic data for behavioral alignment of llms},
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@inproceedings{meng:2025simpo,
 author = {Yu Meng and
Mengzhou Xia and
Danqi Chen},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/0001X024.bib},
 booktitle = {Advances in Neural Information Processing Systems 38: Annual Conference
on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver,
BC, Canada, December 10 - 15, 2024},
 editor = {Amir Globersons and
Lester Mackey and
Danielle Belgrave and
Angela Fan and
Ulrich Paquet and
Jakub M. Tomczak and
Cheng Zhang},
 timestamp = {Thu, 13 Feb 2025 00:00:00 +0100},
 title = {SimPO: Simple Preference Optimization with a Reference-Free Reward},
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@inproceedings{zhou:2024prior,
 author = {Zhou, Hang and Wang, Chenglong and Hu, Yimin and Xiao, Tong and Zhang, Chunliang and Zhu, Jingbo},
 booktitle = {China National Conference on Chinese Computational Linguistics},
 organization = {Springer},
 pages = {555--570},
 title = {Prior constraints-based reward model training for aligning large language models},
 year = {2024}
}

@article{shao:2025earlier,
 author = {Shao, Ruichen and Li, Bei and Liu, Gangao and Chen, Yang and Zhou, Xiang and Wang, Jingang and Cai, Xunliang and Li, Peng},
 journal = {ArXiv preprint},
 title = {Earlier Tokens Contribute More: Learning Direct Preference Optimization From Temporal Decay Perspective},
 url = {https://arxiv.org/abs/2502.14340},
 volume = {abs/2502.14340},
 year = {2025}
}

@misc{openai:2024learning,
    title = {Learning to Reason with LLMs},
    url = {https://openai.com/index/learning-to-reason-with-llms/},
    author = {OpenAI},
    month = {September},
    year = {2024}
}

@article{deepseek:2025r1,
    title={Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning},
    author={Deepseek},
    journal={arXiv preprint arXiv:2501.12948},
    year={2025}
}