Alexander Bukharin

According to our database, Alexander Bukharin authored at least 21 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation.

Zhuolin Yang, Zihan Liu, Yang Chen, Wenliang Dai, Boxin Wang, Sheng-Chieh Lin, Chankyu Lee, Yangyi Chen, Dongfu Jiang, Jiafan He, Renjie Pi, Grace Lam, Nayeon Lee, Alexander Bukharin, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping

CoRR, March, 2026

2025

Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training.

Mingjie Liu, Shizhe Diao, Jian Hu, Ximing Lu, Xin Dong, Hao Zhang, Alexander Bukharin, Shaokun Zhang, Jiaqi Zeng, Makesh Narsimhan Sreedhar, Gerald Shen, David Mosallanezhad, Di Zhang, Jonas Yang, June Yang, Oleksii Kuchaiev, Guilin Liu, Zhiding Yu, Pavlo Molchanov, Yejin Choi, Jan Kautz, Yi Dong

CoRR, July, 2025

HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages.

Zhilin Wang, Jiaqi Zeng, Olivier Delalleau, Hoo-Chang Shin, Felipe Soares, Alexander Bukharin, Ellie Evans, Yi Dong, Oleksii Kuchaiev

CoRR, May, 2025

Adversarial Training of Reward Models.

Alexander Bukharin, Haifeng Qian, Shengyang Sun, Adithya Renduchintala, Soumye Singhal, Zhilin Wang, Oleksii Kuchaiev, Olivier Delalleau, Tuo Zhao

CoRR, April, 2025

Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment.

Shengyang Sun, Yian Zhang, Alexander Bukharin, David Mosallanezhad, Jiaqi Zeng, Soumye Singhal, Gerald Shen, Adithya Renduchintala, Tugrul Konuk, Yi Dong, Zhilin Wang, Dmitry Chichkov, Olivier Delalleau, Oleksii Kuchaiev

CoRR, February, 2025

Deep Reinforcement Learning from Hierarchical Preference Design.

Alexander Bukharin, Yixiao Li, Pengcheng He, Tuo Zhao

Proceedings of the Forty-second International Conference on Machine Learning, 2025

HelpSteer2-Preference: Complementing Ratings with Preferences.

Zhilin Wang, Alexander Bukharin, Olivier Delalleau, Daniel Egert, Gerald Shen, Jiaqi Zeng, Oleksii Kuchaiev, Yi Dong

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

RNR: Teaching Large Language Models to Follow Roles and Rules.

Kuan Wang, Alexander Bukharin, Haoming Jiang, Qingyu Yin, Zhengyang Wang, Tuo Zhao, Jingbo Shang, Chao Zhang, Bing Yin, Xian Li, Jianshu Chen, Shiyang Li

CoRR, 2024

Robust Reinforcement Learning from Corrupted Human Feedback.

Alexander Bukharin, Ilgee Hong, Haoming Jiang, Qingru Zhang, Zixuan Zhang, Tuo Zhao

CoRR, 2024

Adaptive Preference Scaling for Reinforcement Learning with Human Feedback.

Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Robust Reinforcement Learning from Corrupted Human Feedback.

Alexander Bukharin, Ilgee Hong, Haoming Jiang, Zichong Li, Qingru Zhang, Zixuan Zhang, Tuo Zhao

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Data Diversity Matters for Robust Instruction Tuning.

Alexander Bukharin, Shiyang Li, Zhengyang Wang, Jingfeng Yang, Bing Yin, Xian Li, Chao Zhang, Tuo Zhao, Haoming Jiang

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

2023

Data Diversity Matters for Robust Instruction Tuning.

Alexander Bukharin, Tuo Zhao

CoRR, 2023

Deep Reinforcement Learning from Hierarchical Weak Preference Feedback.

Alexander Bukharin, Yixiao Li, Pengcheng He, Weizhu Chen, Tuo Zhao

CoRR, 2023

Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms.

Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Machine Learning Force Fields with Data Cost Aware Training.

Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao

Proceedings of the International Conference on Machine Learning, 2023

Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data.

Shixiang Zhu, Alexander Bukharin, Liyan Xie, Khurram Yamin, Shihao Yang, Pinar Keskinocak, Yao Xie

IEEE J. Sel. Top. Signal Process., 2022

PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance.

Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao

Proceedings of the International Conference on Machine Learning, 2022

2021

High-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S.

Shixiang Zhu, Alexander Bukharin, Liyan Xie, Mauricio Santillana, Shihao Yang, Yao Xie

ACM Trans. Manag. Inf. Syst., 2021

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data.

Shixiang Zhu, Alexander Bukharin, Liyan Xie, Shihao Yang, Pinar Keskinocak, Yao Xie

CoRR, 2021