Dzmitry Bahdanau

PipelineRL: Faster On-policy Reinforcement Learning for Long Sequence Generation. Rafael Pardinas, Ehsan Kamalloo, Alexandre Piche, Dzmitry Bahdanau. At Transactions on Machine Learning Research (TMLR), 2026.

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Faster On-Policy Reinforcement Learning for Long Sequence Generation. Alexandre Piche, Ehsan Kamalloo, Rafael Pardinas, Xiaoyin Chen, Dzmitry Bahdanau. At NOW AI, 2025.

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BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning. Ahmed Masry, Abhay Puri, Masoud Hashemi, Juan A. Rodriguez, Megh Thakkar, Khyati Mahajan, Vikas Yadav, Sathwik Tejaswi Madhusudhan, Alexandre Piche, Dzmitry Bahdanau, Christopher Pal, David Vazquez, Enamul Hoque Prince , Perouz Taslakian, Sai Rajeswar Mudumba, Spandana Gella. At Conference on Language Modeling (COLM), 2025.

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LLMs can learn self-restraint through iterative self-reflection. Alexandre Piche, Aristides Milios, Dzmitry Bahdanau, Christopher Pal. At Transactions on Machine Learning Research (TMLR), 2025.

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LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders. Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, Siva Reddy. At Conference on Language Modeling (COLM), 2024.

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Evaluating In-Context Learning of Libraries for Code Generation. Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep Dasigi. At North American Chapter of the Association for Computational Linguistics (NAACL), 2024.

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Self-evaluation and self-prompting to improve the reliability of LLMs. Alexandre Piche, Aristides Milios, Dzmitry Bahdanau, Christopher Pal. At Workshop at the International Conference of Learning Representation (ICLR), 2024.

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NNetscape Navigator: Complex Demonstrations for Web Agents Without a Demonstrator. Shikhar Murty, Hao Zhu, Dzmitry Bahdanau, Chris Manning. At ArXiv, 2024.

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TapeAgents: a Holistic Framework for Agent Development and Optimization. Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang, Ehsan Kamalloo, Rafael Pardinas, Alexandre Piche, Torsten Scholak, Oleh Shliazhko, Jordan Prince Tremblay, Karam Ghanem, Soham Parikh, Mitul Tiwari, Quaizar Vohra. At ArXiv, 2024.

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MAGNIFICO: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations. Arkil Patel, Satwik Bhattamishra, Siva Reddy, Dzmitry Bahdanau. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.

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PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation. Gaurav Sahu, Olga Vechtomova, Dzmitry Bahdanau, Issam H. Laradji. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.

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RepoFusion: Training Code Models to Understand Your Repository. Disha Shrivastava, Denis Kocetkov, Harm de Vries, Dzmitry Bahdanau, Torsten Scholak. At ArXiv, 2023.

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StarCoder: may the source be with you!. Raymond Li, Loubna Ben Allal, Yangtian Zi, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Jia Li, Jenny Chim, Terry Yue Zhuo, Thomas Wang, Mishig Davaadorj, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Ming-Ho Yee, Logesh Kumar Umapathi, Benjamin Lipkin, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf , Arjun Guha, Leandro von Werra, Harm de Vries, Joel Lamy Poirier, Alex Gu, Armel Zebaze, Jian Zhu, Manan Dey, Marc Marone, Mayank Mishra, Muhtasham Oblokulov, Olivier Dehaene, Qian Liu, Tri Dao, Wenhao Yu, Niklas Muennighoff. At Transactions on Machine Learning Research (TMLR), 2023.

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SantaCoder: don't reach for the stars!. Harm de Vries, Raymond Li, Joel Lamy Poirier, Dzmitry Bahdanau, Denis Kocetkov, Sean Hughes. At Workshop at the International Conference on Learning Representations (ICLR), 2023.

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In-Context Learning for Text Classification with Many Labels. Aristides Milios, Dzmitry Bahdanau, Siva Reddy. At Workshop at the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

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On the Compositional Generalization Gap of In-Context Learning. Dzmitry Bahdanau, Arian Hosseini, Aaron Courville, Alessandro Sordoni, Ankit Vani. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

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The Stack: 3 TB of permissively licensed source code. Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf , Dzmitry Bahdanau, Leandro von Werra, Harm de Vries. At Transactions on Machine Learning Research (TMLR), 2022.

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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. Gaurav Sahu, Pau Rodriguez, Issam H. Laradji, Parmida Atighhehchian, David Vazquez, Dzmitry Bahdanau. At Montreal AI Symposium (MAIS), 2022.

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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. Gaurav Sahu, Pau Rodriguez, Parmida Atighhehchian, Issam H. Laradji, David Vazquez, Dzmitry Bahdanau. At Workshop at the Annual Meetings of the Association for Computational Linguistics (ACL), 2022.

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Compositional Generalization in Dependency Parsing. Emily Goodwin, Siva Reddy, Timothy J. O'Donnell, Dzmitry Bahdanau. At Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

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LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing. Dora Jambor, Dzmitry Bahdanau. At Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

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Systematic Generalization with Edge Transformers. Leon Bergen, Timothy J. O'Donnell, Dzmitry Bahdanau. At Conference on Neural Information Processing Systems (NeurIPS), 2021.

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Picard: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

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DuoRAT: Towards Simpler Text-to-SQL Models. Torsten Scholak, Raymond Li, Dzmitry Bahdanau, Harm de Vries, Christopher Pal. At North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

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Understanding by Understanding Not: Modeling Negation in Language Models. Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, Aaron Courville. At North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

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Towards Ecologically Valid Research on Language User Interfaces. Harm de Vries, Dzmitry Bahdanau, Chris Manning. At ArXiv, 2020.

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CLOSURE: Assessing Systematic Generalization of CLEVR models. Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell, Philippe Beaudoin, Yoshua Bengio, Aaron Courville, Shikhar Murty. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning. Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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Systematic Generalization: What Is Required and Can It Be Learned?. Dzmitry Bahdanau, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville, Shikhar Murty. At International Conference on Learning Representations (ICLR), 2019.

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