Igor Gitman

According to our database, Igor Gitman authored at least 18 papers between 2017 and 2026.

Collaborative distances:

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

2026

iGRPO: Self-Feedback-Driven LLM Reasoning.

Ali Hatamizadeh, Shrimai Prabhumoye, Igor Gitman, Ximing Lu, Seungju Han, Wei Ping, Yejin Choi, Jan Kautz

CoRR, February, 2026

Learning Generative Selection for Best-of-N.

Shubham Toshniwal, Aleksander Ficek, Siddhartha Jain, Wei Du, Vahid Noroozi, Sadegh Mahdavi, Somshubra Majumdar, Igor Gitman

CoRR, February, 2026

2025

Nemotron-Math: Efficient Long-Context Distillation of Mathematical Reasoning from Multi-Mode Supervision.

Wei Du, Shubham Toshniwal, Branislav Kisacanin, Sadegh Mahdavi, Ivan Moshkov, George Armstrong, Stephen Ge, Edgar Minasyan, Feng Chen, Igor Gitman

CoRR, December, 2025

Scaling Generative Verifiers For Natural Language Mathematical Proof Verification And Selection.

Sadegh Mahdavi, Branislav Kisacanin, Shubham Toshniwal, Wei Du, Ivan Moshkov, George Armstrong, Renjie Liao, Christos Thrampoulidis, Igor Gitman

CoRR, November, 2025

GenSelect: A Generative Approach to Best-of-N.

Shubham Toshniwal, Ivan Sorokin, Aleksander Ficek, Ivan Moshkov, Igor Gitman

CoRR, July, 2025

The Challenge of Teaching Reasoning to LLMs Without RL or Distillation.

Wei Du, Branislav Kisacanin, George Armstrong, Shubham Toshniwal, Ivan Moshkov, Alexan Ayrapetyan, Sadegh Mahdavi, Dan Zhao, Shizhe Diao, Dragan Masulovic, Marius Stanean, Advaith Avadhanam, Max Wang, Ashmit Dutta, Shitij Govil, Sri Yanamandara, Mihir Tandon, Sriram Ananthakrishnan, Vedant Rathi, David Zhang, Joonseok Kang, Leon Luo, Titu Andreescu, Boris Ginsburg, Igor Gitman

CoRR, July, 2025

AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset.

Ivan Moshkov, Darragh Hanley, Ivan Sorokin, Shubham Toshniwal, Christof Henkel, Benedikt Schifferer, Wei Du, Igor Gitman

CoRR, April, 2025

NeMo-Inspector: A Visualization Tool for LLM Generation Analysis.

Daria Gitman, Igor Gitman, Evelina Bakhturina

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data.

Shubham Toshniwal, Wei Du, Ivan Moshkov, Branislav Kisacanin, Alexan Ayrapetyan, Igor Gitman

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset.

Shubham Toshniwal, Ivan Moshkov, Sean Narenthiran, Daria Gitman, Fei Jia, Igor Gitman

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Confidence-based Ensembles of End-to-End Speech Recognition Models.

Igor Gitman, Vitaly Lavrukhin, Aleksandr Laptev, Boris Ginsburg

Proceedings of the 24th Annual Conference of the International Speech Communication Association, 2023

Powerful and Extensible WFST Framework for Rnn-Transducer Losses.

Aleksandr Laptev, Vladimir Bataev, Igor Gitman, Boris Ginsburg

Proceedings of the IEEE International Conference on Acoustics, 2023

2019

Understanding the Role of Momentum in Stochastic Gradient Methods.

Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

OpenSeq2Seq: extensible toolkit for distributed and mixed precision training of sequence-to-sequence models.

Oleksii Kuchaiev, Boris Ginsburg, Igor Gitman, Vitaly Lavrukhin, Carl Case, Paulius Micikevicius

CoRR, 2018

Novel Prediction Techniques Based on Clusterwise Linear Regression.

Igor Gitman, Jieshi Chen, Eric Lei, Artur Dubrawski

CoRR, 2018

Convergence Analysis of Gradient Descent Algorithms with Proportional Updates.

Igor Gitman, Deepak Dilipkumar, Ben Parr

CoRR, 2018

2017

Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification.

Igor Gitman, Boris Ginsburg

CoRR, 2017

Scaling SGD Batch Size to 32K for ImageNet Training.

Yang You, Igor Gitman, Boris Ginsburg

CoRR, 2017