Taming Momentum in a Distributed Asynchronous Environment. - Ido Hakimi; Saar Barkai; Moshe Gabel; Assaf Schuster (CoRR, 2019)

Gap-Aware Mitigation of Gradient Staleness. - Saar Barkai; Ido Hakimi; Assaf Schuster (ICLR, 2020)

LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time. - Ido Hakimi; Rotem Zamir Aviv; Kfir Y. Levy; Assaf Schuster (ICDM, 2021)

Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. - Rotem Zamir Aviv; Ido Hakimi; Assaf Schuster; Kfir Yehuda Levy (ICML, 2021)

Faster Neural Network Training with Approximate Tensor Operations. - Menachem Adelman; Kfir Y. Levy; Ido Hakimi; Mark Silberstein (NeurIPS, 2021)

Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism. - Saar Eliad; Ido Hakimi; Alon De Jagger; Mark Silberstein; Assaf Schuster (USENIX ATC, 2021)

Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays. - Rotem Zamir Aviv; Ido Hakimi; Assaf Schuster; Kfir Y. Levy (CoRR, 2021)

SMEGA2: Distributed Asynchronous Deep Neural Network Training With a Single Momentum Buffer. - Refael Cohen; Ido Hakimi; Assaf Schuster (ICPP, 2022)

Distributed Deep Neural Networks. - Ido Hakimi (2022)

q2d: Turning Questions into Dialogs to Teach Models How to Search. - Yonatan Bitton; Shlomi Cohen-Ganor; Ido Hakimi; Yoad Lewenberg; Roee Aharoni; Enav Weinreb (EMNLP, 2023)

Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs. - Jonas Hübotter; Sascha Bongni; Ido Hakimi; Andreas Krause 001 (ICLR, 2025)

MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors. - Jakub Macina; Nico Daheim; Ido Hakimi; Manu Kapur; Iryna Gurevych; Mrinmaya Sachan (CoRR, 2025)

Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging. - Ryo Bertolissi; Jonas Hübotter; Ido Hakimi; Andreas Krause 001 (CoRR, 2025)

From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning. - David Dinucu-Jianu; Jakub Macina; Nico Daheim; Ido Hakimi; Iryna Gurevych; Mrinmaya Sachan (CoRR, 2025)

Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning. - Matthias Otth; Jonas Hübotter; Ido Hakimi; Andreas Krause 001 (CoRR, 2025)

Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning. - Jonas Hübotter; Leander Diaz-Bone; Ido Hakimi; Andreas Krause 001; Moritz Hardt (CoRR, 2025)