The paper studies how different optimizers perform as training duration (overtraining) increases, focusing on matrix‑preconditioned methods (Muon, SOAP) and a momentum‑scheduled method (ADANA) compared to AdamW. Across models ranging from 51M to 253M parameters and overtraining factors up to 256×, the authors find that optimal learning‑rate schedules, weight‑decay coefficients, and memory settings shift with horizon, and that ADANA consistently outperforms AdamW, especially with log‑time weight decay and momentum cooldown. Muon and SOAP maintain roughly constant token‑efficiency advantages, with SOAP potentially improving at the highest overtraining levels.
By Katie Everett, Shikai Qiu
arXiv:2607. 20548v1 Announce Type: cross Abstract: Higher-order optimizers such as Muon and SOAP offer faster convergence than AdamW, but their computational cost and numerical stability challenges have limited adoption at scale.
By Mikail Khona, Aditya Vavre, Boxiang Wang, Deyu Fu, Hao Wu, Mike Chrzanowski, Bryan Catanzaro, Dheevatsa Mudigere, Jeff Pool, Michael Lightstone, Mohammad Shoeybi, Mostofa Patwary, Nima Tajbakhsh, Tijmen Blankevoort
arXiv:2505. 23725v3 Announce Type: replace Abstract: DiLoCo is a powerful framework for training large language models (LLMs), enabling larger optimal batch sizes and increased accelerator utilization under networking constraints.
By Benjamin Th\'erien, Xiaolong Huang, Aaron Defazio, Irina Rish, Eugene Belilovsky
arXiv:2507. 01598v5 Announce Type: replace Abstract: Muon, a recently proposed optimizer that leverages the inherent matrix structure of neural network parameters, has demonstrated strong empirical performance, indicating its potential as a successor to standard optimizers such as AdamW.
By Naoki Sato, Hiroki Naganuma, Hideaki Iiduka
The paper reports on the updated AlgoPerf V1 benchmark, evaluating new training algorithms on wall‑clock time‑to‑target across nine workloads on fixed hardware. Using a self‑tuning ruleset that requires a single configuration for all workloads, Schedule‑Free AdamW achieved the best overall performance among 13 PyTorch and JAX submissions. The study also examines trade‑offs between step efficiency and runtime, finding that matrix‑preconditioned and two‑loop methods are sample‑efficient but need aggressive computational optimizations, and highlights the high sensitivity of learning rate and momentum across domains, underscoring the need for more robust hyperparameter‑tuning‑resistant methods.
By Priya Kasimbeg (Alice), Ahmed Khaled (Alice), Aahlad Chandrabhatta (Alice), Yifan Wang (Alice), Andy Liu (Alice), Niccol\`o Ajroldi (Alice), David Tweedle (Alice), Xingyu (Alice), Yang, Isaac Patole, Frank Schneider, George E. Dahl
arXiv:2606. 13894v1 Announce Type: cross Abstract: AdamW is a default optimizer for modern deep learning, but its first and second moment states add roughly two parameter-sized buffers to training memory.
By Nadav Benedek, Tomer Koren, Ohad Fried
arXiv:2610.27895v1 Announce Type: cross
Abstract: Direct preference optimisation (DPO) is widely used for language model post-training, but its performance depends strongly on the choice of its hyper...
By Hannan Mahadik, David Salinas, Jacek Golebiowski, Aaron Klein
The article surveys recent neural‑network optimizers, noting that the field has moved beyond simple Adam variants to encompass matrix‑ and layer‑level designs, time‑policy horizons, and state representations that survive sharding and low‑precision computation. It categorizes optimizers along four axes—temporal estimation, update geometry, horizon management, and representation & systems—highlighting methods such as Muon, Shampoo, SOAP, and quantized states. The survey concludes that while matrix‑aware methods are a genuine advance, no single optimizer universally replaces AdamW, and performance depends on model scale, data‑to‑parameter ratio, batch size, schedule, partitioning, tuning budget, and target metric.
By Ruoran Xu
arXiv:2606. 03938v1 Announce Type: cross Abstract: Multi-epoch training is becoming the standard now that compute is growing faster than the supply of high-quality text.
By Bishwas Mandal, Shmuel Berman, Akshay Vegesna, Samip Dahal
arXiv:2608. 04407v1 Announce Type: cross Abstract: Memory-efficient matrix optimizers such as Sinkhorn gradient descent remove most AdamW optimizer state for dense Transformer matrices, but direct application to Mixture-of-Experts (MoE) training is unreliable.
By Masato Fujitake
arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.
By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky
The paper introduces TACO, a new optimizer for fine‑tuning large language models that drastically reduces optimizer state memory while preserving first‑order gradients. TACO selects the sign of the largest magnitude entry in each column of weight matrices, achieving a 174× reduction in persistent optimizer memory compared to AdamW8bit and a 2.9× decrease in peak training memory on OPT‑13B. This allows full‑parameter fine‑tuning of 30–32B‑parameter models on a single 80 GB GPU across multiple model families and tasks, with comparable accuracy and runtime to existing methods.
By Jichao Jiang (University of Central Florida), Cristian McGee (University of Central Florida), El Houcine Bergou (Mohammed VI Polytechnic University), Hanqin Cai (University of Central Florida), Aritra Dutta (University of Central Florida)