The field of large-scale language model training is rapidly advancing, with a focus on developing optimization techniques to improve training efficiency and performance. Recent research has explored various methods to reduce the need for hyperparameter tuning, including the development of learning-rate-free methods and the proposal of new learning rate schedules. Additionally, novel gradient transformation techniques have been introduced to accelerate language model pre-training. Another area of research has investigated the role of dropout in single-epoch pretraining, with findings suggesting that dropout may not be necessary in certain scenarios. Noteworthy papers include:

• Critical Batch Size Revisited, which introduces a simple empirical approach to directly measure the critical batch size and its evolution over training.
• GradPower, which proposes a lightweight gradient-transformation technique for accelerating language model pre-training.
• Stepsize anything, which presents a unified learning rate schedule for budgeted-iteration training.
• You Only Train Once, which contributes to limiting training to one shot for loss selection and weighting. These papers demonstrate significant advancements in optimization techniques for large-scale language model training, with potential applications in various natural language processing tasks.