Efficient Language Model Training

This field asks how to train language models with fewer resources while preserving their quality, making model development more accessible and large-scale training more practical. Approaches include selecting and scheduling data, changing tokenization or model architecture, improving optimizers and gradient computation, and coordinating parallelism, memory, and hardware; related work also targets fine-tuning and reasoning-model training. Researchers evaluate these methods with measures such as training loss, downstream benchmarks, throughput, memory use, and energy across different model sizes and hardware settings. A central tension is that gains depend on the task and system, and lower training cost may come with losses in generalization, robustness, or reproducibility, so efficiency must be assessed alongside model quality.