The recent developments in language model training have seen a shift towards more efficient data utilization and scaling strategies. Researchers are increasingly focusing on optimizing dataset composition for sample-efficient training, recognizing that the complexity and richness of the data significantly impact model performance, especially in smaller models. The field is also exploring novel parameter-efficient training methods that dynamically adapt subsets of model parameters, expanding the operational range and potentially reducing computational costs. Additionally, there is a growing interest in improving data efficiency through dynamic bootstrapping of contrastive pre-training, which allows for iterative updates to the dataset, enhancing the model's ability to track and utilize useful data. Notably, the use of Variation Sets in child-directed speech data is being investigated to understand their impact on training efficiency, suggesting that specific linguistic properties can influence model performance. These advancements collectively aim to make language model training more efficient and scalable, addressing the high costs and resource demands associated with current large-scale models.

Noteworthy papers include one that highlights the importance of dataset composition for smaller models, showing that more complex datasets like Gutenberg outperform simpler ones. Another paper proposes a dynamic subset tuning method for parameter-efficient training, which outperforms existing techniques in various NLP tasks. Lastly, a study on bootstrapping contrastive pre-training demonstrates significant improvements in data efficiency with minimal performance degradation.