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We present Lil-Bevo, our submission to the BabyLM Challenge. We pretrainedour masked language models with three ingredients: an initial pretraining withmusic data, training on shorter sequences before training on longer ones, andmasking specific tokens to target some of the BLiMP subtasks. Overall, ourbaseline models performed above chance, but far below the performance levels oflarger LLMs trained on more data. We found that training on short sequencesperformed better than training on longer sequences.Pretraining on music mayhelp performance marginally, but, if so, the effect seems small. Our targetedMasked Language Modeling augmentation did not seem to improve model performancein general, but did seem to help on some of the specific BLiMP tasks that wewere targeting (e.g., Negative Polarity Items). Training performant LLMs onsmall amounts of data is a difficult but potentially informative task. Whilesome of our techniques showed some promise, more work is needed to explorewhether they can improve performance more than the modest gains here. Our codeis available at https://github.com/venkatasg/Lil-Bevo and out models athttps://huggingface.co/collections/venkatasg/babylm-653591cdb66f4bf68922873a

Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways