# Findings of the Third BabyLM Challenge: Accelerating Language Modeling Research with Cognitively Plausible Data

Authors: Lucas Charpentier, Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul, Michael Y. Hu, Jing Liu, Jaap Jumelet, Tal Linzen, Aaron Mueller, Candance Ross, Raj Sanjay Shah, Alex Warstadt, Ethan Gotlieb Wilcox, Adina Williams
Venue: Proceedings of the First BabyLM Workshop (2025)

## Abstract
This report summarizes the findings from the 3rd BabyLM Challenge and the 1st BabyLM Workshop. The BabyLM Challenge is a shared task aimed at closing the data efficiency gap between human and machine language learners. The goal is to improve the performance of language models given a fixed training budget of no more than 100 million words. This year, the challenge was held as part of an expanded BabyLM Workshop that invited paper submissions on topics relevant to the BabyLM effort, including sample-efficient pretraining and cognitive modeling for LMs. For the challenge, we kept the text-only and text{--}image tracks from previous years, but also introduced a new \textit{interaction} track, where student models are allowed to learn from feedback from larger teacher models. Furthermore, we introduce a new set of evaluation tasks to assess the ``human likeness'' of models on a cognitive and linguistic level, limit the total amount of training compute allowed, and measure performance on intermediate checkpoints. We observe that new training objectives and architectures tend to produce the best-performing approaches, and that interaction with teacher models can yield high-quality language models. The strict and interaction tracks saw submissions that outperformed the best-performing methods from previous years. We do not observe a complete correlation between training FLOPs and performance. {\%}, suggesting that some methods can produce real gains beyond allowing us to spend more compute. This year{\'}s BabyLM Challenge shows that there is still room to innovate in a data-constrained setting, and that community-driven research can yield actionable insights for language modeling.

## Links
- DOI: https://doi.org/10.18653/v1/2025.babylm-main.28
- Semantic Scholar: https://www.semanticscholar.org/paper/282893571
- Publisher: https://aclanthology.org/2025.babylm-main.28/

## How to cite
@inproceedings{charpentier-etal-2025-findings,
title = "Findings of the Third {B}aby{LM} Challenge: Accelerating Language Modeling Research with Cognitively Plausible Data",
    author = "Charpentier, Lucas  and
      Choshen, Leshem  and
      Cotterell, Ryan  and
      Gul, Mustafa Omer  and
      Hu, Michael Y.  and
      Liu, Jing  and
      Jumelet, Jaap  and
      Linzen, Tal  and
      Mueller, Aaron  and
      Ross, Candance  and
      Shah, Raj Sanjay  and
      Warstadt, Alex  and
      Wilcox, Ethan Gotlieb  and
      Williams, Adina",
    editor = "Charpentier, Lucas  and
      Choshen, Leshem  and
      Cotterell, Ryan  and
      Gul, Mustafa Omer  and
      Hu, Michael Y.  and
      Liu, Jing  and
      Jumelet, Jaap  and
      Linzen, Tal  and
      Mueller, Aaron  and
      Ross, Candace  and
      Shah, Raj Sanjay  and
      Warstadt, Alex  and
      Wilcox, Ethan Gotlieb  and
      Williams, Adina",
    booktitle = "Proceedings of the First BabyLM Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.babylm-main.28/",
    doi = "10.18653/v1/2025.babylm-main.28",
    pages = "399--420",
    ISBN = "TODO",
    abstract = "This report summarizes the findings from the 3rd BabyLM Challenge and the 1st BabyLM Workshop. The BabyLM Challenge is a shared task aimed at closing the data efficiency gap between human and machine language learners. The goal is to improve the performance of language models given a fixed training budget of no more than 100 million words. This year, the challenge was held as part of an expanded BabyLM Workshop that invited paper submissions on topics relevant to the BabyLM effort, including sample-efficient pretraining and cognitive modeling for LMs. For the challenge, we kept the text-only and text{--}image tracks from previous years, but also introduced a new \textit{interaction} track, where student models are allowed to learn from feedback from larger teacher models. Furthermore, we introduce a new set of evaluation tasks to assess the ``human likeness'' of models on a cognitive and linguistic level, limit the total amount of training compute allowed, and measure performance on intermediate checkpoints. We observe that new training objectives and architectures tend to produce the best-performing approaches, and that interaction with teacher models can yield high-quality language models. The strict and interaction tracks saw submissions that outperformed the best-performing methods from previous years. We do not observe a complete correlation between training FLOPs and performance. {\%}, suggesting that some methods can produce real gains beyond allowing us to spend more compute. This year{\'}s BabyLM Challenge shows that there is still room to innovate in a data-constrained setting, and that community-driven research can yield actionable insights for language modeling."
}