@inproceedings{padovani-etal-2025-dialogue,
    title = "Dialogue Is Not Enough to Make a Communicative {B}aby{LM} (But Neither Is Developmentally Inspired Reinforcement Learning)",
    author = "Padovani, Francesca  and
      Bunzeck, Bastian  and
      Ali, Manar  and
      Momen, Omar  and
      Bisazza, Arianna  and
      Buschmeier, Hendrik  and
      Zarrie{\ss}, Sina",
    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://preview.aclanthology.org/ingest-emnlp/2025.babylm-main.29/",
    pages = "421--435",
    ISBN = "TODO",
    abstract = "We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we employ a variety of fine-tuning strategies to enforce ``more communicative'' text generations by our models. Although our models underperform on most standard BabyLM benchmarks, they excel at dialogue continuation prediction in a minimal pair setting. While PPO fine-tuning has mixed to adversarial effects on our models, DPO fine-tuning further improves their performance on our custom dialogue benchmark."
}