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arXiv · 2510.20358

Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)

Francesca Padovani·Bastian Bunzeck·Manar Ali·Omar Momen·Arianna Bisazza·Hendrik Buschmeier·Sina Zarrieß
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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.

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Francesca Padovani, Bastian Bunzeck, Manar Ali, Omar Momen, Arianna Bisazza, Hendrik Buschmeier, Sina Zarrieß. 2025-10-23. Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning). https://doi.org/10.18653/v1%2F2025.babylm-main.29

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