@inproceedings{stepankova-etal-2026-impact,
title = "The Impact of Preference Optimization Data Selection: Teaching Practical Life Skills to {B}aby{LM}",
author = "{\v{S}}t{\v{e}}p{\'a}nkov{\'a}, Barbora and
Lanz, Vojt{\v{e}}ch and
Szymanik, Jakub",
editor = "Choshen, Leshem and
Cotterell, Ryan and
Gul, Mustafa Omer and
Jumelet, Jaap and
Linzen, Tal and
Mueller, Aaron and
Salhan, Suchir and
Shah, Raj Sanjay and
Warstadt, Alex and
Wilcox, Ethan Gotlieb",
booktitle = "Proceedings of the Second {B}aby{LM} Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.babylm-main.45/",
pages = "575--584",
ISBN = "979-8-89176-475-0",
abstract = "This work investigates how the choice of preference optimization data affects small language models in the BabyLM setting. We apply SimPO to a GPT-2 model pretrained on 9M words, using 21 different 1M-word preference datasets, including UltraFeedback, which we cluster by topic, grammatical, and Reddit-based data. We show that preference datasets of the same size lead to different downstream performance. A combination of Business Content Creation, Food and Cooking, and Math and Calculations, which we refer to as the Practical Life Skills dataset, consistently improves Entity Tracking, MultiRC, and WSC tasks. The Entity Tracking gains are largely related to the model{'}s ability to handle cases where ``nothing.'' is a valid option, raising the question of whether models' ability to abstain from selecting an answer in such cases or explicitly express uncertainty should receive more attention. Overall, we show that under strict data constraints, allocating the final 1M words to a different training objective can provide larger benefits than using them for additional pretraining."
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%0 Conference Proceedings
%T The Impact of Preference Optimization Data Selection: Teaching Practical Life Skills to BabyLM
%A Štěpánková, Barbora
%A Lanz, Vojtěch
%A Szymanik, Jakub
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Gul, Mustafa Omer
%Y Jumelet, Jaap
%Y Linzen, Tal
%Y Mueller, Aaron
%Y Salhan, Suchir
%Y Shah, Raj Sanjay
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%S Proceedings of the Second BabyLM Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-475-0
%F stepankova-etal-2026-impact
%X This work investigates how the choice of preference optimization data affects small language models in the BabyLM setting. We apply SimPO to a GPT-2 model pretrained on 9M words, using 21 different 1M-word preference datasets, including UltraFeedback, which we cluster by topic, grammatical, and Reddit-based data. We show that preference datasets of the same size lead to different downstream performance. A combination of Business Content Creation, Food and Cooking, and Math and Calculations, which we refer to as the Practical Life Skills dataset, consistently improves Entity Tracking, MultiRC, and WSC tasks. The Entity Tracking gains are largely related to the model’s ability to handle cases where “nothing.” is a valid option, raising the question of whether models’ ability to abstain from selecting an answer in such cases or explicitly express uncertainty should receive more attention. Overall, we show that under strict data constraints, allocating the final 1M words to a different training objective can provide larger benefits than using them for additional pretraining.
%U https://aclanthology.org/2026.babylm-main.45/
%P 575-584
Markdown (Informal)
[The Impact of Preference Optimization Data Selection: Teaching Practical Life Skills to BabyLM](https://aclanthology.org/2026.babylm-main.45/) (Štěpánková et al., BabyLM 2026)
ACL