Learning from Reasoning Failures via Synthetic Data Generation
Gabriela Ben Melech Stan, Estelle Aflalo, Avinash Madasu, Vasudev Lal, Phillip Howard
Abstract
Training models on synthetic data has emerged as an increasingly important strategy for improving the performance of generative AI. This approach is particularly helpful for large multimodal models (LMMs) due to the relative scarcity of high-quality paired image-text data compared to language-only data. While a variety of methods have been proposed for generating large multimodal datasets, they do not tailor the synthetic data to address specific deficiencies in the reasoning abilities of LMMs which will be trained with the generated dataset. In contrast, humans often learn in a more efficient manner by seeking out examples related to the types of reasoning where they have failed previously. Inspired by this observation, we propose a new approach for synthetic data generation which is grounded in the analysis of an existing LMM
BibTeX
@inproceedings{aaai2026_learningfromreas,
title = {Learning from Reasoning Failures via Synthetic Data Generation},
author = {Gabriela Ben Melech Stan and Estelle Aflalo and Avinash Madasu and Vasudev Lal and Phillip Howard},
booktitle = {AAAI 2026},
year = {2026}
}