Closing the Data-Efficiency Gap Between Autoregressive and Masked Diffusion LLMs

ICLR 2026 Conference Submission13765 Authors

Keywords: LLM, diffusion language model, knowledge injection, fine-tuning

Abstract: Despite autoregressive large language models (arLLMs) being the current dominant paradigm in language modeling, effectively updating these models to incorporate new factual knowledge still remains difficult. They resist knowledge injection via fine-tuning due to inherent shortcomings such as the "reversal curse" — the challenge of answering questions that reverse the original information order in the training sample. Masked diffusion large language models (dLLMs) are rapidly emerging as a powerful alternative to the arLLM paradigm, with evidence of better data efficiency and free of the "reversal curse" in pre-training. However, it is unknown whether these advantages extend to the post-training phase, i.e. whether pre-trained dLLMs can easily acquire new knowledge through fine-tuning. On three diverse datasets, we fine-tune arLLMs and dLLMs, evaluating them with forward and backward style Question Answering (QA) to probe knowledge generalization and the reversal curse. Our results confirm that arLLMs critically rely on extensive data augmentation via paraphrases for QA generalization, and paraphrases are only effective when their information order matches the QA style. Conversely, dLLMs achieve high accuracies on both forward and backward QAs without paraphrases; adding paraphrases yields only marginal gains. Inspired by the dLLM's performance, we introduce a novel masked fine-tuning paradigm for knowledge injection into pre-trained arLLMs. This proposed method successfully and drastically improves the data efficiency of arLLM fine-tuning, effectively closing its performance gap with dLLMs. We further show that the masked fine-tuning paradigm of arLLMs can be extended to the supervised fine-tuning (SFT) of mathematical capability. Across two models and two datasets, our masked SFT outperforms regular SFT.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 13765