arXiv:2606.11189v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) typically maximizes the likelihood of every token in a demonstrated trajectory. However, an observed token can be non-unique, noisy, or misaligned with the model prior. Strictly fitting toward this one-hot target may be suboptimal, especially when the pretrained model encodes a rich knowledge prior. In this work, we reinterpret SFT as target distribution design: instead of studying only the loss objective, we analyze the token-level target that the loss drives the model to match. We introduce the Q-target framework,
The paper introduces a novel framework for supervised fine-tuning at a time when researchers are actively seeking more efficient and effective methods to align large language models.
This research provides a theoretical and practical framework (Q-target) that could significantly improve the performance and alignment of AI models, moving beyond sub-optimal one-hot target fitting.
The focus shifts from merely optimizing loss objectives to intelligently designing the target distribution for token-level matching, potentially making model fine-tuning more robust and effective.
- · AI researchers
- · Model developers
- · Companies deploying LLMs
- · Inefficient SFT methods
- · Models reliant on simplistic fine-tuning
Improved fine-tuning techniques lead to more performant and reliable large language models.
Enhanced model capabilities accelerate the development and deployment of more sophisticated AI applications and agents.
More aligned and capable AI models could allow for the automation of complex tasks, impacting various industries and labor markets.
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