arXiv:2605.16865v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasoning and general-domain performance. We argue this forgetting arises because fine-tuning targets from humans or external systems diverge from the model's autoregressive distribution, forcing the optimizer to imitate low-probability token sequences. To address this problem, we propose MixSD, a simple external-teacher-free method for distribution-aligned knowledge injection. Instead of training on
The continuous development in AI necessitates better methods for knowledge injection without compromising existing model capabilities, making new research like MixSD timely.
Improving how new knowledge is injected into language models without 'catastrophic forgetting' is crucial for developing robust, general-purpose AI and accelerating AI agent development.
This research proposes a method that could allow for more efficient and less destructive updates to large language models, potentially speeding up iterative development and application.
- · AI developers
- · Companies using SFT
- · AI research community
- · Methods causing significant model degradation
- · Developers reliant on complex retraining
Language models can be updated with new knowledge more effectively while retaining established abilities.
Faster iteration cycles for AI development and deployment, leading to more capable and adaptable AI systems.
Accelerated progress in autonomous AI agents that can continuously learn and adapt without significant performance decay.
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