arXiv:2605.26246v1 Announce Type: new Abstract: Knowledge distillation (KD) transfers knowledge from a large teacher model to a smaller student. In language modeling, the student is trained either on tokens sampled from the teacher (hard labels) or the teacher's full next-token distribution (soft labels). Despite soft labels appear strictly richer, we find that mixing hard and soft labels consistently yields better results. Crucially, we show that this gain cannot be explained by closer teacher matching during training. Instead, it comes from reduced exposure bias, the mismatch between trainin
This research addresses a practical dilemma in LLM distillation, driven by the current need to optimize language models for efficiency and performance.
Improved distillation techniques lead to more efficient and capable smaller language models, which expands the deployability and accessibility of advanced AI.
The understanding of how to effectively train smaller LLMs to retain teacher knowledge is refined, offering a direct path to better student model performance.
- · AI developers
- · Cloud computing providers
- · Hardware manufacturers
- · Companies adopting AI
- · Inefficient LLM architectures
- · Users with limited computational resources if not adopted
More sophisticated and smaller language models become readily available for a wider range of applications.
The reduced computational demands for powerful LLMs could accelerate their integration into edge devices and specialized hardware.
This could democratize access to advanced AI capabilities, fostering innovation in areas previously limited by model size and cost.
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Read at arXiv cs.LG