arXiv:2606.00306v1 Announce Type: new Abstract: Reverse Kullback-Leibler (RKL) divergence is widely favored over forward KL (FKL) in large language models (LLM) distillation, yet this preference is largely based on comparisons that omit the temperature $\tau$, overlooking its central role in softening teacher distributions and improving knowledge transfer. In this work, we revisit temperature in LLM distillation and show that it fundamentally changes the comparison between FKL and RKL. Our analysis reveals an asymmetric effect: temperature substantially enriches FKL with non-dominant token sig
This research is emerging as the field of large language model distillation matures, with researchers seeking to optimize knowledge transfer efficiency and performance.
Understanding the role of temperature in LLM distillation can lead to more efficient and effective model training, impacting the development and deployment of AI systems.
The fundamental understanding and application of temperature in the comparison between forward and reverse Kullback-Leibler divergence for LLM distillation is changing.
- · AI researchers
- · LLM developers
- · Companies with limited compute
- · Inefficient LLM distillation methods
Improved methods for distilling large language models, leading to smaller, more performant models.
Reduced computational costs for deploying advanced AI capabilities, increasing accessibility.
Acceleration of AI integration into various applications as development becomes more efficient and less resource-intensive.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG