KDFP: A first-principles approach to knowledge distillation in large language models
Knowledge distillation is an established technique for improving the capabilities of small, efficient student models by training them with the representations of larger, more capable teacher models.
Key points
- Much of the recent work in the distillation of large language models (LLMs) has focused on distilling abilities learned during post-training, such as instruction following, chain-of-thought reasoning, and tool usage.
- This has left a large research gap in general knowledge distillation for LLMs, which is essential for developing efficient and private systems suitable for deployment on edge devices.
Sources (1)
- [1]KDFP: A first-principles approach to knowledge distillation in large language modelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 07:59 PM
Knowledge distillation is an established technique for improving the capabilities of small, efficient student models by training them with the representations of larger, more capable teacher models.
Much of the recent work in the distillation of large language models (LLMs) has focused on distilling abilities learned during post-training, such as instruction following, chain-of-thought reasoning, and tool usage.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 7, 2026MIMESIS: Learning User Simulators as Training Environments for Interactive Agents
- Oct 7, 2026Composing What Each Teacher Learned: Multi-Teacher On-Policy Distillation through Teacher-Relative Shifts
- Oct 7, 2026On-Policy Distillation Teaches New Skills but Not New Knowledge
- Oct 7, 2026UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy
- Oct 6, 2026Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight
- Oct 6, 2026RISED: Rubrics for Agentic Multi-Environment Selection and Self-Distillation