PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning
arXiv:2606.10369v1 Announce Type: new Abstract: As large language models (LLMs) continue to scale, it becomes increasingly challenging to grow model capacity under fixed computation budgets. We propose Path-Aligned Decompression Distillation (PADD), a framework for distilling knowledge from dense teachers without explicit routing into mixture-of-experts (MoE) students while learning high-quality routing policies. PADD organizes knowledge distillation into four stages in two phases: an initialization phase (Stage I) that builds diverse functionality in the student's experts through teacher neur
The continuous scaling of LLMs has reached a point where efficiency in training and inference under fixed computational budgets is paramount, driving innovation in model architectures like MoE.
This research introduces a novel distillation method that significantly improves the efficiency of training Mixture-of-Experts (MoE) models, which are crucial for scaling language models under resource constraints.
The ability to effectively distil knowledge from dense models into MoE students with high-quality routing policies will accelerate the development and deployment of more performant and cost-effective large language models.
- · AI developers
- · Cloud providers
- · Large language model users
- · Companies relying on inefficient LLM training
- · Hardware developers focused solely on brute-force scaling
More powerful and efficient LLMs become accessible for a wider range of applications and organizations.
Increased competition among AI companies as the barrier to developing sophisticated models is lowered through efficiency gains.
The proliferation of advanced AI capabilities could accelerate the development of autonomous AI agents across various sectors.
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