arXiv:2605.23857v1 Announce Type: new Abstract: Knowledge distillation generally assumes a strong-to-weak relationship where stronger teachers yield better students. In this work, we examine this assumption about distillation in large language model pretraining. By varying architecture sizes and training token budgets, we create strong-to-weak, same-level, and weak-to-strong teacher-student relationships, and study distillation's effectiveness under each. We find that the teacher need not be strong: with proper mixing of the language modeling and knowledge distillation losses, even small and u
The accelerating pace of large language model development and the increasing costs associated with pretraining compel researchers to find more efficient methods for model creation and improvement.
This research suggests that effective large language model distillation does not always require a stronger teacher, potentially democratizing access to powerful models and reducing computational requirements.
The paradigm for enterprise LLM development could shift, allowing smaller models to achieve performance comparable to larger ones, thereby reducing computational cost and environmental footprint.
- · AI startups (small LLMs)
- · Cloud providers (cost efficiency)
- · Developers (easier access)
- · Researchers (new distillation methods)
- · Companies reliant on massive compute for leadership
More efficient and accessible LLMs will accelerate AI integration across various industries.
Reduced barriers to entry for developing competitive AI models could fragment the AI market and spur innovation from smaller players.
A proliferation of capable, smaller LLMs may lead to increased on-device AI capabilities and reduced reliance on centralized cloud-based solutions.
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Read at arXiv cs.LG