Chain-of-Models Pre-Training: Rethinking Training Acceleration of Vision Foundation Models

Researchers present Chain-of-Models Pre-Training (CoM-PT), a novel method that accelerates vision foundation model training by up to 7.09X through sequential knowledge transfer from smaller to larger models in a unified pipeline, rather than training each model independently. The approach maintains or improves performance while significantly reducing computational costs, with efficiency gains increasing as more models are added to the training sequence.

CoM-PT addresses a fundamental inefficiency in how vision foundation model families are typically developed. Rather than treating each model size as an isolated training problem, the method creates an interconnected training pipeline where smaller models serve as knowledge sources for larger ones. This represents a paradigm shift in thinking about model development at scale, moving from individual optimization to family-level efficiency.

The technical innovation leverages knowledge transfer across both parameter and feature spaces, allowing downstream models to inherit learned representations from their predecessors. The validation across 45 datasets demonstrates practical viability beyond theoretical claims. The counterintuitive finding that adding more models to the chain improves overall efficiency suggests the method unlocks emergent properties in knowledge reuse—training a 7-model family proves more efficient than a 3-model family, creating accelerating returns rather than diminishing ones.

For the AI infrastructure ecosystem, this work has significant implications. Organizations training vision foundation model families face substantial computational costs; a 7.09X acceleration translates directly to reduced energy consumption, faster time-to-market, and lower capital expenditure. This becomes increasingly valuable as models grow larger and more expensive to train. The authors' commitment to open-sourcing the code positions this as a potential standard practice across the industry.

The framework's agnosticism to specific pre-training paradigms suggests broader applicability beyond vision models. The explicit mention of potential extensions to large language model pre-training indicates the authors view this as a foundational approach that could reshape how entire model families are developed across modalities.

• →CoM-PT achieves up to 7.09X training acceleration for vision foundation model families through sequential inverse knowledge transfer
• →Training efficiency paradoxically increases as more models are added to the model chain, contrary to typical scaling dynamics
• →The method maintains or exceeds performance of independently trained models while dramatically reducing computational costs
• →Open-source release enables potential adoption across vision and language model pre-training pipelines
• →Knowledge transfer occurs simultaneously in parameter and feature spaces, enabling efficient multi-model family development