Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate

• URL: http://arxiv.org/abs/2507.07129v2
• Date: Tue, 04 Nov 2025 18:17:08 GMT
• Title: Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate
• Authors: A. Bochkov,
• Abstract summary: The prevailing paradigm for scaling large language models (LLMs) involves monolithic, end-to-end training.<n>This paper explores an alternative, constructive scaling paradigm, enabled by the principle of emergent semantics in Transformers.<n>We operationalize this with a layer-wise constructive methodology that combines strict layer freezing in early stages with efficient, holistic fine-tuning of the entire model stack.
• Score: 1.0152838128195467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The prevailing paradigm for scaling large language models (LLMs) involves monolithic, end-to-end training, a resource-intensive process that lacks flexibility. This paper explores an alternative, constructive scaling paradigm, enabled by the principle of emergent semantics in Transformers with frozen, non-semantic input embeddings. We posit that because high-level meaning is a compositional property of a Transformer's deep layers, not its input vectors, the embedding layer and trained lower layers can serve as a fixed foundation. This liberates backpropagation to focus solely on newly added components, making incremental growth viable. We operationalize this with a layer-wise constructive methodology that combines strict layer freezing in early stages with efficient, holistic fine-tuning of the entire model stack via low-rank adaptation (LoRA) as complexity increases. This method not only demonstrates stable convergence but also reveals a direct correlation between model depth and the emergence of complex reasoning abilities, such as those required for SQuAD, which are absent in shallower models. In a controlled study, our constructively grown model rivals the performance of a monolithically trained baseline of the same size, validating the efficiency and efficacy of the approach. Our findings suggest a path towards a paradigm shift from monolithic optimization towards a more biological or constructive model of AI development. This opens a path for more resource-efficient scaling, continual learning, and a more modular approach to building powerful AI systems. We release all code and models to facilitate further research.

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