Deterministic Continuous Replacement: Fast and Stable Module Replacement in Pretrained Transformers
- URL: http://arxiv.org/abs/2511.18670v1
- Date: Mon, 24 Nov 2025 00:55:14 GMT
- Title: Deterministic Continuous Replacement: Fast and Stable Module Replacement in Pretrained Transformers
- Abstract summary: Cold-start reinitialization destabilizes frozen backbones. We isolate this core stability challenge in a controlled study.<n>In a single-seed study, DCR attains faster convergence and stronger alignment than gating and distillation baselines on controlled attention replacement, establishing a foundation for heterogeneous operator swaps.
- Score: 0.7829352305480287
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Replacing modules in pretrained models, especially swapping quadratic self-attention for efficient attention alternatives, poses a hard optimization problem: cold-start reinitialization destabilizes frozen backbones. We isolate this core stability challenge in a controlled study. Deterministic Continuous Replacement (DCR) blends teacher and student outputs with a deterministic, annealed weight. Theoretically, DCR eliminates gate-induced gradient variance inherent to stochastic replacement. In a single-seed study, DCR attains faster convergence and stronger alignment than stochastic gating and distillation baselines on controlled attention replacement, establishing a foundation for heterogeneous operator swaps.
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