We revisit continual pre-training for large language models and argue that progress now depends less on scaling parameters than on scaling the right structure. We introduce SCALE, a width upscaling architecture that inserts lightweight expansions into linear modules while freezing all pre-trained parameters, preserving residual and attention topologies and increasing capacity without perturbing the base model’s original functionality. SCALE follows two principles: Persistent Preservation, which maintains the base model’s behavior via preservation-oriented initialization and freezing of the pre-trained weights, and Collaborative Adaptation, which trains only selected expansion components to acquire new knowledge with minimal interference. We instantiate these ideas as SCALE-Preserve (preservation-first), SCALE-Adapt (adaptation-first), and SCALE-Route, an optional routing extension that performs token-level routing between preservation and adaptation heads. On a controlled synthetic biography benchmark, SCALE reduces the severe forgetting seen in depth expansion while still learning new knowledge. In continual pre-training on a Korean corpus, SCALE variants forget less on English evaluations and achieve competitive gains on Korean benchmarks, yielding the best overall stability-plasticity trade-off. We further analyze when preservation holds provably and why combining preservation and adaptation stabilizes optimization relative to standard continual learning.