Keyu Wang

The work introduces DepthBench, a controlled benchmark that varies the width–depth aspect ratio (d_model/n_layer) from shallow-wide to deep-narrow under a fixed parameter budget and pre-training recipe, comparing 10 representative residual and normalization architectures, and finds that the benefit of depth is strongly architecture-dependent: Pre-LN and most of its norm- and scaling-based variants worsen as models get deeper and narrower (Pre-LN rises from 2.759 to 2.782), whereas HC and Full AttnRes improve consistently (HC from 2.729 to 2.699, Full AttnRes from 2.751 to 2.718), accompanied by stronger cross-layer causal dependence and order sensitivity, though deeper models also incur compute and memory overhead.

The work introduces DepthBench, a controlled benchmark that varies the width–depth aspect ratio (d_model/n_layer) from shallow-wide to deep-narrow under a fixed parameter budget and pre-training recipe, comparing 10 representative residual and normalization architectures, and finds that the benefit of depth is strongly architecture-dependent: Pre-LN and most of its norm- and scaling-based variants worsen as models get deeper and narrower (Pre-LN rises from 2.759 to 2.782), whereas HC and Full AttnRes improve consistently (HC from 2.729 to 2.699, Full AttnRes from 2.751 to 2.718), accompanied by stronger cross-layer causal dependence and order sensitivity, though deeper models also incur compute and memory overhead.