Every block adds an update to a stream that travels through the whole network. If the residual branch is too large, many such updates can change the activation and gradient scales as depth grows. The exact behavior depends on pre-norm versus post-norm, initialization, the residual stream, optimizer and precision. DeepNet studies a depth-aware residual modification together with initialization for very deep transformers. It does not say every model should use the same coefficient. Here the bug is removing a coefficient from a trained or tested architecture without preserving the computation or revalidating its training recipe.

I would first establish whether this is a checkpoint compatibility failure or a new run failure. If weights were trained with α, changing the inference or resume graph changes the represented function immediately. Compare layer outputs and logits before any optimizer step. If training from scratch, compare residual-stream RMS, branch-to-stream norm ratios, gradients by depth, nonfinite rates and the first step where the two runs diverge. Use the same data, initialization seed and effective batch. Do not infer from one global loss curve that “layer 74 exploded” unless the per-layer trace supports it.

The correction is to restore the intended scaling in the optimized path and add a numerical parity test on representative inputs. If the team wants a new residual design, pair it with the appropriate initialization and stability experiments, then treat it as a model change. A simple rescale at the end of the network is not generally equivalent to scaling every branch before subsequent nonlinear and attention operations. The exact α may vary by sublayer and architecture. Write the computation down at the block boundary rather than assuming two implementations with the same tensor shapes are equivalent.

An interviewer might point to A 12-layer Transformer trains. Why does the 48-layer version fail after moving LayerNorm?, where moving LayerNorm changed the 48-layer model's behavior. The connection is depth sensitivity, but the changed operation is different. In this question the norm placement can stay fixed. The residual update magnitude itself changed at every layer, which can ruin both checkpoint equivalence and deep optimization.