No. First do the multiplication: data-parallel workers × microbatches accumulated per worker × examples per microbatch. If only the worker count halves and accumulation doubles, the example count per optimizer step stays the same. But even that arithmetic assumes that each example contributes equally to the objective and that every worker participates in one logical step. With variable-length packed sequences or masked supervision, “same number of examples” can mean a different number of loss-bearing tokens. Averaging a mean loss from each microbatch gives a short microbatch as much weight as a long one unless the reduction is designed for the actual target tokens.

I would compare a single effective step from both configurations using the same ordered examples. Record the exact sample IDs, target-token count, per-microbatch loss numerator and denominator, gradient before clipping, reduction across workers, optimizer state and the update applied. For a token-average objective, sum token losses over the effective batch and divide by the total target-token count, with distributed reduction handled consistently. Dividing each microbatch's already averaged loss by the number of accumulation steps is only equivalent when the relevant denominators are equal. PyTorch's mixed-precision accumulation example also makes the accumulation boundary for scaling, unscaling and stepping explicit. We must inspect the actual trainer's reduction convention rather than copy a snippet blindly.

Even if the first step matches, long trajectories need not be bitwise identical. Worker sharding and shuffling may give different examples in each step. Dropout consumes random numbers differently. Collective reduction order changes floating-point rounding. If the learning-rate scheduler advances per microbatch instead of per optimizer step or per consumed token, doubling accumulation changes the schedule. Gradient clipping before full accumulation is not the same as clipping the effective gradient. A failed microbatch followed by a skip must have a globally consistent outcome. Check whether checkpoint restore also reshards optimizer state, which is a separate problem covered by A training job restarts with half as many GPUs. Can its optimizer state be loaded safely?.

The decision is what level of equivalence the project requires. If this is a continuation of a high-cost run, freeze the data stream and recipe, compare a few steps numerically within tolerances, then run a short matched validation pilot. Track consumed target tokens and optimizer steps, not wall-clock time alone. If exact numerical replay is impossible across topology, say so, and establish that quality and stability remain inside a measured range. Do not explain away a persistent cohort regression as random noise before finding the first place the two recipes differ.

If the interviewer says the example count and gradient norm match, I would still ask whether the direction and per-layer update match, and whether the schedule saw the same number of tokens. Two gradients can have the same norm while pointing somewhere else. The effective-batch formula is a useful starting invariant, not the whole training contract.