The loss every language model trains on, built from logits with no torch in sight. The screen for whether you understand log-softmax and the numerical-stability trick, not just import nn.CrossEntropyLoss.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
The loss every language model trains on, built from logits with no torch in sight. The screen for whether you understand log-softmax and the numerical-stability trick, not just import nn.CrossEntropyLoss.
The single discriminator is the log-sum-exp trick. A candidate who writes log(sum(exp(logits))) without subtracting the max has shipped an overflow that NaNs the moment logits get large, which they do during training. The second tell is computing softmax then taking its log separately (two passes, loses precision) instead of log-softmax directly. The follow-up linking cross-entropy to perplexity is where you separate people who debugged a training run from people who memorized a formula.
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