1Cademy - Diagnosing Pre-training Objective Mismatch from Product Failures

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Essay

Diagnosing Pre-training Objective Mismatch from Product Failures

You lead an internal team building an LLM-based assistant for a regulated enterprise. After pre-training and light fine-tuning, you observe three recurring failures in pilot use:

In long-form drafting, the model often contradicts itself across paragraphs and sometimes “jumps” to content that would only make sense if it had seen later text.
In document review, the model is strong at filling in missing words inside a sentence, but it is unreliable at deciding whether a proposed follow-up sentence is a coherent continuation of the previous one (e.g., it misses topic shifts and non sequiturs).
In reconstruction-style tasks (e.g., restoring a partially corrupted policy excerpt), the model sometimes produces fluent text that does not faithfully match the original wording, even when the corruption is minor.

Assume you can choose among these pre-training objectives (alone or in combination): masked token prediction, next-sentence relationship classification, left-to-right next-token prediction, denoising reconstruction from corrupted inputs using an encoder-decoder, and permuted-order token prediction.

Write a recommendation memo that (a) identifies the most likely objective-level causes of the three failures, and (b) proposes a revised objective mix (and why) that would reduce all three issues. Your answer must explicitly connect each proposed change to how information is (or is not) allowed to flow during training (bidirectional context vs strictly past context vs permuted context) and to the difference between predicting a few tokens vs reconstructing an entire sequence vs classifying sentence-pair coherence.

Updated 2026-02-06

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