Issue 25, Oct 14, 2026

The Synthetic Planner Trap

Senior AI Engineer interview at Google DeepMind, and the interviewer asks:

“You want to fine-tune a dedicated planner model, but all you have are raw agent action logs. Nobody ever wrote down plans. How do you build the training set, and what’s the most dangerous flaw in the naive version?”

Don’t say: “Feed each log to GPT, have it summarize the trajectory into a plan, then fine-tune on task → plan.”

Why mining success logs quietly destroys an agent's ability to replan and how to structure leak-free datasets for long-horizon execution.

Share this trapShare on LinkedIn

The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.

Read it on Substack

Get the next one

Free on Substack. Unsubscribe in one click.

More traps set at Google DeepMind