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Diagnose optimization traces
Tutor me on https://mlprep.iwase.dev/item/ml-screen-optimization-advanced-diagnostics/. This is a screening item. If window.mlPrepAgent is available, read attempts for item ml-screen-optimization-advanced-diagnostics before tutoring. Inspect my attempt, keep the item ID, and do not reveal the full answer first. After a real attempt, append its record with recorded_by agent, include agent_session_id when available, and read it back.Problem
Diagnose and give one action for each trace.
- Loss is ; parameter norms grow and gradients stay large.
- Loss is stable; the gradient norm is ; the Hessian is positive definite.
- Data-fit loss is low, but the total objective is dominated by a very large penalty.
Reveal answer or reference solution
Trace indicates an excessively large learning rate; reduce it or use a line search. Trace is a converged strict local minimum; stop after checking tolerance. Trace indicates overly strong regularization; reduce the penalty coefficient if validation performance supports the change. The data-fit term alone is not the stated total objective.
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