Learning in biological brains is driven by errors and friction, not by comfortable success, and voluntary execution of non-preferred tasks causes measurable structural change. Agent self-improvement loops that only sample tasks already within capability miss the exact signal that triggers plasticity; a curriculum needs a controlled fraction of error-rich, non-preferred tasks.
An objective that minimizes error on the current distribution may actually defeat capability growth. The useful operation is to generate friction, expose failure states, and measure recovery from errors instead of hiding errors through relabeling or easy benchmarks.
Evaluation and continual-learning loops should deliberately include desirable difficulties—hard examples, time pressure, adversarial failures—because the same error signal that lowers current performance can build robust long-term structure.
Neuroplasticity is driven by errors and friction, not just comfortable success.
Cellular changes in the anterior midcingulate cortex occur when humans do things they do not want to do.