A research group at the Institute for Applied Frustration has published findings confirming what many in the field had long suspected: that the optimization landscape of deep neural networks bears a striking resemblance to the emotional landscape of the researchers who train them. The study, conducted over 18 months with a cohort of 12 doctoral students and one post-doc who “technically already had his degree but was still being paid like a student,” tracked convergence behavior across both loss curves and psychological wellbeing assessments administered on a biweekly basis.
The results were unambiguous. All 12 participants converged to local minima in their training objectives by month six, a finding the team attributed to a learning rate that was “set too high initially, then overcorrected to something so small the model essentially stopped learning.” Simultaneously, scores on the Generalized Anxiety and Existential Dread Scale (GAEDS) plateaued at a level the authors describe as “suboptimal but stable, like a ResNet that technically works but you wouldn’t publish it.” The post-doc’s metrics were excluded from the primary analysis as an outlier, though a footnote notes he “seemed fine, which was deeply unsettling to everyone.”
Attempts to escape the local minima using standard techniques proved instructive. Stochastic gradient descent with momentum succeeded in moving the loss function slightly, at the cost of introducing high variance into the researchers’ sleep schedules. Warmup scheduling showed promise in the first two months before the team collectively forgot they had implemented it. The Adam optimizer was adopted midway through the study and described by participants as “better, I guess, though I’m not sure I understand why,” a sentiment the authors note is “consistent with the broader literature’s relationship to adaptive methods.”
The paper concludes with a recommendation that future training runs incorporate more careful monitoring of researcher convergence alongside model convergence, and suggests that “periodic learning rate resets, extended holidays, and the genuine possibility of graduation” may serve as effective escape strategies. The code and psychological assessment instruments are available on GitHub, though the team notes the repository “has not been touched since the paper was accepted” and “several of the file paths are wrong.”