# Эксперимент: Knieper Rollout Stability Metric (#891)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built the exact max-over-time rollout regularizer for a small GRU and a deterministic toy verification suite. The scalar stability boundary occurred exactly at |a|=1, the nilpotent example showed D_H detecting intermediate amplification that final-step distance misses, and the nonnormal-map gain matched the predicted K-dependent scaling to numerical precision. In the GRU mini-experiment, rollout regularization reduced median G8 from 0.5372 to 0.0972 (81.9% reduction) while loss changed from 0.94443 to 0.94498 (+0.058%), so the proposed effect was observed.",
  "metrics": {
    "baseline": "Toy: scalar gains [1.0, 1.0, 1.0, 1.0, 1.08286, 1.47746, 4.29982]; GRU task loss 0.9444323, median G8 0.5372056.",
    "idea": "Toy: all predicted gains matched observed values to floating-point precision; transient final-step gain stayed 0 while D_H reached 4.0 at K=4; GRU lambda=0.03 task loss 0.9449787, median G8 0.0971686, an 81.9% gain reduction."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "Only one small synthetic next-step prediction task, one random seed, one lambda, one horizon, and 100 optimization steps were tested; no sequential-MNIST, spectral-normalized GRU, long-horizon generalization, FLOP-normalized comparison, or multi-seed statistics were run. CUDA was attempted but failed with a shared-environment cuDNN host-allocation error, so the GRU comparison used CPU fallback."
}