Эксперимент: Semiglobal-PL Phase Scheduler (#816)

{ "worked": true, "confidence": 7, "verdict": "Built the Semiglobal-PL Phase Scheduler with gradient-norm/loss-gap diagnostics, rolling percentile detection, clipping, and a one-time 1.5x learning-rate increase. The LQR checks confirmed the claimed math: q-formula relative error was 1.3e-10, gradient-flow log-gap rates agreed with -q² within 0.07%, discrete rates agreed within 0.1%, and the far-field absolute rate approached 0.25. On the small synthetic MLP, scheduled SGD improved mean final validation loss from 0.3226 to 0.2403 and reduced tail validation standard deviation from 0.0164 to 0.0128 across three seeds, providing a promising but not definitive practical signal.", "metrics": { "baseline": "Fixed SGD, lr=0.08, identical norm clipping: final validation loss 0.32265 +/- 0.02972; last-100 validation std 0.01642 +/- 0.00202.", "idea": "Scheduled SGD: lr increased to 0.12 at step 23 in all three runs; final validation loss 0.24034 +/- 0.01743; last-100 validation std 0.01283 +/- 0.00193. LQR checks passed with maximum reported relative rate error 0.00064." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "run_output.txt" ], "limitations": "The neural experiment used a tiny synthetic 2D classification task and full-batch SGD, not MNIST/CIFAR-10 or AdamW. Only three seeds and 500 updates were tested; the scheduler switched unusually early and clipping was inactive, so the gain may partly reflect the learning-rate increase rather than robust phase detection. No larger-scale generalization or FLOP study was performed." }