Эксперимент: Pick-to-Learn Safety Fine-Tuning (#492)

{ "worked": true, "confidence": 8, "verdict": "Built a self-contained Pick-to-Learn safety fine-tuning toy experiment with worst-violation selection, differentiable safety penalties, held-out certification, and fixed seeds. The mechanism manifested: observed order-statistic maxima matched B/(B+1) within 0.001, the certificate correction had log-log slope -0.500 versus the predicted -0.5, and Pick-to-Learn reduced held-out violation rate from 0.2331 to 0.0837 at equal simulator budget.", "metrics": { "baseline": "Uniform updates: theta=0.8085, violation rate=0.2331, maximum normalized violation=0.1885, mean violation=0.02197.", "idea": "Pick-to-Learn: theta=0.6766, violation rate=0.0837, maximum normalized violation=0.0566, mean violation=0.002368. Held-out N=600: 52 violations, phat=0.0867, 95% Hoeffding upper bound=0.1366. Certificate epsilon slope=-0.500." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "run.log" ], "limitations": "This is a one-step scalar stochastic safety abstraction rather than a 2D multi-step simulator or full neural policy. It does not test RL-scale sample efficiency, Brownian dynamics, top-q selection, model misspecification, or a K-dependent certificate." }