NaN/divergence during training is a valid PSO/TPE outcome — it means
the sampled hyperparameters are unstable. Score failed trials with 1e6
penalty so the optimizer learns to avoid that region, instead of
aborting all remaining trials.
The PSO/argmin paths already handled this (lines 1118, 1261). Only
the shared evaluate_point (TPE path) propagated errors as fatal.
Tested: 6 trials × 20 epochs on RTX 3050 — 2 NaN trials scored as
penalty, optimizer completed all 6 trials in 597s.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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