With per-component clipping removed, raw gradient norms are ~4000.
The old gradient_clip_norm=1.0 (calibrated for pre-clipped norms ~7)
discarded 99.97% of gradient magnitude every step, making the effective
learning rate 570× too small. Q-values grew 9× slower than old run.
10000 lets normal gradients (~4000) pass through unclipped. Only fires
on genuine divergence spikes (>2.5× normal). Adam's internal adaptive
step sizing handles the rest.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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