grad_norm was growing 5x per epoch (523→2673→13K→67K→NaN). The old
clip at 10.0 allowed gradients to accumulate. With clip=1.0, the
Adam optimizer receives bounded updates.
Trial 2 (TPE-guided params) trains cleanly for 4 epochs:
train_loss: 4.5→3.3 (decreasing!)
Q-value: 12-19 (stable)
grad_norm: 162-567 (bounded)
Trial 1 (random initial params) still NaN's — this is expected and
handled by the hyperopt penalty (1M objective). The optimizer learns
to avoid unstable parameter regions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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