Previously test_training_throughput_measurement and test_real_data_single_epoch
only asserted loss.is_finite() on the final metric. That passes on trivial
zeros, on huge-but-finite NaN-disguised values, and on any regression that
doesn't produce literal NaN — giving effectively no signal.
test_training_throughput_measurement now asserts:
- loss finite AND non-negative
- epochs_trained >= 1
- throughput floor: epochs_per_sec > 0.05 (i.e. each epoch < 20s on the
RTX 3050 Ti; catches accidental CPU fallback or kernel CPU-pinning).
Documented as a conservative local floor; CI may tighten.
- avg_q_value present, finite, |avg_q| < 1e6 (rules out finite-but-huge
NaN propagation)
test_real_data_single_epoch now asserts:
- loss finite, non-negative, and < 1e8 (a real DQN loss of 0.0 is a
sign-bug or accumulation-bug tell; huge-but-finite rules out NaN
propagation)
- epochs_trained >= 1
- avg_q_value finite and |avg_q| < 1e6
Why these are safe:
- Bounds are chosen from observed smoke runs with 2-3 orders of margin.
- Passes locally in 1.58s and 10.24s respectively.
- Designed to flag regressions, not true production-scale deviations.
Verified PASS on laptop (RTX 3050 Ti).
0472b97300