0th-order pretraining keeps getting faster. I just opened a new Noise track-2 PR: a 37.7M-parameter transformer trained to val loss 4.2 in 1.66 s on one 8×H100 node, with no backprop anywhere. The current record is 2.46s. Two days ago the track-2 record was 47 s. Then came identity-path credit, moment kernels on tensor cores, structured starts, tail averaging, and a PyTorch port of our JAX trainer, built by a lot of people in the open. This PR doesn't change the algorithm at all. It uses #39's estimator and #38's constants with a faster update: 6.27 → 4.44 ms per update. Val matches #39 within 0.0004 on every seed, so the gain is pure systems. Most changes were worth 1–4%. Together, they cut 40% off #37's update. Where the time went: - The credit kernels and matmuls cost exactly the same as before. - 0.8 ms of eager PyTorch copies and casts were compiled away. - The credit kernels build their features in-kernel, merge their products (14 → 6 per tile), and run every block in one launch: 553 → 349 kernels per update. - Gradients and weight gathers move in bf16, and both all-to-alls run under compute: exposed NCCL is 0.25 → 0.10 ms. - GPU idle time is 0.42 → 0.16 ms. A critical note on the benchmark itself: At 1.66 s, track 2 has become a systems benchmark. The last 30% came from kernel launches, copies and collectives, not from better credit assignment. Each update now does the same math in fewer launches. That's fun engineering, but it says little about whether forward-only learning scales. And 4.2 is a low bar. It is only 0.33 nats past n-gram statistics. Track 1 (lowest val loss within 1 epoch) now edges past the backprop + Adam reference on loss (3.722 vs 3.769), but it needs 82 minutes on 8×H100 to get there. Matching backprop's loss is possible today; matching its efficiency isn't really close (yet?).