record: val_bpb=1.1622, NorMuon + int6 STE + SWA + sliding window

[vmfunc]

mean val_bpb=1.1622 across 3 seeds on 8xH100 (1.1624, 1.1623, 1.1618). stacks six orthogonal improvements:

• int6 STE, fake per-row int6 quantization during training w/ straight-through estimator. model learns to handle post-training quant. gap is only +0.002 bpb
• fp16 embedding passthrough tied embed/logit head kept in fp16 instead of quantized, most quant-sensitive tensor, no STE protection
• MLP 3x (1536 hidden) int6 compression frees enough artifact bytes to fit the wider model
• NorMuon row-normalized Newton-Schulz updates (from modded-nanogpt) second-moment normalization on top of Muon
• SWA over 7 checkpoints during warmdown
• sliding window eval stride=64 (every scored token gets 960 tokens of context) ~0.033 bpb improvement

run	seed	steps	post-quant bpb	sliding window bpb
1	1337	11917	1.1956	1.1624
2	42	11925	1.1955	1.1623
3	2025	11917	1.1951	1.1618

artifact: 15.5MB (code 54KB + int6+zstd model 15.4MB). ~50ms/step, 600s wall clock

[MatoTeziTanka]

Community Review — record: val_bpb=1.1622, NorMuon + int6 STE + SWA + sliding window

BPB: 1.1622 | Compliance: LOOKS CLEAN — pure-neural submission, no TTT/SLOT/n-gram-cache

What I found in the code (head SHA c887ef435460, file records/track_10min_16mb/2026-03-19_NorMuon_Int6STE_SWA_SlidingWindow/train_gpt.py):

Static code review found no TTT adaptation function, no SLOT optimization loop, no n-gram-cache class, and no pre-quant val-token fine-tune. The eval path uses the standard sliding-window stride-64 pattern. The submission is a pure-neural architecture iteration on the standard SP1024/SP4096/SP8192 baseline.

CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.04s, dim=512, layers=9, vocab=1024, code=54361 B, SMOKE_TEST_PASS

Verdict: LOOKS CLEAN.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: MERGE pending the usual record-track checks (3-seed validation, under-16MB artifact cap, ≤600s train + ≤600s eval on 8×H100 SXM). No compliance flags from the classification pass — this looks like a clean pure-neural iteration on the standard baseline.

Auto-classification caveat: this review was drafted by the AST-based classifier. If there's a non-standard eval mechanism (logit postprocessing, hedge mixing, etc.) that I missed because it's factored into a helper file or a non-standard function name, please flag it and I'll re-run the audit manually.

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Reviewed by @MatoTeziTanka — The Agora. CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.04s, dim=512, layers=9, vocab=1024, code=54361 B, SMOKE_TEST_PASS. Classification via deterministic AST-based classify_prs.py (pattern bank derived from ~65 manually-reviewed PRs earlier in the 2026-04-11 sweep). This review was auto-drafted from a template and spot-checked before posting — if the template misread your code, please call it out so I can iterate the classifier.