Non-record: 6-Technique Stack — Catalytic Residuals + Value Residual + Gated Attention + BigramHash(10240) + 12L (val_bpb=1.1690)

[joshuaswarren]

Non-record: 6-Technique Stack (val_bpb=1.1690, 8xH100 SXM)

First submission combining 6 independently-proven architecture improvements never stacked together:

Technique	Source	Impact
Catalytic Residuals	PR #450	-0.024 bpb
Value Residual (ResFormer)	PR #413, arXiv:2410.17897	-0.015 bpb
Gated Attention	PR #413, arXiv:2505.06708	-0.003 bpb
BigramHash(10240)	PR #450	-0.070 bpb vs 2048
12 Layers	PR #450	-0.023 bpb vs 11L
3x MLP	Merged SOTA	Standard

Results

• Sliding window val_bpb: 1.1690 (stride 64)
• Post-quant roundtrip: 1.2043
• Pre-quant: 1.1911
• 6,981 steps at 85.78 ms/step
• Artifact: 15.3 MB (int6+zstd, under 16MB)
• Training: 598.8s on 8xH100 SXM

Additional techniques

OrthoInit, Muon WD=0.04, SWA (last 20%), Late QAT (threshold 0.25), logit softcap 30.0

Run

pip install sentencepiece zstandard
python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 80
torchrun --standalone --nproc_per_node=8 train_gpt.py

All hyperparameters set as defaults.

[mohosy]

value residual + gated attention on 12 layers is alot of new stuff at once lol. did you try adding them one at a time to see which ones actualy helped or did you just yolo the whole stack

[MatoTeziTanka]

Community Review — Non-record: 6-Technique Stack — Catalytic Residuals + Value Residual + Gated Attention + BigramHash(10240) + 12L (val_bpb=1.1690)

BPB: 1.1690 | Compliance: LOOKS CLEAN — pure-neural submission, no TTT/SLOT/n-gram-cache

What I found in the code (head SHA cd3b1385d524, file records/track_10min_16mb/2026-03-22_6TechniqueStack_CatalyticRes_ValueRes_GatedAttn_BigramHash10240/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 0.02s, dim=512, layers=12, vocab=1024, code=60630 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 0.02s, dim=512, layers=12, vocab=1024, code=60630 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.