10L XSA + EMA + Partial RoPE + LN Scale (val_bpb: 1.1365)

[ofirkris]

Results

Seed	val_bpb	Artifact	Steps
42	1.1365	15,759,319	6491
1337	1.1366	15,820,386	6520

• 8xH100 SXM, 600s, 92ms/step
• PyTorch 2.7.0 + FlashAttention 2.8.3
• No TTT, no test-time adaptation

Techniques

10L 512d, 3x MLP, XSA last 4 layers, EMA 0.997, Partial RoPE 16/64, LN Scale,
SmearGate, BigramHash(10240), Int5 MLP / Int6 attn, FP16 embeds, 3.2% pruning, zstd-22,
sliding window eval stride=64

[mohosy]

partial rope is interesting havent seen that in many submissions yet. how many dims did you find works best? 16 seems low but if it works it works

[ofirkris]

partial rope is interesting havent seen that in many submissions yet. how many dims did you find works best? 16 seems low but if it works it works

I tested 16 out of 64 dims (25%) based on ablations from other competitive runs on this challenge.

The intuition is that most heads don't need full positional information - leaving 48 dims position-free lets them learn content-based attention patterns.
I didn't sweep extensively (tried 0, 16, 32, 64) and 16 gave the best result on my setup. Would be interesting to see if anyone finds a different sweet spot at larger layer counts.

[MatoTeziTanka]

Community Review — 10L XSA + EMA + Partial RoPE + LN Scale (val_bpb: 1.1365)

BPB: 1.1365 | Compliance: LOOKS CLEAN — pure-neural submission, no TTT/SLOT/n-gram-cache

What I found in the code (head SHA 663a135c138b, file records/track_10min_16mb/2026-03-22_XSA_EMA_PartialRoPE_LNScale_TTT/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 1.88s, dim=512, layers=10, vocab=1024, code=58978 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 1.88s, dim=512, layers=10, vocab=1024, code=58978 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.