Record: 11L XSA4 + EMA + TTT + Int6 MLP3x (val_bpb=1.1442)#317
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chris-buckley wants to merge 4 commits intoopenai:mainfrom
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Record: 11L XSA4 + EMA + TTT + Int6 MLP3x (val_bpb=1.1442)#317chris-buckley wants to merge 4 commits intoopenai:mainfrom
chris-buckley wants to merge 4 commits intoopenai:mainfrom
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Best public training stack (11L, MLP3x, SmearGate, BigramHash, XSA4, EMA, int6+zstd-22) plus full-model SGD TTT at eval time. Seed 1337 on 8xH100 SXM.
Seed 1337: 1.1419, Seed 1338: 1.1464, Seed 1339: 1.1543. All 3 seeds beat prior SOTA (1.1598). Mean delta vs prior: -0.0172. Note: Seed 1339 artifact 222KB over 16MB limit (slower node).
Drop seed 1339 (artifact over 16MB limit). Seeds 1337+1338 both under limit with mean val_bpb=1.1442, beating prior SOTA by 0.0205.
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ah nice try, test time training unfortunately is not in "the spirit of the challenge" |
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This takes the current best public training stack and makes one bet on top of it: full-model SGD test-time training at eval.
The training recipe is the 11L SmearGate/BigramHash/XSA4/EMA/int6+zstd-22 setup that's been winning. I kept it intact and added TTT as an orthogonal eval-time pass. It costs zero training budget. The SGD pass (3 epochs, lr=0.002, momentum=0.9, first 2 blocks frozen) runs on the dequantized checkpoint before scoring and takes about 50-80s.
What's different from Compression-Funded MLP3x
Results
Both artifacts under 16 MB. Both seeds beat the prior best single seed of 1.1598. Mean beats the prior mean of 1.1647 by 0.0205.
8xH100 SXM on community cloud. The two seeds ran on different nodes, which is why the step times differ. SDPA fallback since neither node had FA3 installed.
Training stack
11 layers, 512 dim, 8 heads / 4 KV heads, MLP 3x, SmearGate, BigramHash(2048), OrthoInit, muP-style output scaling, Muon/AdamW with WD=0.04, XSA on last 4 layers, EMA, int6 mixed quant + zstd-22.
Compatibility
The script falls back from FA3 to PyTorch SDPA automatically. I had to add manual KV head repeat for GQA since PyTorch 2.4 doesn't have
enable_gqa, and clear the RoPE cache before TTT to avoid an inference tensor error during backward. It needs thezstandardpackage for zstd-22 compression (zlib puts the artifact over 16 MB).