Record: MuonEq-R + Depth Recurrence + Mixed Int5/Int6 GPTQ — val_bpb 1.0929 (3-seed mean)#1260
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dexhunter wants to merge 1 commit intoopenai:mainfrom
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Record: MuonEq-R + Depth Recurrence + Mixed Int5/Int6 GPTQ — val_bpb 1.0929 (3-seed mean)#1260dexhunter wants to merge 1 commit intoopenai:mainfrom
dexhunter wants to merge 1 commit intoopenai:mainfrom
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…1.0929 (3-seed mean) Adds three techniques to PR openai#1218's 4096-vocab high-WD stack: - MuonEq-R optimizer (row-norm before NS5 orthogonalization) - Depth recurrence on layers 4,5 (shared MLP, zero extra params) - Mixed int5/int6 GPTQ via Hessian sensitivity ranking 3-seed mean: 1.0929 BPB / 2.5145 nats All seeds under 16MB (max: 15,981,324 bytes) No TTT, no SLOT, no eval-time adaptation.
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Great submission @dexhunter! Did you happen to test muon column norm or row+column norm? I found R+C worked the best with the smaller vocab and I am wondering if that holds here as well. |
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Summary
Key Innovations
Results (8xH100 80GB SXM, PyTorch 2.9.1+cu128)
Changes from PR #1218
Credits
Test plan