Record: MuonEq-R + Depth Recurrence + WD=0.090 + All-Int6 GPTQ — val_bpb 1.0912 (3-seed mean)#1285
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Record: MuonEq-R + Depth Recurrence + WD=0.090 + All-Int6 GPTQ — val_bpb 1.0912 (3-seed mean)#1285dexhunter wants to merge 1 commit intoopenai:mainfrom
dexhunter wants to merge 1 commit intoopenai:mainfrom
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….0912 (3-seed mean) WD-quantization synergy: higher weight decay (0.090 vs 0.085) compresses 5% better, creating headroom for ALL 66 layers at int6 precision. The extra quantization quality more than recovers the WD BPP cost. 3-seed mean: 1.0912 BPB / 2.5106 nats (seeds 42, 0, 1337) All seeds under 16MB with 32K+ margins. No TTT, no SLOT, no eval-time adaptation. Built on PR openai#1218 by @clarkkev. Improves PR openai#1260 (1.0929) by 0.0017 BPP.
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Summary
Key Innovation: WD-Quantization Synergy
Higher WD (0.090 vs 0.085) → smaller weights → 5% better brotli compression → enough headroom for ALL 66 layers at int6 precision. The quantization quality gain exceeds the WD BPP cost:
Results (8xH100 80GB SXM, PyTorch 2.9.1+cu128)
Changes from PR #1218
Credits
Test plan