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
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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.
This was referenced Apr 3, 2026
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…b 1.0900 (3-seed mean) 3-layer depth recurrence (layers 3,4,5) with WD-LR synergy: higher WD (0.095) compresses for all-int6 headroom, higher MLR (0.022) recovers quality. All 66 layers at int6 precision. 3-seed mean: 1.0900 BPP / 2.5077 nats (seeds 42, 0, 7) All seeds under 16MB with 36K+ margins. No TTT, no SLOT, no eval-time adaptation. Improves PR openai#1285 (1.0912) by 0.0013 BPP. Beats PR openai#1218 by 0.0079. Built on PR openai#1218 by @clarkkev.
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Hypothesis: Polar Express 4-step minimax NS on top of full PR openai#1334 stack Expected delta: ~-0.001 to -0.002 BPB from 1.0897 baseline Key changes vs PR openai#1334: - Polar Express Newton-Schulz (4-step minimax coefficients, arXiv:2505.16932) - MATRIX_LR=0.022 (validated for WD=0.090) - MUON_WD=0.090 (PR openai#1285/1334 optimal for 2-layer recurrence) - NoPE explicitly disabled (nope_every_n=0) after critique - Trackio experiment tracking added Stack: SP4096 vocab + MLP 4x + WD=0.090 + MuonEq-R + QK-Gain 5.0 + Depth recurrence L4-5 (step 3000) + Parallel residuals L7+ + Brotli
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…el spectral test-time training Base: clarkkev openai#1218 (1.0974 BPB, 4096 vocab, brotli, 34M params) Added: depth recurrence L4,5 (from openai#1285), MuonEq-R, WD=0.09 Novel: Spectral TTT — adapt singular values at eval time (8192 params) Target: ~1.085 BPB
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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