Turbo-Muon + EngramLite + ParamBanking + GPTQ Reserve Opt — val_bpb 1.1126 (3-seed mean)

[Bortlesboat]

Summary

• val_bpb: 1.1126 (3-seed mean, std 0.0003)
• Artifact: ~15.98 MB (all seeds under 16,000,000 bytes)
• Eval time: ~120s (no TTT, sliding window stride=64)
• Built on PR #1089 by @mikeapedia

3-Seed Results

Seed	Sliding BPB	val_loss (nats)	Artifact
1337	1.1126	1.87857	15,981,856
42	1.1123	1.87803	15,984,349
999	1.1129	1.87900	15,985,912
Mean	1.1126	1.87853

vs merged SOTA (PR #549, 1.89002 nats): -0.01149 nats. Note: open PRs #1089 (1.1091) and #1105 (1.1138) achieve better scores.

What's New vs PR #1089

1. GPTQ Reserve Optimization: Reduced calibration reserve from 14s to 9s (actual calibration ~8.4s), recovering ~55 extra training steps
2. Experimental fused Triton MLP kernel: Forward-only fusion via torch.library.triton_op with standard PyTorch backward. Hard-disabled in this submission (produces NaN on PT2.9 due to TTIR analysis bug). Included as experimental code for future work.

Compliance

• Standard F.cross_entropy scoring
• No TTT, no eval-time training data access
• Artifact < 16,000,000 bytes (all 3 seeds)
• Training < 600s, eval < 600s
• Causal sliding-window evaluation (stride=64)
• 3-seed verification: -0.01149 nats vs merged SOTA (> 0.005 threshold)

Credits

• PR #1089 by @mikeapedia (Turbo-Muon, EngramLite, ParamBanking)
• PR #1072 by @vimeto (fused Triton kernel design)
• PR #1105 by @abaybektursun (forward-only fusion insight)
• PR #549 by @abaybektursun (base scaffold)