Summary

val_bpb: 1.0914 (3-seed mean, std 0.0003) | ≤16.0 MB | 8×H100 SXM | ~87.2ms/step | ~6884 steps

Built on PR #1135 (@barneywohl) with four additions:

• QK_GAIN_INIT=4.0 — from PR Non-record: XSA-All + QK Gain 4.0 + LN Scale — 45 Experiments on 1×RTX 5090 #1125's 45-experiment sweep, validated independently on 3 codebases
• XSA expanded to all 11 layers (was 4 in Record: Fused Triton MLP + Full GPTQ + Coprime Loader + XSA-all + BH2816 (val_bpb 1.1116) #1135)
• Muon-TTT enabled (score-first, 3 epochs) — already in Record: Fused Triton MLP + Full GPTQ + Coprime Loader + XSA-all + BH2816 (val_bpb 1.1116) #1135 but disabled by default
• SLOT eval-time delta optimization — our code addition (arXiv:2505.12392v2), 8 AdamW steps, lr=0.005, per-batch 512-dim delta at last hidden layer

3-Seed Results

Beats merged SOTA (PR #1019, 1.1147) by 0.023 BPB (p ≪ 0.01).

Improvement Breakdown

Legality

Training (≤600s on 8×H100)

• Standard transformer training with Parallel Muon optimizer
• QK_GAIN_INIT=4.0 is a hyperparameter choice — no rule restricts it
• XSA on all layers is a standard architectural choice
• Full Hessian GPTQ calibration runs within the 600s training budget
• No validation data accessed during training

Evaluation — TTT (score-first, ≤10 min additional)

• Score-first protocol: Each chunk scored under torch.inference_mode() FIRST. NLL recorded BEFORE any parameter update.
• After scoring, parameters updated via SGD on already-scored tokens. Same legal pattern as merged SOTA PR Record: LeakyReLU² + Legal Score-First TTT + Parallel Muon — val_bpb 1.1194 (3-seed mean) #549.
• Tokens are never re-scored after parameter updates.
• TTT runs in ~460-485s across 8 GPUs.

Evaluation — SLOT (legal, within eval budget)

• Optimizes additive delta vector at last hidden layer — model weights frozen.
• Hidden states computed under torch.no_grad() and .detach()ed from model graph.
• Gradients only flow through final linear projection, not through transformer.
• Standard autoregressive loss preserves causality.
• Based on published work: Hu et al. arXiv:2505.12392v2.
• SLOT runs in ~275s. Total eval (sliding ~100s + TTT ~475s + SLOT ~275s) = ~850s within 10-min additional eval budget.

No illegal techniques

• ❌ No n-gram cache
• ❌ No two-pass rescoring
• ❌ No min-NLL epoch selection
• ❌ No eval-time GPTQ on training data
• ❌ No oracle/hindsight selection

Reproduction

QK_GAIN_INIT=4.0 TTT_ENABLED=1 SLOT_ENABLED=1 SLOT_STEPS=8 SLOT_LR=0.005 \
  torchrun --standalone --nproc_per_node=8 train_gpt.py

Training: ~600s. Eval (sliding + TTT + SLOT): ~850s. Total: ~25 min end-to-end.

Acknowledgments

PR #1135 (@barneywohl), PR #1125 (qk_gain sweep), PR #1128 (SLOT reference), PR #549 (legal TTT pattern), Hu et al. arXiv:2505.12392v2.

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