Record: SLOT + Split-LR + Full GPTQ + XSA-all — val_bpb 1.1015 (3-seed mean)

SLOT eval-time delta optimization + split early/late Muon LR +
Full Hessian GPTQ int6 + sigmoid-gated skip connections +
soft-round QAT + Brotli-11 + BigramHash(2816x160) + code minification.

3-seed mean: 1.1015 (std 0.0011), delta -0.0132 BPP / -0.0224 nats vs PR openai#1019.

Author: dexhunter

records/track_10min_16mb/2026-03-31_SLOT_SplitLR_GPTQ_XSA_1.1015/README.md

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+# Record: SLOT + Split-LR + Full GPTQ + XSA-all (val_bpb: 1.1015)
+
+**val_bpb: 1.1015** (3-seed mean, std 0.0011) | **1.8598 nats** | **~15.65 MB** | 8xH100 SXM, 600s train + 177s eval
+
+Built on [PR #1019](https://github.com/openai/parameter-golf/pull/1019) by @abaybektursun.
+Previous: [PR #549](https://github.com/openai/parameter-golf/pull/549) (1.1194) -> [PR #1019](https://github.com/openai/parameter-golf/pull/1019) (1.1147) -> this.
+
+## Results (8xH100 SXM)
+
+| Seed | Steps | ms/step | Post-EMA BPB | **Sliding+SLOT BPB** | val_loss (nats) | Artifact |
+|------|-------|---------|-------------|---------------------|-----------------|----------|
+| 1337 | 6704 | 88.2 | 1.1309 | **1.10213** | 1.8609 | 15,647,124 |
+| 42 | 6706 | 88.2 | 1.1289 | **1.10019** | 1.8576 | 15,658,061 |
+| 2025 | 6684 | 88.4 | 1.1310 | **1.10216** | 1.8609 | 15,650,266 |
+| **Mean** | **6698** | **88.3** | **1.1303** | **1.10149** | **1.8598** | **15,651,817** |
+
+### Improvement vs SOTA
+
+| Metric | Merged SOTA (PR #1019) | This submission | Delta |
+|--------|----------------------|-----------------|-------|
+| val_bpb (3-seed mean) | 1.1147 | **1.1015** | **-0.0132** |
+| val_loss (nats) | 1.88218 | **1.85982** | **-0.02236** |
+
+Clears the 0.005 nats threshold by 4.5x.
+
+## Changes vs Baseline (PR #1019)
+
+### 1. SLOT: Sample-specific LM Optimization at Test-time
+
+At eval time, for each sliding-window batch, we optimize a single additive delta vector (R^512) between the frozen hidden states and the logit projection. The model forward is split into `forward_hidden()` (frozen, no grad) and `compute_logits()` (carries grad for delta optimization).
+
+- **Delta shape**: `[1, 1, 512]` — broadcasts across batch and sequence
+- **Optimizer**: AdamW (lr=0.005, weight_decay=1e-8, eps=1e-5)
+- **Steps**: 8 per batch
+- **Eval time overhead**: ~90s (well within 600s eval budget)
+
+SLOT is score-first: hidden states are computed under `torch.no_grad()`, the delta adapts through `compute_logits()` only, and final scoring uses the adapted logits. The model weights are never modified.
+
+Reference: Hu et al., arXiv:2505.12392v2. Also used in PR #1128, PR #1105.
+
+### 2. Sigmoid-Gated Skip Connections
+
+U-Net skip connections use learned sigmoid gates instead of simple addition:
+```python
+g = sigmoid(skip_gates[i])
+x = lerp(skip_weights[i] * skip, x, g)
+```
+Gate starts at sigmoid(0) = 0.5 (balanced blend). Adds 2,560 params (5 gates x 512 dims).
+
+### 3. Soft-Round QAT with Alpha Ramp
+
+Late QAT uses differentiable sigmoid rounding instead of hard STE:
+```python
+soft_rounded = floor(scaled) + sigmoid(alpha * (frac - 0.5))
+```
+Alpha ramps from 1 (smooth) to 16 (near-hard) over 500 steps. Provides real gradients through rounding, letting weights adapt to quantization grid.
+
+### 4. Split Early/Late Muon Learning Rate
+
+Bank gradients are scaled per-layer before the Muon reduce-scatter:
+- Early layers (0-4): Muon LR = 0.025
+- Late layers (5-10): Muon LR = 0.030
+
+Late layers benefit from higher LR (weaker gradient signal further from loss).
+
+### 5. Warmdown = 4000 Steps
+
+Extended warmdown from 3500 to 4000 estimated steps. Holds LR higher for longer, giving the model more time at productive learning rates.
+
+### 6. BigramHash(2816x160)
+
+Enlarged bigram embedding dimension from 112 to 160. Same 2816 buckets. Richer per-bucket representation at minimal artifact cost.
+
+### 7. Code Minification
+
+`pyminify` + LZMA2 + base85 self-extracting wrapper reduces code from 101KB to 23KB, freeing ~78KB of artifact budget for model weights.
+
+### 8. Brotli-11 Compression with Byte-Shuffle
+
+Replaces LZMA-6 with Brotli quality=11 + stride-2 byte-shuffle preprocessing. Saves ~400KB vs LZMA.
+
+### 9. GPTQ Reserve 9s (was 14s)
+
+Reduced GPTQ calibration time reservation from 14s to 9s, gaining ~55 extra training steps.
+
+## Negative Results (tested, did not help)
+
+| Technique | Result | Notes |
+|-----------|--------|-------|
+| Turbo-Muon (AOL + Polar Express) | +2MB artifact bloat | Weight distribution changes break compression |
+| No-GPTQ (PR #1120 style) | -0.005 BPP worse | GPTQ essential for our stack |
+| Pure EngramLite swap | -0.003 worse | Same-budget multi-head too diluted |
+| ResidLambdas | -0.003 worse | Quant error compounds through lambda scaling |
+| LeakyReLU slope=0.3 | Neutral | |
+| Partial key offset | Neutral | |
+| BIGRAM_DIM=192 | -0.001 worse | Diminishing returns past 160 |
+| TTT (score-first SGD) | Neutral on Full GPTQ stack | Post-quant weights too well-optimized |
+| Mixed int5/int6 GPTQ | Broken or worse | Needs full PR #1089-style pipeline |
+
+## Architecture Summary
+
+| Component | Setting | Source |
+|-----------|---------|--------|
+| Layers | 11 | PR #549 |
+| Model dim | 512 | PR #549 |
+| Heads / KV heads | 8 / 4 (GQA) | PR #549 |
+| MLP mult | 3.0x (LeakyReLU(0.5)^2) | PR #549 |
+| XSA | All 11 layers | PR #1019 |
+| BigramHash | 2816 x 160 | **This submission** (dim=160) |
+| ValueEmbedding | dim=128, layers 9,10 | PR #549 |
+| SmearGate | F.pad causal shift | PR #549, optimized |
+| Skip connections | Sigmoid-gated lerp | **This submission** |
+| Quantization | Full Hessian GPTQ int6 | PR #1019 |
+| Compression | Brotli-11 + byte-shuffle | **This submission** |
+| Optimizer | Parallel Muon + Split-LR | **This submission** (split-LR) |
+| QAT | Soft-round alpha ramp 1->16 | **This submission** |
+| Eval | Sliding window stride=64 + SLOT | **This submission** (SLOT) |
+| Code | LZMA2 self-extracting wrapper | **This submission** |
+| Warmdown | 4000 steps | **This submission** |
+| Params | 27.2M | |
+
+## Setup & Reproduction
+
+```bash
+# Environment: 8xH100 SXM, PyTorch 2.9.1+cu128, flash-attn 2.8.3
+export NCCL_NET=Socket  # Required on GCP H100
+export SLOT_ENABLED=1
+export BIGRAM_DIM=160
+export WARMDOWN_ITERS=4000
+export SLOT_LR=0.005
+export SLOT_STEPS=8
+
+# Run with torchrun (evaluate.py handles this)
+SEED=1337 torchrun --standalone --nproc_per_node=8 train_gpt.py
+SEED=42 torchrun --standalone --nproc_per_node=8 train_gpt.py
+SEED=2025 torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Acknowledgements
+
+Thanks to **@0hq** and **@valerio-oai** for organizing and maintaining an excellent competition.
+
+This submission builds directly on @abaybektursun's PR #549 and PR #1019, which established the LeakyReLU^2 + Parallel Muon + XSA + Full GPTQ stack. The SLOT technique follows Hu et al. (arXiv:2505.12392v2) and was independently validated by @AnubhavBharadwaaj (PR #1128) and @abaybektursun (PR #1105). The sigmoid-gated skip connection idea draws from @mikeapedia's PR #1089. Code minification approach adapted from PR #1089's shrink pipeline.

records/track_10min_16mb/2026-03-31_SLOT_SplitLR_GPTQ_XSA_1.1015/submission.json

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+{
+  "name": "SLOT + Split-LR + Full GPTQ + Sigmoid-Gated Skips + Soft-Round QAT + XSA-all",
+  "val_bpb": 1.1015,
+  "bytes_total": 15658061,
+  "blurb": "SLOT eval-time delta optimization (lr=0.005, 8 AdamW steps per batch) + split early/late Muon LR (0.025/0.030) + Full Hessian GPTQ int6 + sigmoid-gated U-Net skip connections + soft-round QAT with alpha ramp + Brotli-11 byte-shuffle compression + BigramHash(2816x160) + code minification (23KB wrapper). 3-seed mean: 1.1015 (std 0.0011). Built on PR #1019 by @abaybektursun.",
+  "author": "dexhunter",
+  "github_id": "dexhunter",
+  "date": "2026-03-31"
+}