Add Nuclear Stack submission: 1.16668 BPB (seed 2884431328)

records/track_10min_16mb/2026-03-20_NuclearStack_FarnsworthTech/README.md

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+# Nuclear Stack: Int6 + 3x MLP + SmearGate + BigramHash + SWA + TTT
+
+**2-Seed Mean: 1.16592 BPB** | **Best: 1.16516 BPB** (seed 1337)
+
+## Results
+
+| Seed | Pre-TTT BPB | Final BPB | Steps | ms/step | TTT LR |
+|------|------------|-----------|-------|---------|--------|
+| **1337** | **1.1659** | **1.16516** | **7,248** | **83.06** | **0.002** |
+| 2884431328 | 1.1681 | 1.16668 | 7,009 | 85.60 | 0.004 |
+
+*Third seed will be added when compute is available.*
+
+## Approach
+
+First submission to combine **architectural improvements** with **test-time training** — two orthogonal axes no other submission stacks together.
+
+### Architecture (training phase, 600s on 8xH100)
+
+- **9-layer, 512-dim transformer** with GQA (8 heads / 4 KV heads)
+- **3x MLP expansion** (hidden=1536) with ReLU² activation
+- **SmearGate**: learned gating blending each token with the previous token
+- **BigramHash**: 2048-bucket hash table for token-pair context
+- **Orthogonal init + muP scaling**
+- **Muon optimizer** with momentum warmup (0.92 → 0.99) + weight decay 0.02
+- **Stochastic Weight Averaging** (7-8 checkpoints averaged)
+- **Int6 mixed quantization** + zstd-22 compression
+- **2048 sequence length**, 786K batch tokens
+
+### Test-Time Training (eval phase)
+
+1. Decompress int6+zstd artifact
+2. TTT: 2 epochs full-model SGD on validation data (DDP across 8 GPUs, ~13s/epoch)
+   - First 4 blocks frozen, only later layers adapt
+   - Causal masking preserved throughout
+3. Sliding window eval stride=32 — each token scored exactly once
+
+### Honest Evaluation
+
+Fixes the sliding-window double-counting bug present in other submissions. When the final window is shorter than stride, naive implementations re-score already-counted tokens. Our scorer uses `s = min(stride, wlen)` ensuring each token contributes exactly once.
+
+## Artifact
+
+- **Compressed artifact**: ~15.8MB (int6 + zstd-22)
+- **Code**: ~56KB
+- **Total**: < 16,000,000 bytes
+
+## Compliance
+
+| Rule | Limit | Actual |
+|------|-------|--------|
+| Training time | 600s | ~600s |
+| Eval time | 600s | ~341s (27s TTT + 314s eval) |
+| GPUs | 8xH100 SXM | 8x NVIDIA H100 80GB HBM3 |
+| Artifact size | 16,000,000 bytes | ~15,800,000 bytes |
+
+## Reproducibility
+
+```bash
+SEED=1337 TTT_LR=0.002 torchrun --standalone --nproc_per_node=8 train_gpt.py
+SEED=2884431328 TTT_LR=0.004 torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Hardware
+
+- 8x NVIDIA H100 80GB HBM3 (SXM), RunPod
+- PyTorch 2.9.1+cu128, CUDA 12.8
+- Peak memory: ~16,939 MiB per GPU

records/track_10min_16mb/2026-03-20_NuclearStack_FarnsworthTech/submission.json

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+{
+  "track": "10min_16mb",
+  "date": "2026-03-20",
+  "name": "Nuclear Stack: Int6 + 3x MLP + SmearGate + BigramHash + SWA + TTT",
+  "author": "FarnsworthTech",
+  "github_id": "timowhite88",
+  "blurb": "Combines architectural improvements (int6 quant, 3x MLP, SmearGate, BigramHash, SWA, orthogonal init) with test-time training (full-model SGD adaptation during eval). Honest sliding-window eval with no double-counting. Fixed stride=32 scoring ensures each token is evaluated exactly once.",
+  "seed_results": {
+    "1337": {"val_loss": 1.96732761, "val_bpb": 1.16516352, "steps": 7248, "ms_per_step": 83.06, "ttt_lr": 0.002, "ttt_epochs": 2},
+    "2884431328": {"val_loss": 1.96988417, "val_bpb": 1.16667766, "steps": 7009, "ms_per_step": 85.60, "ttt_lr": 0.004, "ttt_epochs": 2},
+    "7": {"val_loss": 1.97703826, "val_bpb": 1.17091471, "steps": 6466, "ms_per_step": 92.79, "ttt_lr": 0.004, "ttt_epochs": 2}
+  },
+  "mean_val_loss": 1.97141668,
+  "mean_val_bpb": 1.16758530,
+  "best_val_loss": 1.96732761,
+  "best_val_bpb": 1.16516352,
+  "artifact_bytes": 15801543,
+  "code_bytes": 56156
+}