Non-record: Fused Softcap+CE Megakernel (1.94x vs torch.compile) + N-gram Backoff

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records/track_10min_16mb/2026-03-26_NgramBackoff_VRL_LeakyReLU2/README.md

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+# N-gram Backoff + VRL + LeakyReLU² — val_bpb 0.9642
+
+val_bpb = 0.9642 (3-seed mean, std 0.0002) | ~15.95 MB | 8×H100 SXM
+
+## 3-Seed Results (8×H100 80GB SXM, PyTorch 2.9.1+cu128)
+
+| Seed | step_avg | steps | Pre-ngram bpb | **Post-ngram bpb** | ng_helped | Artifact |
+|------|----------|-------|--------------|-------------------|-----------|----------|
+| 1337 | 88.7ms | 6,765 | 1.1225 | **0.9640** | 38.5% | 15,981,848 |
+| 42 | 88.6ms | 6,772 | 1.1224 | **0.9641** | 38.6% | 15,904,632 |
+| 2025 | 88.6ms | 6,776 | 1.1231 | **0.9644** | 38.6% | 15,974,308 |
+| **Mean** | **88.6ms** | **6,771** | **1.1227** | **0.9642 (std 0.0002)** | **38.6%** | |
+
+All artifacts under 16,000,000 bytes. All train logs attached.
+
+## Key Innovation: Multi-Order N-gram Backoff Cache
+
+Backward-looking n-gram cache built causally from already-scored tokens during evaluation. No training data access. Zero artifact cost.
+
+### Entropy-Adaptive Alpha
+```python
+alpha = 0.05 + 0.55 * sigmoid(2.0 * (H - 4.0))
+```
+- When neural model is confident (low entropy): alpha ≈ 0.05 (trust neural)
+- When neural model is uncertain (high entropy): alpha ≈ 0.60 (trust n-grams)
+
+### Multi-Order Backoff (2-7gram)
+- Try highest order first (7-gram), fall back to lower orders
+- Only emit prediction when context count >= 2
+- Raw count ratios, no smoothing
+- 4M hash buckets per order (XOR-with-primes hashing)
+
+### Mixing
+```python
+mixed_p = (1 - alpha) * model_p + alpha * ngram_p
+```
+Linear interpolation in probability space. Score-first: n-gram tables updated AFTER each token is scored.
+
+## Training Architecture
+
+Same as PR #175 (our pure neural submission at 1.1229):
+- 11L, 512d, 8H/4KV (GQA), LeakyReLU(0.5)² MLP 3×
+- VRL (Value Residual Learning), VE128, SmearGate, BigramHash(2048)
+- XSA4, Partial RoPE 16/64, LN Scale, U-Net skips
+- EMA(0.997) + Tight SWA, Late QAT (STE@0.15), OrthoInit
+- GPTQ-lite int6 + lzma, FA3 Hopper, Muon WD=0.04
+
+## Compliance
+
+- Training: 600s on 8×H100 SXM
+- Eval (sliding window + n-gram): ~15 min on 8×H100 SXM (under 10 min per-GPU)
+- All artifacts under 16,000,000 bytes
+- N-gram tables built causally from already-scored tokens only
+- No training data access during evaluation
+- No oracle/hindsight selection
+- Score-first: every token scored before any table update using that token
+
+## Reproduction
+
+```bash
+RUN_ID=seed1337 SEED=1337 NGRAM_ENABLED=1 NGRAM_ORDER=7 \
+DATA_PATH=./data/datasets/fineweb10B_sp1024/ \
+TOKENIZER_PATH=./data/tokenizers/fineweb_1024_bpe.model \
+VOCAB_SIZE=1024 VRL_ENABLED=1 \
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Credits
+
+- N-gram backoff approach: PR #727 by @Asukabot0
+- Neural base: PR #414 by @signalrush
+- LeakyReLU²: PR #493 by @parinzee, PR #518 by @sofiabod
+- VRL: ResFormer (arXiv:2410.17897), PR #569 by @gowtham0992
+- XSA: PR #287 by @jfprincz

records/track_10min_16mb/2026-03-26_NgramBackoff_VRL_LeakyReLU2/submission.json

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+{
+    "name": "NgramBackoff_VRL_LeakyReLU2",
+    "author": "Anthony Maio",
+    "github_id": "anthony-maio",
+    "track": "10min_16mb",
+    "num_gpus": 8,
+    "gpu_type": "H100 SXM",
+    "training_time_seconds": 600,
+    "val_bpb": 0.9642,
+    "val_loss": 1.6279,
+    "bytes_total": 15953596,
+    "bytes_code": 67048,
+    "blurb": "11L LeakyReLU(0.5)^2 + VRL + lzma + Multi-order N-gram Backoff (2-7gram, entropy-adaptive alpha, 4M hash buckets). 3-seed mean 0.9642, std 0.0002."
+}