Record Submission: 1.1078 BPB — XSA6 + BigramHash4K on Hedge Mixer Stack

records/track_10min_16mb/2026-03-25_XSA6_BigramHash4K_HedgeMixer_1.1078/README.md

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+# Record Submission: XSA6 + BigramHash4K on Hedge Mixer Stack
+
+**val_bpb: 1.1078** (3-seed mean, std 0.0045)
+
+| Seed | val_bpb |
+|------|---------|
+| 42 | 1.1045 |
+| 1337 | 1.1061 |
+| 2025 | 1.1129 |
+| **Mean** | **1.1078** |
+| **Std** | **0.0045** |
+
+Built on PR #700 (@RoyiRa) with hyperparameter improvements found via systematic combinatorial search ([autoresearch-multi](https://github.com/agalimova/autoresearch-multi)).
+
+## Changes from PR #700
+
+| Parameter | PR #700 Default | Ours | Found via |
+|-----------|----------------|------|-----------|
+| `XSA_LAST_N` | 4 | **6** | autoresearch-multi EXPLOIT round |
+| `BIGRAM_VOCAB_SIZE` | 2048 | **4096** | autoresearch-multi EXPLOIT round |
+
+These two hyperparameter changes were identified by autoresearch-multi, a 4-mode adaptive search tool that tested 12+ configurations with interaction detection. The combination is superadditive: XSA=6 alone gives -0.001, BIGRAM=4096 alone gives -0.001, but together they give -0.002.
+
+## Ablation (8xH100, torch 2.9+cu126, FA3)
+
+| Variant | val_bpb (TTT) | val_bpb (no TTT) |
+|---------|---------------|------------------|
+| PR #700 baseline | 1.1225* | 1.1225* |
+| + XSA_LAST_N=6 | — | 1.1215 |
+| + BIGRAM_VOCAB_SIZE=4096 | — | 1.1217 |
+| + both (this submission) | **1.1078** | 1.1209 |
+
+*Our reproduction; PR #700 reports 1.0541 with their exact setup.
+
+## How to Reproduce
+
+```bash
+# Requirements: 8xH100, PyTorch 2.9+, FlashAttention 3
+
+# Install
+pip install torch==2.9.0 --index-url https://download.pytorch.org/whl/cu126
+pip install sentencepiece huggingface_hub zstandard
+
+# Build FA3 (Hopper only, minimal kernels ~5 min)
+cd flash-attention/hopper
+FLASH_ATTENTION_DISABLE_SM80=TRUE FLASH_ATTENTION_DISABLE_FP16=TRUE \
+FLASH_ATTENTION_DISABLE_FP8=TRUE FLASH_ATTENTION_DISABLE_SPLIT=TRUE \
+FLASH_ATTENTION_DISABLE_LOCAL=TRUE FLASH_ATTENTION_DISABLE_PAGEDKV=TRUE \
+FLASH_ATTENTION_DISABLE_APPENDKV=TRUE FLASH_ATTENTION_DISABLE_VARLEN=TRUE \
+FLASH_ATTENTION_DISABLE_PACKGQA=TRUE FLASH_ATTENTION_DISABLE_SOFTCAP=TRUE \
+FLASH_ATTENTION_DISABLE_HDIM96=TRUE FLASH_ATTENTION_DISABLE_HDIM192=TRUE \
+FLASH_ATTENTION_DISABLE_HDIM256=TRUE TORCH_CUDA_ARCH_LIST="9.0" MAX_JOBS=32 \
+python setup.py build_ext --inplace
+cp build/lib.linux-x86_64-cpython-311/flash_attn_3/_C.abi3.so flash_attn_3/
+
+# Download data
+cd parameter-golf
+python data/cached_challenge_fineweb.py --variant sp1024
+
+# Run (seed 1337)
+SEED=1337 XSA_LAST_N=6 BIGRAM_VOCAB_SIZE=4096 \
+PYTHONPATH=/path/to/flash-attention/hopper:$PYTHONPATH \
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Architecture (inherited from PR #700)
+
+- 11 layers, 512 dim, 8 heads, 4 KV heads (GQA), 33M params
+- LeakyReLU(0.5)^2 activation
+- Parallel Muon optimizer with parameter banking
+- BigramHash embeddings (**4096** buckets, 128 dim)
+- XSA on last **6** layers
+- Partial RoPE (16/64 dims), LN Scale 1/sqrt(layer+1)
+- EMA(0.997) + SWA(every 50 steps)
+- CROWN-Q quantization-aware loss during warmdown
+- GPTQ-lite int6 quantization + zstd compression
+- Legal score-first TTT (4 epochs, AdamW, cosine decay)
+- Hedge Mixer: 4-expert logistic context mixing (neural + unigram + bigram + trigram)
+
+## Search Methodology
+
+We used autoresearch-multi to systematically search over hyperparameter combinations. The search tested:
+
+- **EXPLORE** (6 experiments): Coverage across code variants and tuning options
+- **EXPLOIT** (6 experiments): Drilling into winners, found XSA=6 + BIGRAM=4096
+
+Key finding: structured 2:4 sparsity on MLP weights was tested but degraded quality significantly (1.68 BPB), confirming that post-training sparsity doesn't work at this model scale without retraining.
+
+## Training Details
+
+- 8xH100 SXM 80GB, ~99ms/step (with CROWN-Q overhead)
+- ~5800-6100 steps in 600s wallclock
+- 15.3MB artifact (int6+zstd, under 16MB limit)
+- TTT eval: ~700s (4 epochs, legal score-first)
+
+## Credits
+
+- **@RoyiRa**: PR #700 — Hedge Mixer, CROWN-Q, stride=64 optimization
+- **@abaybektursun**: PR #549 — LeakyReLU^2, Legal TTT, Parallel Muon
+- **@jfprincz**: PR #287 — Partial RoPE, XSA, LN Scale
+- **@signalrush**: PR #374 — GPTQ-lite, EMA
+- Full lineage: PR #70 → #164 → #198 → #287 → #374 → #414 → #549 → #700 → this

records/track_10min_16mb/2026-03-25_XSA6_BigramHash4K_HedgeMixer_1.1078/submission.json

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+{
+  "name": "XSA6 + BigramHash4K on Hedge Mixer Stack",
+  "author": "Aygul Galimova",
+  "github_id": "agalimova",
+  "val_bpb": 1.1078,
+  "val_bpb_std": 0.0045,
+  "seeds": {
+    "42": 1.1045,
+    "1337": 1.1061,
+    "2025": 1.1129
+  },
+  "base_pr": 700,
+  "changes": [
+    "XSA_LAST_N=6 (extended from 4 to 6 layers)",
+    "BIGRAM_VOCAB_SIZE=4096 (doubled from 2048)"
+  ],
+  "env": {
+    "torch": "2.9.0+cu126",
+    "gpu": "8xH100 SXM 80GB",
+    "flash_attn": "FA3 (Hopper, minimal build)"
+  }
+}