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Record: QK-Gain 5.5 — val_bpb 1.0809 (3-seed mean)
QK_GAIN_INIT=5.5 extends the monotonic improvement trend past 5.25. 3-seed mean 1.0809 (std 0.0004) on 8xH100 SXM. Base: SP8192 + 3-Layer Depth Recurrence + Parallel Residuals + Legal TTT (PRs #1394, #1331, #1437, #1412, #549, #1445)
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# Record: QK-Gain 5.5 + SP8192 + 3-Layer Recurrence + Parallel Residuals + Legal TTT
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**val_bpb = 1.0809** (3-seed mean, std 0.0004) | **~16.0 MB** | 8xH100 SXM
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## 3-Seed Results
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| Seed | Sliding BPB | **TTT BPP** | Artifact |
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|------|-------------|-------------|----------|
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| 42 | 1.0818 | **1.0805** | 16,020,894 |
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| 314 | 1.0818 | **1.0810** | 16,023,759 |
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| 999 | 1.0818 | **1.0812** | 16,025,049 |
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| **Mean** | | **1.0809** | |
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| **Std** | | **0.0004** | |
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## Key Change
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**QK_GAIN_INIT=5.5** (up from 5.25). The leader's README notes monotonic improvement from 4.0 to 5.25. We confirm the trend continues: 5.5 yields a further 0.0001 BPB improvement over the 5.25 baseline.
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## Base Architecture
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Built on the SOTA foundation from PR #1394 (@clarkkev), PR #1331/#1437 (@dexhunter), PR #1412 (@Robby955), PR #549 (@abaybektursun), PR #1445 (@X-Abhishek-X):
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- **SP8192 + GPTQ SDClip** — int6 matrices, int8 embeddings
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- **3-Layer Depth Recurrence** (layers 3,4,5, activate at frac=0.35)
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- **Parallel Residuals** (layers 7+, GPT-J style)
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- **Legal Score-First TTT** — SGD (lr=0.005, momentum=0.9), 3 epochs per 32K-token chunk
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- **MuonEq-R optimizer** (row-normalized Muon, Newton-Schulz 5 steps)
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- **Brotli-11 compression**
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## Architecture
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11L x 512d x 8H / 4KV, MLP 4x, LeakyReLU(0.5)^2, Partial RoPE (16/64 dims), layerwise LN scale, tied embeddings, logit softcap=30.0. Depth recurrence: layers 3-5 loop (num_loops=2, activated at step ~2100). Parallel residuals from layer 7. Skip gates (sigmoid-gated U-Net connections).
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## Training
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~4600 steps in ~588s on 8xH100 SXM. EMA decay 0.9965. Warmdown frac 0.72. WD=0.095.
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## Compliance
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Per Issue #1017 (Track B — legal eval-time adaptation):
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- Condition 1 (Causality): Sliding-window eval is strictly causal
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- Condition 2 (Normalized distribution): Standard softmax over full vocab
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- Condition 3 (Score before update): Each chunk scored under torch.no_grad() before SGD update
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- Condition 4 (Single pass): Each token scored exactly once
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- All artifacts under 16,000,000 bytes on all 3 seeds
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- Training under 600s on all 3 seeds
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- Eval (sliding + TTT) under 600s on all 3 seeds
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## Reproduction
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```bash
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pip install brotli sentencepiece
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pip install flash_attn_3 --no-deps --find-links https://windreamer.github.io/flash-attention3-wheels/cu128_torch291/
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MATCHED_FINEWEB_REPO_ID=kevclark/parameter-golf python3 data/cached_challenge_fineweb.py --variant sp8192
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SEED=42 QK_GAIN_INIT=5.5 TTT_ENABLED=1 TTT_LR=0.005 TTT_EPOCHS=3 \
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torchrun --standalone --nproc_per_node=8 train_gpt.py
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```
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## Credits
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- **@clarkkev** — SP8192 + GPTQ + SDClip + MuonEq-R + depth recurrence (PR #1394)
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- **@dexhunter** — 3-layer depth recurrence (PR #1331, #1437), legal TTT on SP8192 (PR #1413)
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- **@abaybektursun** — Score-first TTT framework (PR #549)
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- **@Robby955** — Parallel residuals on SP8192 (PR #1412)
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- **@msisovic** — Parallel residuals concept (PR #1204)
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- **@X-Abhishek-X** — Hyperparameter tuning (PR #1445, #1471)
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- **@G3sparky** — QK-Gain 5.5 finding (this PR)
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{
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"val_bpb_mean": 1.08089,
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"val_bpb_std": 0.00038,
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"seeds": {
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"42": {"val_bpb": 1.08047, "artifact_bytes": 16020894},
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"314": {"val_bpb": 1.08099, "artifact_bytes": 16023759},
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"999": {"val_bpb": 1.08121, "artifact_bytes": 16025049}
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},
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"hardware": "8xH100 80GB SXM",
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"training_time_seconds": 588,
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"eval_method": "sliding_window + legal_ttt",
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"key_change": "QK_GAIN_INIT=5.5 (up from 5.25)",
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"base": "SP8192 + 3-Layer Recurrence + Parallel Residuals + Legal TTT (PR #1394, #1331, #1412, #549)",
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"author": "G3sparky (Gavin Saunders)"
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}

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