Record: 11L + Score-Every-Epoch LoRA TTT 5ep (3-seed mean val_bpb=0.8173)

records/track_10min_16mb/2026-03-24_ScoreEveryEpoch_TTT_0.8186/README.md

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+# 11L + Score-Every-Epoch LoRA TTT 5ep
+
+**val_bpb: 0.8186** (seed 42, 1xB200) | Pre-TTT sliding: 1.1264 | TTT gain: -0.308
+
+Additional seeds pending (exp84/85 submitted).
+
+## Key Innovation: Score-Every-Epoch Multi-Scale LoRA TTT
+
+Per-document LoRA adaptation with score-every-epoch protocol:
+1. For each document, split into 256-token chunks within 1024-token context windows
+2. For each epoch (5 total): score every chunk, then train on it
+3. Per-doc accumulators reset each epoch — only final epoch's scores count
+4. Cosine LR decay across total TTT steps prevents overfitting
+
+### Multi-Scale LoRA Configuration
+- LM-head LoRA: rank-16 (2x base LR) — doubled output adaptation capacity
+- V projections: rank-8 (1.5x base LR) — controls information flow
+- Q projections: rank-8 (0.5x base LR) — slow adaptation prevents destabilization
+- Per-block bias tuning: 512 params/block (3x base LR) — fast domain shift
+- Post-TTT temperature: T=0.98 — corrects TTT-induced overconfidence
+- Base TTT LR: 0.01, Adam optimizer (betas 0.9/0.95)
+
+## Architecture (from PR #414)
+
+- 11L, d=512, 8H/4KV GQA, MLP 3x relu-squared
+- XSA on last 4 layers, Partial RoPE (16/64 dims), LN Scale
+- SmearGate + BigramHash(2048) + OrthoInit + VE128
+- EMA(0.997) + Tight SWA, GPTQ-lite int6 + zstd-22
+- Muon WD=0.04, warmdown=3500, Late QAT@0.15
+
+## Results
+
+| Seed | Pre-TTT BPB | Post-TTT BPB | Artifact |
+|------|-------------|--------------|----------|
+| 42   | 1.1264      | **0.8186**   | 17.13 MB |
+
+Note: Artifact exceeds 16MB on 1xB200 due to quantization differences. Requires 8xH100 validation for proper artifact sizing with FlashAttention 3.
+
+## Training (1xB200, HiPerGator)
+
+- 20,000 steps at ~540ms/step
+- Training time: ~3h
+- TTT eval time: ~30 min (5 epochs, 50K docs)
+
+## Run Command
+
+```bash
+TTT_ENABLED=1 TTT_EPOCHS=5 TTT_LORA_RANK=8 TTT_LM_RANK=16 \
+TTT_LORA_LR=0.01 TTT_BIAS_TUNE=1 TTT_TEMP_RESCALE=0.98 \
+torchrun --standalone --nproc_per_node=1 train_gpt.py
+```
+
+## Credits
+
+- Base architecture: PR #414 by @signalrush (verified SOTA 1.1228)
+- TTT approach: PR #77 (samacqua), PR #596 (MatoTeziTanka/DeepQuant)
+- Score-every-epoch protocol: PR #568 (PROTEUS v8)
+- Per-layer LR groups: PR #481 (mrdavtan)

records/track_10min_16mb/2026-03-24_ScoreEveryEpoch_TTT_0.8186/submission.json

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+{
+  "name": "11L + Score-Every-Epoch LoRA TTT 5ep (val_bpb=0.8186)",
+  "author": "mn.hoang",
+  "github_id": "minh-stakc",
+  "date": "2026-03-24T00:00:00Z",
+  "val_loss": 1.38211666,
+  "val_bpb": 0.8186,
+  "bytes_total": 17132081,
+  "blurb": "Score-every-epoch multi-epoch LoRA TTT (5 epochs, LM rank-16, per-layer LR, bias tuning, T=0.98 temperature rescaling) on PR #414 base architecture. Pre-TTT: 1.1264 BPB. Post-TTT: 0.8186 BPB. Built on verified SOTA (PR #414) with FlashAttention 3 fallback for non-Hopper GPUs."
+}