Summary

• val_bpb = 1.1401 (seed=1337, sliding window stride=32)
• artifact = 15.4 MB (int6 + zstd-22)
• 8×H100 SXM, 600s wallclock, ~7100 steps at ~82ms/step

11-layer transformer stacking: XSA (last 4 layers), EMA (decay=0.997), Test-Time Training (3-epoch SGD), U-Net skip connections, Partial RoPE (16/64 dims), LN Scale, SmearGate, BigramHash, OrthoInit, and late QAT (absmax int6 STE).

Key findings

• SmearGate should use interpolation (torch.lerp(x, prev, g)), not additive blending. The additive formula inflates magnitude at default gate values.
• BigramHash benefits from XOR-based hashing with large primes and a learned output scalar (init 0.05).
• U-Net skip connections with skip added before decoder block and single nn.Parameter tensor for torch.compile compatibility.
• Partial RoPE (16/64 dims) and LN Scale (1/sqrt(layer+1)) provide improvements at zero parameter cost.
• Optimizer coverage: SmearGate and BigramHash parameters must be explicitly added to optimizer groups — they silently freeze from initialization otherwise.

Full development log in records/track_10min_16mb/2026-03-21_11L_XSA_EMA_TTT/SESSION_LOG.md.

Reproduction

cd /workspace
git clone https://github.com/mrdavtan/parameter-golf.git
cd parameter-golf && git checkout 11l-xsa-ema-ttt
pip install flash-attn --no-cache-dir --no-build-isolation
pip install zstandard sentencepiece huggingface_hub
python3 data/cached_challenge_fineweb.py --variant sp1024

unset MLP_HIDDEN QUANT_BITS RUN_ID SEED TIER2_MODE && \
ROPE_DIMS=16 LN_SCALE=1 ROPE_BASE=10000 \
EVAL_STRIDE=32 DOC_ISOLATED_EVAL=0 \
torchrun --standalone --nproc_per_node=8 \
  records/track_10min_16mb/2026-03-21_11L_XSA_EMA_TTT/train_gpt.py

Hardware: 8×H100 SXM (RunPod), PyTorch 2.9.1+cu128, Flash Attention 2