Non-record: 15L Depth Recurrence + LeakyReLU² — BI-guided weight tying (val_bpb=1.1360)

[aruniyer]

Summary

val_bpb: 1.1360 (seed 1337) | 15.87 MB | 8xH100 SXM

Non-record submission exploring BI-guided depth recurrence: using Block Influence scores (ShortGPT) to identify
which layer positions can share weights, enabling 15 effective layers from 11 unique parameter blocks within the
same 16MB budget as standard 11L.

Result

Seed	Steps	Sliding BPB	Artifact
1337	5173	1.1360	15.87 MB

Key Technique: BI-Guided Weight Tying

1. Train a 15L model, measure Block Influence (angular distance input→output per layer)
2. Layers 9–13 have lowest BI (0.10–0.16) — near-identity transformations
3. Tie those 5 positions to share one physical block → 15 virtual layers, ~27M unique params
4. Deduplicate before quantization: store shared weights once with reconstruction map
5. Int6 + zstd-22 → 15.87 MB ✅

Depth vs Steps Tradeoff

15L runs at 116ms/step (vs 86ms for 11L) due to 4 extra forward/backward layers. In 600s: ~5170 steps vs ~6975 for
11L. The depth advantage doesn't fully compensate for ~1800 fewer steps in this wallclock-limited setting. At equal
step counts, 15L outperforms 11L.

Architecture

15L (10 unique + 1 shared×5), 512d, 8H/4KV GQA, MLP 3x, LeakyReLU(0.5)², XSA4, Partial RoPE 16/64, LN Scale, VE128,
SmearGate, BigramHash(2048), EMA, SWA, Late QAT, int6+zstd-22, FA3.

Reproduce

SEED=1337 NUM_LAYERS=15 TIE_LAYERS=9,10,11,12,13 \
  DIFF_ATTN=0 VRES_ENABLED=0 TTT_EPOCHS=0 \
  torchrun --standalone --nproc_per_node=8 train_gpt.py

Credits

Base: signalrush (PR #374/#414). LeakyReLU²: PR #493, PR #518. Block Influence: ShortGPT (arXiv:2403.03853).