Record: Depth Recurrence (layers 4 and 5 repeated): val_bpb 1.1182#686
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msisovic wants to merge 6 commits intoopenai:mainfrom
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Record: Depth Recurrence (layers 4 and 5 repeated): val_bpb 1.1182#686msisovic wants to merge 6 commits intoopenai:mainfrom
msisovic wants to merge 6 commits intoopenai:mainfrom
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Seed 1337 complete (val_bpb=1.1179). Seeds 42 and 2024 need rerun after GPU restart (stale CUDA contexts blocking clean runs). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
msisovic
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Summary
Building on PR #549, I explored two directions for improving val_bpb: width scaling (MODEL_DIM=576) and depth scaling (adding layers). Width scaling to dim=576 provided a regression in performance. Depth scaling to 12 independent layers at dim=512 reached 1.1126 post-TTT - significantly better - so I decided to go in that direction.
This led me to depth recurrence: re-executing mid-network layers with independent learnable block scalars, getting the depth benefit without the parameter/size cost. Layers 4 and 5 are each executed twice in sequence (pattern: 0,1,2,3,4,5,4,5,6,7,8,9,10), producing 13 virtual layers from 11 physical. Only ~2K block scalar params are added. Dual recurrence recovers ~70% of the independent 12-layer gain while keeping the artifact well under budget at ~15.9MB.
I also confirmed that tied TTT (no weight untying for recurrent layers) performs equivalently to untied, and that the TTT gain (~0.0025 BPB) is consistent regardless of ecurrence config. Everything else (TTT, int6 quantization, SWA, bigram embeddings, value embeddings, Muon optimizer) is inherited from #549.
Reproducibility