Building a Loop LM on Colab: 40% Compute Saved with Exit Gates
Flashy-Abalone-9212 · reddit · 2026-08-20
A developer experimented with building a Loop Language Model (Loop LM) on Google Colab's free GPU, aiming to increase computational depth by reusing the same Transformer layer multiple times instead of making the network physically deeper.
Architecture & Experiments
- The model consists of 6 Transformer layers with 4 recurrent loops.
- Combines sparse attention, compressed attention, sparse MoE, and an adaptive exit gate mechanism.
- Attempted to introduce diffusion-based optimization (still in progress).
Training Strategy
- Stage 1 forces execution of all 4 loops to ensure training signals for every layer.
- Stage 2 freezes the LM and trains the exit gate separately (only 513 parameters).
Results
- On the validation set, the gated model loss is nearly identical to the full 4-loop model (+0.015% difference).
- Average loop depth reduced to 2.41, saving an estimated 39.75% of compute.
- Exit distribution: 59% exit at loop 2, 41% at loop 3.
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