Author

Cheng Liang

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Preprint Oct 2026

Asymmetric Looped Transformers: Fixed Global Representations for an Iterative Decoder

Looped Transformers increase effective depth by repeatedly applying shared layers, offering a way to scale computation without increasing parameter count. However, full-stack looping places context construction and iterative state refinement within the same recurrent computation, without explicitly distinguishing their...

Cheng Liang, Teng-Fei Zhang, Wenjie Du et al. · 2 citations

#machine learning Preprint Oct 2026

UpLoop: Training Looped Transformers via Upcycling at No Extra Pretraining Compute

Looped language models can strengthen reasoning through repeated computation with shared weights, increasing execution depth without adding independent parameters. However, each additional pass increases training floating-point operations (FLOPs), making these gains more expensive to obtain. To address this cost, we pr...

Cheng Liang, Teng-Fei Zhang, Ya Zhang et al. · 0 citations