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
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
Real-world datasets often follow a long-tailed distribution, making generalization to tail classes difficult. We revisit this problem through the lens of shortcut learning, where models prefer the easiest predictive cues (e.g., background or textures) over object-centric semantics, especially under scarce and biased su...
Wen-Hai Wan, Teng-Fei Zhang, Shao-Yuan Li et al.· 0 citations
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