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SwiGLU's open positive tail not necessary for language models, study finds

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the necessity of SwiGLU's open positive tail in decoder-only language models. They introduced MemGLU as a closed-tail alternative and found that across multiple pretraining runs, MemGLU performed nearly as well as SwiGLU in terms of validation NLL. While SwiGLU checkpoints were sensitive to positive-tail suppression, mechanism diagnostics revealed different gate usage between the two models despite similar performance. These findings suggest that language models adapt to the gate geometry provided during pretraining, indicating that SwiGLU's open positive tail may not be essential for decoder-only language model FFNs at the tested scales. AI

IMPACT Suggests potential architectural optimizations for decoder-only language models, potentially reducing computational requirements without sacrificing performance.

RANK_REASON The cluster contains an academic paper detailing novel research into language model architectures. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SwiGLU's open positive tail not necessary for language models, study finds

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The cluster contains an academic paper detailing novel research into language model architectures. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuting Ge, Pengju Yang, Mingkai Nie ·

    Is SwiGLU's Open Positive Tail Necessary? Evidence from Closed-Tail Gating with MemGLU

    arXiv:2608.07323v1 Announce Type: new Abstract: We test whether decoder-only language-model FFNs require SwiGLU's open positive tail. We introduce MemGLU as a closed-tail comparator derived from a memristive branch geometry. Across paired 9M and 30M pretraining runs with three se…

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