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Transformer rank collapse linked to gradient path, not loss strength

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a phenomenon in transformers called "rank collapse" where all token representations align in a single direction, halting learning. Experiments with weakened skip connections showed that adding a loss term did not repair this collapse. The issue stems from the gradient path, not the force of the loss, as the gradient failed to reach critical query and key weights. Restoring the skip connection, however, immediately reopened the path and allowed the rank to recover. AI

IMPACT Identifies a critical failure mode in transformers, potentially guiding future architectural improvements and training methodologies.

RANK_REASON The cluster contains a research paper detailing a specific technical finding about transformer models. [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 →

Transformer rank collapse linked to gradient path, not loss strength

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The cluster contains a research paper detailing a specific technical finding about transformer models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Martin Hofmann, Patrick M\"ader ·

    Force without transmission: a depth-induced rank collapse that no loss on the representation reopens

    arXiv:2610.09958v1 Announce Type: new Abstract: Training can drive a transformer into a rank collapse: all token representations point in one direction, and learning stops. In a related collapse of attention, a loss term with a bounded corrective force repairs the network during …

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