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Researchers Fix Numerical Bugs in Softmax and Cross-Entropy Implementations

By PulseAugur Editorial · [1 sources] ·

Researchers have identified and fixed two numerical bugs in standard implementations of softmax, log-softmax, and cross-entropy. The first bug, Softmax Collapse, causes the target logit's gradient to round to zero when one logit is significantly larger than others, disrupting the gradient sum. The second bug, Softmax Cancellation, produces non-zero gradients even when student and teacher logits are identical, occurring when these logits are close. The proposed solutions eliminate the "Slingshot" phenomenon, accelerate grokking with weight decay, and prevent accuracy collapse without it. AI

IMPACT Fixes may improve training stability and efficiency for models using these common functions.

RANK_REASON The cluster contains an academic paper detailing a technical fix for common machine learning functions. [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 →

Researchers Fix Numerical Bugs in Softmax and Cross-Entropy Implementations

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The cluster contains an academic paper detailing a technical fix for common machine learning functions. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jonathan T. Barron, Dan Gnanapragasam, Michael Tschannen ·

    A Fix for Softmax and Cross-Entropy

    arXiv:2610.31642v1 Announce Type: new Abstract: Standard implementations of softmax, log-softmax, and cross-entropy have two numerical bugs. The first bug, Softmax Collapse, rounds the target logit's gradient to zero if that one logit is much larger than the others, resulting in …

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