[KongchangAI]
Verified80% confidenceFactExact time

An excessively large learning rate in gradient descent causes parameters to oscillate or even diverge around the optimal solution, while an excessively small rate results in extremely slow convergence.

6
Sources
80%
Confidence
Long-term
Relevance
7/13/2026
First Seen

Sources

Do You Still Need to Hand-Code Machine Learning Algorithms in the AI Era? The Answer and the Method
redditr/learnmachinelearning7/11/2026

Related Claims

UnverifiedAn excessively high learning rate causes loss oscillation that prevents convergence, while an excessively low one results in extremely slow learning — both are common root causes of ineffective training.82% similarUnverified若验证损失早于训练收敛就停滞或反弹，说明增强强度可能需要微调73% similar

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