Verified65% confidenceFactExact time
An 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.
3
Sources
65%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
Behavior Cloning Training Failing? A Systematic Guide to Data Quality and Tuning
redditr/learnmachinelearning7/9/2026
Related Claims
VerifiedAn 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.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76% similarUnverified过于困难的任务可能引入噪声,产生噪声学习信号,反而干扰训练,最有效的学习发生在略高于当前能力但仍可企及的区域74% similar
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