Unverified50% confidenceFactExact time
In this study, even if the average learning dynamics converge to the correct optimum, the variance of the learning process can become asymptotically infinite.
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Long-term
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10/27/2026
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VerifiedThe learning rate determines how large each step is: too large causes overshooting the valley bottom and divergence, while too small makes training extremely time-consuming.79% similarUnverifiedThe study finds that when the learned computation requires higher-order interactions among input factors, the representation trajectories systematically become less direct (increased path tortuosity).77% similarUnverifiedIn the sampled-gradient setting, when the learning rate η decreases, the expected gain under fixed β vanishes faster than the fluctuation itself, causing the benefit of momentum to be swallowed by noise.76% similarUnverifiedIn long-cycle training, even if one-thousandth of samples are misaligned per epoch, after accumulating over tens of thousands of steps it may significantly change the model's convergence path, making experimental results under different resource schedules lose comparability.75% similarUnverifiedAny study comparing fine-tuning schemes with different numbers of trainable parameters should be wary of the shared learning rate trap; otherwise, conclusions that fewer parameters yield better results may need to be revisited.74% similar
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