Verified65% confidenceFactExact time
Deep learning training has inherent randomness, and retraining with the same configuration does not necessarily yield the same results; sources of randomness include the random seed for weight initialization, the shuffling order of data batches, random noise introduced by Dropout, and non-deterministic accumulated floating-point errors in distributed training.
3
Sources
65%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
AI模型版本管理困境：从一则行业传闻看迭代乱象
redditr/singularity7/10/2026
Related Claims
VerifiedDomain Randomization forces a policy to learn robustness to environmental changes by randomizing simulation parameters during training.78% similarUnverifiedWhen saving checkpoints during distributed training, the data loader state and random seeds must also be saved; otherwise, after recovery the data order and augmentation results will be inconsistent, making training irreproducible.76% similarUnverifiedThe score improvement in the first round of random task training was very small, because a large amount of training time was wasted on samples the model stably got right or wrong.76% similarUnverifiedTemporal randomization during training allows the policy to learn from asynchronous, multi-frequency data without needing to align all measurements to a fixed time grid.75% similarUnverified实验表明按由易到难顺序喂给模型训练样本能加速收敛并学到更鲁棒的特征表示，性能优于随机打乱顺序的训练74% similar
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