Unverified50% confidenceSolutionExact time
When batch size increases, it is generally necessary to increase the learning rate accordingly following the linear scaling rule.
1
Sources
50%
Confidence
Long-term
Relevance
8/17/2026
First Seen
Sources
Hugging Face复现2200篇ICML论文：AI研究可复现性危机全面曝光
Related Claims
UnverifiedLarger batch sizes typically require higher learning rates (the linear scaling rule).78% similarUnverified即便固定所有随机种子，在不同硬件或并行度下，训练结果仍可能出现被大模型放大为显著能力差异的细微偏差71% similarUnverifiedThe 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.69% similarUnverifiedAs large model reasoning capabilities strengthen, the tokens consumed per single call rise significantly, creating a paradox of 'stronger model' versus 'faster limit reached' under fixed usage limits.69% similarUnverifiedGradient accumulation accumulates gradients over multiple small batches before performing a single parameter update, with an effect equivalent to training with a large batch.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/762269API
curl https://kongchang.com/api/v1/knowledge/claims/762269MCP
get_claim(id=762269)