Unverified50% confidenceFactExact time
Backpropagation requires global gradient transmission with strong coupling between layers, making parallelization and distributed training more complex from an engineering perspective
1
Sources
50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA core long-standing challenge for non-backpropagation methods is that their final performance on large-scale tasks is often difficult to match that of mature backpropagation training76% similarUnverified大模型训练高度依赖集群内部的高速互联（如NVLink或InfiniBand），跨云迁移意味着需要重新优化分布式训练的通信拓扑74% similarUnverified权重归一化技术将权重向量分解为方向与模长两个独立分量分别优化，可显著改善训练稳定性73% similarUnverifiedIn multi-objective joint training, gradient conflicts between different objectives may cause optimizer oscillation and training curve instability.71% similarUnverifiedEnd-to-end joint training can avoid the cumulative propagation of errors that occurs in modular cascaded architectures, which is the fundamental reason it performs better in complex environments.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/978872API
curl https://kongchang.com/api/v1/knowledge/claims/978872MCP
get_claim(id=978872)