Unverified50% confidenceFactExact time
The MoE architecture faces the problem of uneven expert load, with vastly different activation frequencies among different experts, easily causing some devices to be idle while others are overloaded.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedMoE kernels need to simultaneously address both compute regularization and load balancing, as uneven load across experts drags down overall utilization.75% similarUnverifiedThe Top-K routing strategy in MoE is prone to causing a small number of experts to be overused (the expert collapse problem), which requires techniques such as auxiliary load-balancing loss functions to address.73% similarUnverifiedMoE模型量化面临额外挑战,因不同专家权重分布差异大,统一量化方案易导致部分专家精度严重下降72% similarUnverifiedExcessive domain randomization can cause policies to become overly conservative and degrade in performance; system identification requires extensive engineering measurement work and is difficult to cover all physical parameters.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/995751API
curl https://kongchang.com/api/v1/knowledge/claims/995751MCP
get_claim(id=995751)