Unverified50% confidenceFactExact time
多头自注意力机制突破了RNN架构只能顺序处理的瓶颈，使训练可高度并行化，支撑千亿乃至万亿参数规模扩展
1
Sources
50%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
Getting Started with AI Agents: A Deep Dive into the Perception-Decision-Action Loop
bilibiliAI大模型学习圈7/9/2026
Related Claims
UnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.72% similarUnverified蒸馏攻击通过大规模API调用收集输入-输出对，训练效率可能是常规预训练数据的数十倍71% similarVerifiedDQN introduced two key innovations—experience replay and target networks—where the former breaks the strong temporal correlation in sequential data to stabilize training, and the latter prevents training oscillation and divergence.70% similarUnverified智能体轨迹数据集将成为训练实用智能体的主要瓶颈之一69% similarUnverified智谱GLM-5.3基于743B（7430亿参数）基座进行后训练，主打编程与智能体两大核心场景69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/483044API
curl https://kongchang.com/api/v1/knowledge/claims/483044MCP
get_claim(id=483044)