Unverified50% confidenceTradeoffExact time
Mamba的选择性扫描机制以O(L)复杂度处理任意长序列,但生成侧训练稳定性仍是活跃研究方向
1
Sources
50%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Related Claims
UnverifiedMamba因选择性机制无法用卷积形式高效并行训练,转而依赖选择性扫描算法进行序列化计算79% similarUnverifiedMamba的状态更新是序列化的,每个token的表示依赖于之前累积的状态,这在推理时是效率优势但在训练阶段带来优化困难76% similarUnverifiedDiffusion models are more stable to train and produce better generation diversity, but their inference speed is slow (typically requiring hundreds of sampling steps); acceleration methods such as DDIM and consistency models are alleviating this issue.63% similarVerifiedStrategies for mitigating LLM judge instability include majority voting over multiple samples, cross-validation using multiple judge models, designing more fine-grained scoring dimensions, and retaining a manual review step.63% similar
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