Diff-Attn v2: Differential Attention for Stable Transformer Training and Long-Context Efficiency
Автор: CosmoX
Загружено: 2026-01-24
Просмотров: 0
Описание:
🔹 Overview of Microsoft’s Diff-Attn v2 and its core motivation
🔹 How Differential Attention stabilizes attention score distributions
🔹 Addressing softmax saturation and gradient instability in Transformers
🔹 Training stability improvements for long-context language models
🔹 Architectural differences between Diff-Attn v2 and standard attention
🔹 Implications for large-scale LLM training efficiency and scalability
🔹 Why Diff-Attn v2 matters for next-generation Transformer design
#DiffAttnV2 #DifferentialAttention #TransformerModels #LongContext #LLMTraining #AttentionMechanism #MicrosoftAI #AIResearch
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