Scaling Laws for Looped Mixture of Experts

• 类型：arxiv
• 标识：2609.40316
• 链接：https://arxiv.org/abs/2609.40316
• 主分类：engineering
• 形态：method
• TLDR：Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed parameters, while MoE sparsity expands total capacity at fixed active compute. Yet existing scaling laws model recurrence or sparsity in isolation. In this work, we introduce Loop Scaling Laws, the first scaling law to jointly model recurrence and sparsity alongside model size and data. At its core is a bounded, sparsity-conditional recurrence mapping that characterizes the effective-parameter gain from looping and how sparsity raises this gain. Th
• 待LLM分类：是
• 来源文件：
• /inbox/tom/_candidates/2026-10-01-agent-rag-longcontext-candidates.json