MoE

MoE on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on moe in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around moe, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

Machine Learning

Does this idea sound fun? [R]

Are you curious about inference-time learning? I’ve explored a novel approach within the Mixture of Experts (MoE) framework, integrating specialized experts to update sibling expert weights. While the components were already in place, this idea hadn’t been tested before, and my proof of concept showed promising results. I invite your feedback on this concept. For further insights, check out my related article on an innovative LLM post-training method called RPS, which highlights how we can enhance program synthesis reliability.

Machine Learning

DeepSeek V4 paper full version is out, FP4 QAT details and stability tricks [D]

This week, DeepSeek released the full version of their V4 paper, expanding on the technical insights previewed in April. Notably, the introduction of FP4 quantization-aware training (QAT) during late-stage training significantly enhances performance, achieving a 2x speedup on the QK selector while preserving 99.7% recall. The paper also addresses training stability with innovative mechanisms like anticipatory routing and SwiGLU clamping. Additionally, the generative reward model streamlines evaluations with minimal human labeling. Explore the detailed findings to understand how these advancements could reshape your data management

Machine Learning

Transformer Math Explorer [P]

Introducing the Transformer Math Explorer, an interactive math reference designed to demystify transformer models through intuitive dataflow graphs. Covering a range from GPT-2 to Qwen 3.6, this tool allows users to toggle between various models and concepts, including MLA, MoE, RoPE, MTP, and hybrid attention. Originally created for personal use, it aims to provide clarity in understanding complex variations. If you encounter any errors or find aspects that are unclear, your feedback is invaluable for enhancing this resource.