<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Transformer on WorldSense Tech Blog</title><link>https://worldsensetech.com/en/tags/transformer/</link><description>Recent content in Transformer on WorldSense Tech Blog</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Wed, 12 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://worldsensetech.com/en/tags/transformer/index.xml" rel="self" type="application/rss+xml"/><item><title>When World Models Meet Transformers: From RSSM to Large-Scale Sequence Modeling</title><link>https://worldsensetech.com/en/articles/world-model-transformer/</link><pubDate>Wed, 12 Aug 2026 00:00:00 +0000</pubDate><guid>https://worldsensetech.com/en/articles/world-model-transformer/</guid><description>&lt;p&gt;In previous articles, we covered the RSSM architecture and training techniques in DreamerV3 in depth. RSSM is a classic design in reinforcement learning world models, but if you follow recent research, you&amp;rsquo;ll notice a clear trend: world models are becoming Transformer-based.&lt;/p&gt;
&lt;p&gt;From Google&amp;rsquo;s UniSim to Wayve&amp;rsquo;s GAIA-1, from NVIDIA&amp;rsquo;s Cosmos to solutions from domestic embodied AI teams, the Transformer is emerging as a key technical approach for large-scale world models.&lt;/p&gt;</description></item></channel></rss>