CLM-8B: Contrastive Language Models

Jev made System 1 models hot. CLM from Stanford and NVIDIA Research takes a completely different approach: it never generates an answer. Instead, it embeds the situation and every possible action into the same space and picks whichever action lands closest. Because actions are embedded once and cached, 1,000 options cost about the same as one: 44 ms vs 579 ms for Jev. The catch: each option is encoded on its own, so it never compares them side by side. Full breakdown + demos on my DGX Spark: [LINK TO FULL VIDEO] 📄 Blog: https://contrastive-lm.notion.site 💻 Code: https://github.com/Contrastive-LM/CLM #AI #LLM #MachineLearning #SystemOne #Jev #CLM #NVIDIA