Deep Cogito Releases Suite of LLMs Trained with Iterative Policy Improvement

DeepCogito's release of Cogito v2 marks a significant advancement in AI intelligence through hybrid reasoning and multimodal models, offering a new approach to scalable superintelligence by focusing on improving intuition over lengthening reasoning chains. This innovation allows Cogito models to maintain performance levels while using 60% shorter reasoning paths, leading to reduced computational costs and increased efficiency. The release includes four models, both dense and mixture of experts (MoE), catering to varying computational needs and designed for easy integration with existing systems. The underlying technique, iterative policy improvement, enhances the model's base intelligence with each iteration, fostering better problem-solving abilities without simply extending inference time. Performance tests have shown notable inference speed improvements compared to equivalent models, translating into cost savings in serverless architectures. Cogito v2 is accessible on the Runpod platform, with resource recommendations provided to optimize deployment, and this development positions DeepCogito as a leader in creating more intelligent, efficient AI systems.