We dive into DisCIPL (Decentralized Collaborative Intelligent Planning Language model), a two-part framework that splits reasoning into a planner LM that writes a task-specific program and a swarm of cheap follower LMs that execute in parallel. The planner acts as a blueprint-writer and gatekeeper, guiding thousands of quick, inexpensive attempts and filtering them against constraints. This setup lets small, affordable models match or beat a single giant model on hard tasks—from precise rhyming to strict-budget itineraries—while delivering huge efficiency gains. We also explore how this approach points toward fully recursive, self-steering AI and the future of scalable, autonomous problem solving.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC