This AI Learns in Two Minds (Slow RL, Fast GEPA)

The video's central move is to stop treating LLM adaptation as a single process that must all be written into the tensor weights. Instead, it models adaptation as a coupled cognitive system with two interacting channels: a slow parametric channel θ and a fast textual/contextual channel ϕ. Slow and Fast together - a coherent learning process for AI. We bring together the classical RL with verifiable rewards and the skill.md and memory.md complexity from the AI harness. The slow channel is the ordinary model update path: expensive, persistent, and global. The fast channel is the prompt, instruction, reflection, and context layer: cheap, editable, and temporary. The key claim is that these two channels should co-evolve, not be optimized in isolation. This AI Learns in Two Minds Why LLMs Need Two Timescales of Learning all rights w/ authors: "Learning, Fast and Slow: Towards LLMs That Adapt Continually" Rishabh Tiwari∗ 1,4 Kusha Sareen∗ 2 Lakshya A Agrawal∗ 1 Joseph E. Gonzalez 1 Matei Zaharia 1 Kurt Keutzer 1 Inderjit S Dhillon 3 Rishabh Agarwal† 2,5 Devvrit Khatri† 3,6 from 1 UC Berkeley 2 Mila 3 UT Austin 4 Eragon 5 Periodic Labs 6 Mirendil #aithinking #scienceexplained #aiexplained #airesearch