Reading NTU ML 2026: Can AI Improve Itself? (Part 2) — Improving the Harness, Improving the Improver, and Whether Growth Can Run Away | NTU Hung-yi Lee Machine Learning Topic Guide
Part 1 was about an AI setting its own loss and updating its own parameters. Part 2 fills in the other half: AI Agent = Harness + LLM, and the harness can grow too. You can't take a gradient through a harness, so the usual move is to hand it to a language model as a rewriter and keep a pool of candidates, much like a genetic algorithm (OPRO, GEPA, Darwin Gödel Machine; DSPy if you want a ready-made tool). Three extensions follow: updating harness and parameters together beats updating either alone; when the goal changes you have to choose between discarding everything and carrying everything, and editing a harness can cause forgetting too; and the update rule itself can be updated (HyperAgent, Gödel Agent, SEAL), which is meta learning. Hung-yi Lee closes with a new analogy — parameters are genes, context is the neurons — then argues that today's agents lack intrinsic motivation, and that the likeliest source of runaway growth is a gap between the goal humans meant and the goal the AI inferred.