Google researcher Alex Krentsel reproduces the Weight-Space Programming experiment…

Google researcher Alex Krentsel reproduced Exp. 5 of the Weight-Space Programming experiment from the MIT blog post: in a single response, each model, without training or using tools, directly writes out all of the roughly 20,000 weights of a playable Breakout network, with a network trained via PPO reinforcement learning as the 1× baseline. A cross-generation sweep shows that GPT-5 through 5.5 and Opus 4.5 through 4.8 are basically near or below the baseline, GPT-5.6 Sol and Opus 5 slightly exceed 1×, and only GPT-6.1 Sol, GPT-6 Astra and Opus 5.5 jump to about 18×–22×; he believes this is a step-change new capability that has just emerged in the latest batch of models. He emphasizes that the network whose weights are written has only about 20,486 parameters, several orders of magnitude away from trillion-parameter frontier models, and this cannot be extrapolated to mean that large models can already modify themselves; at present it is still an outer model modifying an inner small network, and he says he is reproducing it on open-weight models.