What are the best current techniques to have autoresearch behave better than (slightly improved) random search? By which I mean (in Sijun below example), having the agent understand that (given some constraints) exploring int5 quantization is more exciting and have more downstream fruits than playing with the random seed? I’m talking about the beginning of having an agent pushed a real research program. The ones where you know the current technique will not give crazy results out of the box but it still push it because it believe and can demonstrate that the general direction has potential. Like neural networks used to be a worse way to do AI performance-wise. But we still pushed them… Sijun Tan (@sijun_tan) We took @karpathy's autoresearch agent, scaled it into a collaborative swarm, and topped @OpenAI's Parameter Golf Challenge—twice. Here’s how we did it: — https://nitter.net/sijun_tan/status/2036584756729749802#m

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