Evals & Harnesses beyond Code Mode & Pass Rate: in a Math degree, assignments & exams are largely closed book + no fancy calculator this is for good reason tooling can help you get answers, but putting constraints on your tools helps you sharpen innate skills developing reasoning skills on the underlying structure of problem solving is often more valuable to learn than how to use a tool this imo totally applies to LLM RL training & the current code mode paradigm I LOVE code mode and find it amazing how well models can use code to solve any problem, but using code as a tool is a decision on giving a model a very powerful harness to solve problems, Python is Turing Complete ofc this has tradeoffs such as not being able to solve other problems Im seeing this pretty distinctly in visual reasoning tasks that don’t have code mode, models just aren’t great at persistence or visual perception without code Maybe that’s a fine tradeoff, but AGI level should be able to do tasks without code and I bet training in harnesses without code mode will make them generally more intelligent

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This evaluation and improvement process exactly mirrors the evolutionary process I described earlier. It means looking at how to improve or change the design or people to achieve your goals. Schematically, the process is a feedback loop, as shown in the diagram here. #principleoftheday#