RL environment specs are among the most consequential things we can write as AI researchers. A relatively short spec (e.g., <1000 words of instructions saying what problems to create and how to grade them) often gets expanded either by humans or via synthetic methods into thousands of datapoints. Just one sentence in the spec can be the difference between a perfect post-trained model versus one with crazy hacking. Specs are also typically a product of a large amount of compute, where each training run allows us to iterate on the spec to patch reward hacking and get the nuances of model behavior just right. Writing a good spec requires context and taste and I don’t think AI can automate this just yet

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