I recently gave a talk introducing young economists to post-training. The slides are now up! kawine.github.io/assets/aies… Not only is post-training your own model more doable than ever, but economists and other social scientists can play a big part in the next era of post-training. The post-training suite looks roughly like: 1) SFT 2) Offline Methods (DPO/KTO/SimPO/etc) 3) Online Methods (PPO/GRPO/CISPO/etc) 4) RL Environments 5) Distillation (<--- we are here) 6) World Adaptation (<--- the future) As agents are deployed in the real world, the real world will adapt to them. This adaptation is largely overlooked by the current post-training paradigm because working with verifiable rewards from static environments is much simpler and scalable. As we saw with the OpenAI-Huggingface incident however, we shouldn't assume that the real world will remain fixed---quite the opposite, in fact. So: 1. How can we anticipate real-world adaptation during post-training? 2. What do equilibria look like in these scenarios, if there are any at all? 3. What tools can be created to scale up human oversight? Many such questions abound, and if you're an economist, these are high-impact problems worth studying.