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Reinforcement Learning with Human Feedback (RLHF) / Reinforcement Learning with Verifiable Rewards (RLVR) Reinforcement Learning for Calibrated Decisions (RLCD) Optimizes for Human preference: writeups and chat responses that human raters prefer. Verifiable rewards: outputs that can be programmatically verified. Calibrated decisions: answers with epistemically honest probabilities on System One tasks. Inputs Unstructured data (e.g. text) with an emphasis on sequential messages. Unstructured data (e.g. text) with an emphasis on structured program state. Outputs Strings / generated text. Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be