RLCD: Reinforcement Learning for Calibrated Decisions
Standard RLVR rewards a correct answer with +1 whether the model was guessing or genuinely sure, so models learn to sound confident even when they aren’t. RLCD (Reinforcement Learning for Calibrated Decisions), the post-training method behind TypeSafe AI’s model Jev, targets calibration instead: when the model says it’s 88% sure, it should be right 88% of the time.
In this article, published in Towards AI, I cover:
- What calibration is, and how ECE and the Brier score measure it
- Why binary rewards push models toward overconfidence
- How proper scoring rules and a per-bin calibration penalty fit into PPO and GRPO
- What TypeSafe claims about Jev’s speed, schema guarantees, and calibration
