MIT: RLCR teaches AI models to say 'I'm unsure' and improves calibration

Friends, a quick AI update: researchers at MIT CSAIL propose RLCR, a method for models to assess their own confidence.

- What they did: added a Brier-score penalty to the reward function so the model outputs a confidence estimate alongside its answer.
- Result: calibration error fell by up to 90% without loss of accuracy; effect generalised to new tasks.
- Practice: models learn to "reflect" on uncertainty, improving answer selection.

Why it matters: reduces the risk of misleading confidence in critical domains.

Are AI product teams ready to adopt this training?

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