The mechanism
The score is called the reward. There are two ways to get one.- A program. The answer matches the reference, the tests pass, the tool was called before the reply. When a program can check the job, use the program. It is cheap, repeatable, and cannot be flattered. The field calls this a verifiable reward, and the program a verifier.
- A model. When no program can check the job (was the tone right, did it explain the policy), a model reads the reply against a written rubric and answers 0 or 1. That is an LLM judge.
Run it
The setup is lesson 2’s run. The judge below is the one this course uses from here to lesson 7. It is a program over the row’smessages, so it
scores the stand-in and, in lesson 7, a trained model, with the same
three rules: look the order up with the id the customer gave (and ask
when there is none, rather than guess), lead the reply with that order,
and claim nothing the tool did not return.
trusted: False: with
only 40 labels the lower bound on that agreement is 68%, under its 80%
floor, and it flags that the judge passes long replies more often than
short ones with the same label. Read trust["disagreements"] before you
argue with it. Five are asks that name no order, where the stand-in
guessed an id and its answer key does not count that as a mistake; two
are replies that quote the hidden answer key, which this judge does not
read (lesson 6 drops those rows before training). The number told you
where to look, and you looked. That is what checking the checker means.
The numbers after each pass rate are the interval. Lesson 4 is about
them. For now:
0.58 [0.38..0.77] means the true rate very likely sits
between 38% and 77%.Where it comes from
- Lambert, N. et al. Tülu 3: Pushing Frontiers in Open Language Model Post-Training. arXiv:2411.15124, 2024. Reinforcement learning with verifiable rewards: the reward is a program.
- Zheng, L. et al. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. NeurIPS, 2023. arXiv:2306.05685. How well a model judge agrees with people, and where it is biased.
- Cohen, J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 1960. Kappa.
- Lambert, N. Reinforcement Learning from Human Feedback. arXiv:2504.12501, 2025. Chapter Reward Modeling.
Next
A pass rate without an interval is a guess: what0.58 [0.38..0.77] means and why the brackets matter more than the
number.