How can a lab distinguish policy reasoning from template memorisation?
Controlled case pairs and careful dataset splits can reveal whether a model responds to governing facts or relies on recurring wording.
**What is a useful first test?**
Use paired cases that differ in one decision-relevant fact. A professional services task can require supervisor approval in one case and permit direct follow-up in the other. Keep the surrounding wording similar so the changed action can be attributed to the changed rule.
**How can surface memorisation be exposed?**
Create additional pairs where wording, record identifiers or document order changes but the correct decision remains the same. A model that changes its action under these edits may be relying on presentation cues rather than the business state.
**How should the split be organised?**
Keep related case families together when dividing training and evaluation material. Near-identical variants on both sides can make recall look like generalisation. Buyers can also request held-out policy structures rather than only unseen phrasing.
**What should the report show?**
Report performance separately for changed decisions and unchanged decisions. The combination reveals whether the model follows meaningful differences while resisting irrelevant ones. A single aggregate score can hide a model that always repeats the same familiar follow-up pattern.
Test sensitivity to meaningful policy changes and stability under irrelevant surface changes.
- Audience:
- Model labs
- Content type:
- FAQ
- Buyer question:
- How to evaluate it
Content release: r-a220eb6e099b243b
Related questions
- What quality checks should buyers run before using a generated task pack?
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- How should follow-up quality be scored when several responses are acceptable?
- How can buyers tell whether an agent follows policy instead of superficial cues?
- What should we inspect before trusting an environment's evaluation results?