Section 115 · Chapter 15, How Models Work
Testing RLHF, RLAIF, and Reward Model Behavior
Preference tuning teaches models what gets rewarded. That is not the same as teaching truth.
RLHFRLAIFreward modelrlhf rlaif reward model behavior
What to do
- Test for sycophancy, over-refusal, under-refusal, confidence inflation, reward hacking, hidden regression, and style-over-substance.
- Compare human preference, expert correctness, automated judge score, and production outcome as separate signals.
- Define runnable checks that exercise RLHF, RLAIF, and reward model.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for RLHF, RLAIF, reward model, rlhf rlaif reward model behavior needed to reproduce work on Testing RLHF, RLAIF, and Reward Model Behavior.
- Report results for RLHF, RLAIF, reward model, rlhf rlaif reward model behavior by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Test for sycophancy, over-refusal, under-refusal, confidence inflation, reward hacking, hidden regression, and style-over-substance. Compare human preference, expert correctness, automated judge score, and production outcome as separate signals.
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Cite this page
Jason Arbon. "Testing RLHF, RLAIF, and Reward Model Behavior." Testing AI Knowledge Edition, section 115.
https://jarbon.ai/testing-ai/knowledge/ch115-rlhf-rlaif-reward-model-behavior.html