Abstention in RL can collapse reward gradients, endangering hallucination mitigation
The paper proves that error‑penalized scoring (+1/‑λ/0) used in RL alignment can erase both the reward gradient and KL anchor, driving models to abstain everywhere while reward appears to rise. Their repair trains a continuous confidence report with a proper score and applies abstention only at inference, restoring coverage and accuracy.