A $20 reward function can strip a model's safety alignment; Bait probes the reward to block the learning
Agentic reinforcement-learning-as-a-service hands data sampling and reward rating to the user, and a harmful reward function can erase a model's safety alignment at a cost as low as $20. Bait mixes explicit harmful responses from a separately trained Bait model into the policy model's response group during rollout, uses the sign of the reward gap between Bait and non-Bait responses (reward divergence) to flag a harmful reward function, and flips that group's reward sign to penalize learning when it detects one. On the authors' testbed, Bait cut the average harmful score across three harmful-instruction datasets from 20.4 to 6.8 while average agentic accuracy rose from 93.1 to 96.0.