speculative decoding is not behavior-neutral for agents: numerical noise grows inside verification windows and draft acceptance can shift the target model's output distribution after 30 steps, pushing long-horizon reasoning in random directions. Training the draft to favor rejection exactly at critical reasoning points forces the target to sample those tokens itself, under its least noisy conditions.