Behavioral cloning outperforms entropy-regularized RL: Critic-driven failure of actor-critic methods on adaptive tumor treatment

Adaptive dosing requires policies that reduce tumor burden without excessive toxicity. Learned dosing policies are typically judged against historical or heuristic comparators, which cannot show whether a policy has found the best behavior available. We instead study a three-population tumor-control ODE in which optimal-control analysis fixes the form of a good schedule -- bang-bang dosing punctuated by a singular arc -- and construct a numerical controller of that form as a proxy for near-optimal behavior. Judged against this reference under a sustained-cure criterion -- 200 consecutive days below 5% carrying capacity -- Soft Actor-Critic (SAC) trained from scratch never reaches cure. Behavioral cloning (BC) of the reference reproduces it (100% sustained cure, 30/30 seeds), but SAC fine-tuning of the cloned policy destroys it across five entropy coefficients, and TD3 and BC-regularized SAC fail identically; the pattern persists under multiplicative pharmacokinetic action noise. Along curative trajectories the post-collapse critic ranks the collapsed-policy action above the reference action in 96% of states, concentrated in the maintenance phase, and the policy settles into a non-curative adaptive-therapy equilibrium. The reference is what makes this legible: against a heuristic comparator the fine-tuned policy would read as a competent controller rather than a failure.