This paper proposes Trust Region Q Adjoint Matching (TRQAM), an off-policy fine-tuning algorithm for reinforcement learning that adapts a trust-region parameter to control the path-space KL between the fine-tuned and pretrained policies. TRQAM achieves stable off-policy RL and outperforms prior methods on 50 OGBench tasks.