Reinforcement learning from verifiable emotion rewards (RLVER) yields strong empathetic language models, but is evaluated with cooperative, honest simulated users. Real users gaslight, escalate, and demand unconditional validation. We introduce the Adversarial Empathy Benchmark (AEB), six adversarial trajectory types whose discriminative rewards penalize formulaic comfort, and the Emotional Consistency Score (ECS), which separates whether a model \emph{improves} the user's emotional state from whether that state stays legible in the transcript. Across eight scenario-matched conditions (480 dialogues; two RLVER and two Qwen2.5 base models, with and without thinking), RLVER-PPO-Think outperforms its untuned same-scale baseline (FS $0.979$ vs.\ $0.761$, $p<10^{-12}$) with no dialogue collapses and 53\% higher hidden-intention detection; the gain is not explained by response length. Yet under an independent cross-family judge (Gemma-3-4b-it), ECS is indistinguishable between the two ($p=0.43$). We call this an outcome--legibility dissociation: RLVER improves emotional outcomes without making the user's state more legible. We do not claim ECS measures internal understanding.

翻译：暂无翻译