TSR Desk · science · 4 November 2026, 01:00 UTC
Can You Break RLVER? Probing Adversarial Robustness of RL-Trained Empathetic Agents
- What
- Can You Break RLVER? Probing Adversarial Robustness of RL-Trained Empathetic Agents
- Who
- arxiv.org
- When
- 3 November 2026, 05:00 UTC
- Category
- Science
- Primary source
- https://arxiv.org/abs/2605.07138
- What is not known
- This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
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. It comes from a paper posted to arXiv on 3 November 2026. 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. 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.
Why it counts
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. 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. We call this an outcome--legibility dissociation: RLVER improves emotional outcomes without making the user's state more legible.
Sources
Primary source: primary source
What is not known
This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
No clip. The article still stands.