Understanding and Reducing Misalignment in Persuasion Optimization

Frontier models are now trained for complex multi-agent tasks—from running a company to engaging in negotiations. These tasks necessarily train models to be persuasive. But what other optimization pressures does this create on model alignment? In this work, we study how reinforcement learning (RL) training for persuasion affects models’ use of misaligned strategies, and what techniques can reduce this misalignment. We find that optimizing sender models solely for receiver belief shifts causes the average number of distinct misaligned strategies used per game to increase by more than 25%, and fabrications per game increase to more than 7 times relative to pre-RL models. We compare various mitigation approaches, including stronger strategy guidance, clean demonstrations, penalizing misaligned strategy usage, and improving the model’s ability to detect fabrications. We find that navigating the persuasiveness-misalignment trade-off in this setting is challenging, with the best trade-offs coming from combining guidance on aligned persuasion strategies with reward penalties on misalignment. Overall, our work provides a testbed for assessing misalignment stemming from RL-based persuasion training and shows concrete paths for reducing misalignment. Importantly, it highlights that without direct intervention, persuasion-involved RL environments are likely to naturally lead to large increases in the use of misaligned strategies.