Background and aims: Maladaptive eating habits are a major cause of obesity and weight-related illness. The development of empirically-based approaches, such as mindfulness training (MT) that target accurate mechanisms of action to address these behaviors is therefore critical. Two studies were conducted to examine the impact of MT on maladaptive eating and determine the involvement of reinforcement learning mechanisms underlying these effects. Methods: In Study1, maladaptive eating behaviors were assessed using self-report questionnaires at baseline and 8 weeks after an app-based MT intervention (n 5 46). A novel mindful eating craving tool was embedded in our intervention to assess: eating behaviors (intake frequency/magnitude), and reward (contentment ratings) experienced after eating. Using a wellestablished reinforcement learning (Rescorla-Wagner) model, expected reward values (EV) were estimated as a function of contentment levels reported after eating. In Study2 (n 5 1,119), craving tool assessments were examined in an independent sample using the app in a real-world naturalistic context. Results: Study 1?s results revealed a significant decrease in EV and eating behaviors across craving tool uses. In addition, changes in reward values predicted decreases in eating behaviors. Finally, Study 1?s results revealed significant pre-post intervention reductions in self-reported eating behaviors. In Study2, we observed a significant decrease in EV, but not in eating behaviors, across craving tool uses. Study 2 also revealed a predictive relationship between EV and eating behaviors. Discussion and conclusions: These results support the implementation of MT to prevent and treat maladaptive eating behaviors, which target reinforcement learning processes as mechanisms of action.  Kulcsszavak: maladaptive eating, craving-related eating, mobile mindfulness training, reinforcement learning, computational modeling, reward value