Background: Theoretical and empirical accounts of conduct disorder (CD) suggest problems with reinforcement learning as well as heightened impulsivity. These 2 facets can manifest in similar behavior, such as risk taking. Computational models that can dissociate learning from impulsive initiation of actions are essential for understanding the cognitive mechanisms that underlie CD. Methods: A large, international sample of youths from 11 European countries (N = 1418; typically developing [TD] n = 742, CD n = 676) completed a learning task. We used computational modeling to disentangle reward and punishment learning from action initiation. Results: Punishment learning rates were significantly reduced in youths with CD compared with their TD peers, suggesting that youths with CD did not update their actions based on punishment outcomes as strongly. Intriguingly, youths with CD also had a greater tendency to initiate actions regardless of outcomes, although their ability to learn from reward was comparable to that of their TD peers. We also observed that variability in action initiation correlated with self-reported impulsivity in youths with CD. Conclusions: These findings provide empirical support for a reduced ability to learn from punishment in CD, while reward learning is typical. Our results also suggest that behaviors that appear superficially to reflect reward learning differences may reflect heightened impulsive action initiation instead. Such asymmetric learning from reward and punishment, with increased action initiation, could have important implications for tailoring learning-based interventions to help individuals with CD.