Over the past thirty years, governments have been faced with complex public problems (or “wicked†problems) that cannot be managed by a single organization. To respond to these problems, the traditional hierarchy has given way to arrangements between multiple public and private actors, called collaborative networks. In this article, learning, understood as changes in the opinions, beliefs and knowledge of network members, is approached as a driver of public innovation. This relationship is examined based on the qualitative analysis of 51 semidirected interviews with members of four Belgian governance networks involved in public innovation. We find, first, that learning is a necessary but not sufficient condition of public innovation. Relational learning, i.e., acquiring knowledge of other members of the collaborative network, serves as a basis for political learning, i.e., acquiring knowledge about the political context and strategies, and for policy learning, i.e., acquiring knowledge about the policy issues and solutions. The extent of learning within the network results from a right mix of different kinds of expertise among network members and depends on the degree of their prior knowledge. There are collective and structural conditions (e.g., atmosphere or the presence of a coordination team) that account for both learning and innovation, whereas there are exogenous conditions (e.g., political support) that account for the dynamic process linking learning to innovation. Last but not least, the transformation of learning into public innovation depends less on amounts or types of learning than on actual prospects of implementation. To conclude, we draw the theoretical, methodological, and practical implications of these results.