How does language emerge, evolve and gets transmitted between individuals? What mechanisms underly the formation and evolution of linguistic conventions, and what are their dynamics? Computational linguistic studies have shown that local interactions within groups of individuals (e.g. humans or robots) can lead to self-organization of lexica associating semantic categories to words [13]. However, it still doesn't scale well to complex meaning spaces and a large number of possible word-meaning associations (or lexical conventions), suggesting high competition among those conventions. In statistical machine learning and in developmental sciences, it has been argued that an active control of the complexity of learning situations can have a significant impact on the global dynamics of the learning process [2, 4, 3]. This approach has been mostly studied for single robotic agents learning sensorimotor affordances [6, 5]. However active learning might represent an evolutionary advantage for language formation at the population level as well [8, 12]. Naming Games are a computational framework, elaborated to simulate the self-organization of lexical conventions in the form of a multi-agent model [11]. Through repeated local interactions between random couples of agents (designated speaker and hearer), shared conventions emerge. Interactions consist of uttering a word – or an abstract signal – referring to a topic, and evaluating the success or failure of communication. However, in existing works processes involved in these interactions are typically random choices, especially the choice of a communication topic. The introduction of active learning algorithms in these models produces significant improvement of the convergence process towards a shared vocabulary, with the speaker [7, 9, 1] or the hearer [10] actively controlling vocabulary growth. We study here how the convergence time and the maximum level of complexity scale with population size, for three different strategies (one with random topic choice and two with active topic choice) detailed in table 1. Both active strategies use a parameter (α and n), which is each time chosen optimal in our simulations. As for the version of the Naming Game used in our work, the scenario of the interaction is described in [10]. Vocabulary is updated as described in the Minimal Naming Game, detailed in [14]. In our simulations, we choose to set N = M = W , where N is the population size, M the number of meanings, and W the number of possible words. The computed theoretical success ratio of communication is used to represent the degree of convergence toward a shared lexicon for the whole population. A value of 1 means that the population reached full convergence. Complexity level of an individual lexicon is measured as the total number of distinct associations between meanings and words in the lexicon, or in other words: memory usage. We show here (see figures 2,3) that convergence time and maximum complexity are reduced with active topic choice, a behavior that is amplified as larger populations are considered. The minimal counts strategy yields a strictly minimum complexity (equal to the complexity of a completed lexicon), while converging as fast as the success threshold strategy. Further work will deal with other variants of the Naming Game (with different vocabulary update, population replacement, and different ratio for N , M and W). For the moment only the hearer's choice scenario is studied, because of its high robustness to changes in parameter values for the different strategies [10].