This paper explores variants of ReactiveBuild, an algorithm that enables 3D, free-form, and environment-adaptive robot self-assembly. The most successful variant introduces a population of “arrogant” agents that ignore low-priority recruitment signals. This variant improved the simulated self-assembly of structures while also reducing the number of steps required for robots to create these structures. These results suggest that swarm heterogeneity can enhance the performance of self-assembly algorithms. Additionally, the paper discusses several variants that did not meaningfully improve algorithm performance but nonetheless provide interesting and useful lessons for robot self-assembly researchers. These variants were the introduction of hysteresis, varying the sensitivity of robots to force measurements with time, randomly moving in an incorrect direction, and probabilistic stopping. The limited impact to algorithm performance indicates that the ReactiveBuild algorithm is robust against robot hardware issues such as force sensor errors, environmental perception issues, and inconsistent communications, suggesting that the algorithm is well-suited to deployment on real robot hardware.