SkillReranker improves accuracy and cuts cost at the same time.

arXiv 2607.06283 attacks the problem that growing skill libraries make selection harder. It decomposes on both the task and skill side, builds a DAG with intermediate task states as nodes and candidate skills as edges, then cross-encodes over candidates per task interval. On ALFWorld and ScienceWorld across three backbones it improved task performance while reducing both environment steps and token consumption. Accuracy and cost usually trade against each other. When they move together, the previous approach was leaving something obvious on the table.