RULER: Reasoning from Trials to Transferable Rules for Finite-Budget Online Algorithm Configuration

Online algorithm configuration adapts solver parameters to individual instances and evolving search states under a limited evaluation budget. Yet each trial offers more than a performance score: it reveals how a parameter edit affects solver behavior in a particular context. Turning these observations into transferable tuning knowledge requires reasoning about when an edit is applicable, what response it should induce, and whether new evidence supports that expectation. We introduce RULER—Rule-guided Updating through Local Experimental Reasoning, a framework that treats configuration trials as local experiments and distills their outcomes into reusable, falsifiable edit–response rules. RULER retrieves these rules as contextual hypotheses, reassesses their applicability to the current solver state. It then plans targeted experiments to test their predictions, refine promising edits, distinguish competing explanations, or explore alternatives when existing knowledge offers little guidance. This evidence–hypothesis–experiment loop enables RULER to improve the current configuration while accumulating tuning knowledge that can be reused across instances. Experiments across four problem–solver settings show that RULER reduces the reference gap by an average of 10.7% relative to the strongest baseline under matched evaluation budgets, maintains gains under matched wall-clock budgets and across planner scales, and transfers useful configuration knowledge to unseen instances.