We argue that robust alignment for non-verifiable tasks is fundamentally a principle generalization problem: reward should be guided by explicit, auditable principles rather than compressed into a learned scalar function. To operationalize this view, we present the Open Rubric System (OpenRS) , a modular, rubrics-based LLM-as-a-Judge framework built around Pairwise Adaptive Meta-Rubrics (PAMR) and lightweight Pointwise Verifiable Rubrics (PVRs). OpenRS is governed by an explicit meta-rubric—not a rubric itself, but a higher-level guideline that specifies how rubrics should be generated, which criteria to prioritize, and which failure modes are non-negotiable—and instantiates concrete adaptive rubrics on the fly by conditioning on the semantic differences between two candidate responses. It then performs criterion-wise pairwise comparisons and aggregates criterion-level preferences externally, avoiding pointwise weighted scalarization while improving discriminability in open-ended settings. Finally, we instantiate OpenRS as reward supervision in pairwise RL training. Empirically, OpenRS achieves state-of-the-art results on four reward-modeling benchmarks and yields stable gains when replacing scalar reward models in end-to-end RL.