Mitigating False Credit Propagation: Probabilistic Graphical Reward Aggregation for Rubric-Based Reinforcement Learning
For practitioners using rubric-based rewards in open-ended language model post-training, this work addresses a structural flaw in reward aggregation that can lead to incorrect credit assignment.
The paper identifies False Credit Propagation (FCP) in rubric-based reinforcement learning, where criterion-level rewards are incorrectly aggregated due to ignored dependency relations. The proposed GEAR framework models these dependencies using probabilistic graphical models, achieving up to 15.5% relative improvement over flat aggregation and reducing FCP leakage by 96.5%.
Rubric-based rewards are increasingly used for open-ended language model post-training, but criterion-level scores are often aggregated as independent utilities. This flat scalarization ignores rubric-specified prerequisite and activation relations among criteria, allowing reward or penalty to be counted even when the condition that licenses it is absent. We call this structural reward-aggregation failure \textbf{False Credit Propagation} (FCP). To address this limitation, we propose \ourname (\textbf{G}raphical \textbf{E}vent \textbf{A}ggregation for \textbf{R}ubric rewards), a probabilistic graphical framework for dependency-aware rubric aggregation. \ourname models each criterion outcome as a latent Bernoulli event in a typed rubric graph, propagates soft suppression from unsupported parent events to their children, and aggregates the resulting event probabilities into a normalized expected signed utility. This yields a linear-time reward computation that can be plugged into standard rubric-based RL pipelines without changing the outer optimization algorithm. Experiments on HealthBench, WritingBench, and PLawBench with two policy backbones show that \ourname consistently improves over flat aggregation and deterministic gating, achieving relative gains of up to 15.5\% over flat aggregation. FCP diagnostics further show that \ourname reduces leakage by 96.5\% relative to flat aggregation while preserving more licensed downstream utility than deterministic gating. Our code is publicly available at https://github.com/LvCan926/GEAR.