SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning

Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of informative gradients in sparse settings, where standard reward models often yield flat landscapes that struggle to guide planning. To address this challenge, we propose S haping L andscapes with O ptimistic P otential E stimates ( SLOPE ), a novel framework that shifts reward modeling from predicting sparse scalars to constructing informative potential landscapes. SLOPE employs optimistic distributional regression to estimate high-confidence upper bounds, which amplifies rare success signals and ensures sufficient exploration gradients. Evaluations on 30+ tasks across 5 benchmarks and real-world robotic deployments , demonstrate that SLOPE consistently outperforms leading baselines in fully sparse , semi-sparse , and dense rewards. The project code is available at https://anonymous.4open.science/r/SLOPE-CF13.