Is One Step Enough for Offline Policy Improvement?

Behavior regularization in offline reinforcement learning limits the exploitation of critic errors, but strong anchoring can also restrict policy improvement. We study how policy improvement is composed through multi-step proximal policy improvement (MPI), which re-centers each proximal objective on the preceding policy. We parameterize the procedure by a nominal total horizon $T$ and $K$ stages with local horizon $T/K$, distinguishing subdivision at a fixed total horizon from additional refinement at a common local horizon. Our analysis shows that sequential re-centering can reach endpoints unavailable to any single proximal step and characterizes how subdivision reduces the leading local discretization error of ideal updates under a fixed critic. We consider TD3+BC and IQL-based policy extraction to examine how improvement composition interacts with actor objectives and policy geometry. TD3+BC experiments on D4RL locomotion suggest that subdivision can broaden the range of useful total horizons, while adding refinement stages at a fixed small local horizon can improve return. The results identify improvement composition as a design choice alongside regularization strength, with distinct effects from horizon subdivision and additional policy extraction.