Stay True to Your Roots: Reference-Anchored Policy Optimization for Faithful GEO

Generative search is redefining web visibility: being retrieved no longer suffices if a source goes unrepresented in the LLM-generated answer. This creates a new optimization problem for content creators—improving visibility among competing sources without compromising faithfulness to the original content. Existing generative engine optimization (GEO) approaches largely optimize visibility in isolation, neglecting faithfulness, and rely on manually designed edit instructions with limited coverage. We introduce Reference-Anchored Policy Optimization (RAPO), a reinforcement learning framework that generates multiple candidate rewrites but measures progress against the original source rather than other sampled rewrites. RAPO anchors advantage estimation to the source reward, so positive signals reflect improvement over the original content, while retaining within-group standard-deviation normalization for stable scaling. To capture the competitive nature of generative search, we introduce a competition-aware visibility signal that evaluates a source against its strongest competitor and combine it with faithfulness in a composite reward. Across GEO-Bench, Researchy-GEO, and E-commerce, RAPO improves Overall visibility over the source by 9.34, 15.82, and 7.15 points with better faithfulness, outperforming prompt-based and trainable RL baselines. Ablations and analysis show source anchoring is essential, RAPO generalizes across rewriter model scales and generative engines, and benefits sources with lower rank in LLM context most, while varying the faithfulness weight reveals a controllable visibility–faithfulness trade-off. Code is released at https://anonymous.4open.science/r/RAPO-4190/README.md.