Reinforcement Learning for View-Adaptive Distillation in 3D Gaussian Compression

3D Gaussian Splatting enables real-time novel view synthesis with high fidelity, yet its sparse and unorganized Gaussian anchors make compression challenging without harming structure and cross-view consistency. A key challenge is that effective compression depends not only on reducing parameter redundancy, but also on identifying where the compressed model is most fragile and how limited representation capacity should be allocated accordingly. We present an RL-guided distillation framework for 3DGS compression (RLGS). RLGS first uses a lightweight reinforcement learning policy to predict informative viewpoint offsets for adaptive distillation. It then distills a compact student 3DGS from a high-quality teacher under multi-view rendering supervision. Finally, it introduces an Unbalanced Optimal Transport based structural selection module to align teacher-student anchor distributions in voxelized space, thereby retaining the most informative anchors during pruning and allocation. Under the same bitrate constraints, our method consistently outperforms strong 3DGS compression baselines in rendering quality. At matched rendering quality, it further reduces model size while preserving structural fidelity, achieving up to about 15% additional size reduction at comparable rendering quality over prior methods.