Masked autoencoders (MAE) have become a dominant paradigm in 3D representation learning, setting new performance benchmarks across various downstream tasks. Existing methods with fixed mask ratios neglect multi-level representational correlations and intrinsic geometric structures, while relying on point-wise reconstruction assumptions that conflict with the diversity of point cloud. To address these issues, we propose a 3D representation learning method, termed Point-SRA, which aligns representations through self-distillation and probabilistic modeling. Specifically, we assign different masking ratios to the MAE to capture complementary geometric and semantic information, while the MeanFlow Transformer (MFT) leverages cross-modal conditional embeddings to enable diverse probabilistic reconstruction. Our analysis further reveals that representations at different time steps in MFT also exhibit complementarity. Therefore, a Dual Self-Representation Alignment mechanism is proposed at both the MAE and MFT levels. Finally, we design a Flow-Conditioned Fine-Tuning Architecture to fully exploit the point cloud distribution learned via MeanFlow. Point-SRA outperforms Point-MAE by 5.37% on ScanObjectNN. On intracranial aneurysm segmentation, it reaches 96.07% mean IoU for arteries and 86.87% for aneurysms. For 3D object detection, Point-SRA achieves 47.3% AP@50, surpassing MaskPoint by 5.12%.

The Mainstream 3D masked point modeling representation learning community typically employs predefined, fixed-ratio random or block masking strategies, aiming to obtain optimal representations and achieve high downstream performance. However, these empirical designs overlook the significant geometric information and structural importance differences that are inherent among different 3D points, leading to a suboptimal trade-off between the representation capture capabilities and reconstruction difficulty of such masking strategies. To address this issue, we are the first to present this decision-making problem to a reinforcement learning agent and propose a Reinforcement Masked Autoencoder for 3D representation learning, named Point-RMAE. Guided by geometric features as state factor, this method leverages the Masking Strategy Analyzer and the Dynamic Masking Generator to adaptively decide and apply the masking strategy during pretraining. The Masking Ratio Scheduling module dynamically adjusts the masking ratio based on the optimal strategy. Subsequently, the analyzer is updated by multiscale rewards derived from reconstruction quality level, distribution-aware feedback, and policy exploration. Notably, to enrich the Reward Function with distribution-aware signals and avoid decision collapse issue, we propose a Flow Matching Point Cloud Fast Generator that guides the selected masking decisions. Our method achieves outstanding performance across downstream tasks such as shape classification, medical diagnosis, object detection, action recognition, denoising and multiscale scene segmentation on ten popular 3D and 4D datasets. More importantly, Point-RMAE pioneers the application of reinforcement learning in 3D self-supervised representation learning.