Developing vision-language models (VLMs) capable of understanding 3D scenes has been a longstanding research goal. Despite recent progress, 3D VLMs still struggle with spatial reasoning and robustness. We identify three key obstacles hindering their progress: (1) scene representation is constrained by a capacity-efficiency trade-off, which impedes scalable learning; (2) training data lacks a comprehensive scheme, with limited diversity across tasks and scene domains; and (3) models exhibit robustness deficiencies and lack effective post-training. To address these challenges, we first propose condensed feature grid (CFG), an efficient scene representation that significantly reduces token overhead while preserving strong perceptual capacity. Building on CFG, we introduce LEO-VL, a 3D VLM trained on over 700k 3D vision-language (3D-VL) data spanning four real-world indoor domains and five tasks such as captioning and dialogue. To further improve robustness, we propose SceneDPO, a novel post-training objective that incorporates contrastive signals across both answers and scenes. LEO-VL achieves state-of-the-art performance on various 3D-VL benchmarks, such as SQA3D, Beacon3D, and Scan2Cap. Extensive analyses highlight the efficiency of CFG and provide key insights such as the importance of task and scene diversity, the priority of data quality for effective scaling, and the advantages of SceneDPO.