Despite strong performance in medical question answering, large language models remain limited in clinical cold-start settings, where little patient information is initially available and key evidence must be acquired through dialogue. This requires models to decide what to ask under a limited turn budget, balancing broad history taking with targeted clarification of high-risk cues. To address the aforementioned challenges, we propose **MedCIA**, a two-stage framework for improving clinical information acquisition. First, it reconstructs patient records into structured inquiry trajectories, enabling supervised learning of expert-guided information-seeking intents and the breadth-depth trade-off in clinical questioning. Second, it refines the policy with Adaptive Tree Policy Optimization (ATPO), an uncertainty-aware reinforcement learning procedure that allocates rollout computation to ambiguous dialogue states in a profile-anchored patient simulator. Furthermore, we curate a multi-turn dataset (MedIntake) for active clinical intake with progressive patient information disclosure and diverse inquiry strategies. Experiments on an EHR-derived benchmark show that **MedCIA** improves importance-weighted coverage, early acquisition, and cost control under controlled simulation.

Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot adapt to different instructions. This limits both interpretability and task sensitivity, especially for text-to-image generation and instruction-based image editing, where different inputs require different evaluation dimensions. We propose RubricRM, a pairwise generative reward modeling framework that first produces an input-specific rubric with evaluation dimensions, weights, and scoring criteria, and then applies the rubric to score candidate images. We train dedicated RubricRM models for text-to-image generation and image editing using a two-stage training pipeline: supervised fine-tuning teaches the model the rubric-based scoring paradigm, while GRPO further improves scoring through fine-grained dimension-level rewards. Experiments on multiple generation and editing benchmarks show that RubricRM outperforms existing specialized reward models and remains competitive with strong proprietary MLLM judges despite using smaller backbones. Our models, data, and code are available at https://github.com/zijiankan/RubricRM.