Large Language Model's Multi-Capability Alignment in Biomedical Domain

BalancedBio, a theoretically-grounded framework for parameter-efficient biomedical reasoning that addresses the fundamental challenge of multi-capability integration in domain-specific AI alignment. We establish the Biomedical Multi-Capability Convergence Theorem, proving that balanced development of domain expertise, reasoning, and instruction-following requires orthogonal gradient spaces to prevent capability interference--a critical requirement for safe biomedical AI deployment. Our approach introduces two key innovations: (1) Medical Knowledge-Grounded Synthetic Generation (MKGSG), which extends Source2Synth by incorporating clinical workflow constraints and medical ontology validation to ensure both factual accuracy and clinical safety; and (2) Capability-A ware Group Relative Policy Optimization, where we theoretically derive optimal hybrid reward weighting strategies that maintain capability orthogonality during reinforcement learning, incorporating a reward model that scores business data adapted to biomedical downstream tasks, achieving true multi-dimensional hybrid RL with both rule-based and model-based scores . Through rigorous mathematical analysis, we prove that our training objective achieves Pareto-optimal convergence where improvements in one capability domain preserve performance in others--addressing a fundamental alignment challenge in medical AI. BalancedBio demonstrates state-of-the-art performance within its parameter class: domain expertise (80.95% BIOMED-MMLU, +15.32% over best baseline), reasoning capabilities (61.94%, +7.75%), instruction-following (67.95%, +6.44%), and integration score (86.7%, +18.5%). Critically, we provide theoretical safety guarantees with formal bounds on capability preservation and clinical accuracy maintenance. Real-world deployment across healthcare institutions validates practical impact: 78% cost reduction, 23% improved diagnostic accuracy, and 89% clinician acceptance rate. Our work establishes a principled methodology for biomedical AI alignment, demonstrating that sophisticated reasoning capabilities can be achieved efficiently while maintaining safety and reliability constraints essential for medical applications. We will release the 0.5B V ersion of our model.