Reinforcement learning is a powerful approach for improving language models, but applying it to open-ended domains such as healthcare remains challenging because high-quality responses are often multidimensional, context dependent, and difficult to capture with a single reward signal. Rubrics offer a natural form of supervision by providing a structured representation of the criteria that define a strong response. Existing rubric-based approaches, however, often rely on instance-specific criteria, creating two important limitations: (1) such rubrics are typically unavailable for unseen examples at test time; and (2) constructing new rubrics for every instance incurs substantial reward design costs. We introduce Health-SCORE, a scalable framework that captures recurring principles of response quality in the form of reusable meta-rubrics. Rather than relying on manually constructed instance-level criteria, Health-SCORE captures generalizable principles that can be applied across examples, tasks, and healthcare datasets. These meta-rubrics support two forms of model improvement: they can serve as structured, safety-aware reward signals for reinforcement learning during post-training, and they can be incorporated directly into prompts to guide model behavior at inference time. Across several open-ended healthcare benchmarks, models trained with Health-SCORE achieve performance competitive with models trained using human-authored instance-level rewards, while substantially reducing reward-design costs and improving generalizability.

On-policy distillation (OPD), which samples trajectories from the student model and supervises them with a teacher at the token-level, avoids relying solely on verifiable terminal rewards and can yield better generalization than off-policy distillation. However, OPD requires expensive on-the-fly sampling of the student policy during training, which substantially increases training cost, especially for responses with long reasoning traces. Our initial analysis shows that, during OPD, training signals are stronger in the prefix of each output reasoning trace, and that even a short teacher-generated prefix can significantly help the student produce the correct answer. Motivated by these observations, we propose a simple yet effective modification of OPD: we apply the distillation objective only to prefixes of student-generated outputs and terminate each sampling early during distillation. Experiments on a suite of AI-for-Math and out-of-domain reasoning benchmarks show that on-policy prefix distillation matches the performance of full OPD in long reasoning outputs while reducing training FLOP by 2x–40x.

Production text-to-code systems increasingly use LLMs to translate natural-language analytics requests into executable SQL and pandas code, serving both business users, who describe desired results in application terms, and technical users, who name tables and columns directly. Existing Text-to-SQL and Text-to-Python benchmarks provide coverage, but they do not isolate when SQL and pandas performance diverge. We introduce BIRD-Persona, a persona-stratified extension of BIRD-Python that adds novice/expert rewrites (business-style and technical-style phrasing) to tasks with matched SQL and pandas ground truth, plus schema variants and task-structure annotations. Across 1,534 tasks, nine models, and 245,592 evaluations, BIRD-Persona reproduces the previously reported SQL-over-Python gap: SQL outperforms pandas for eight of nine systems. The gap is not uniform: expert, schema-explicit prompts improve both paradigms but help SQL more, schema representations have mixed model-dependent effects, and selected-column burden widens the gap while subqueries do not. By making user phrasing a controlled benchmark dimension, BIRD-Persona provides a foundation for persona-aware and paradigm-aware evaluation of natural-language analytics systems.