Rubrics as Rewards: A New Paradigm for Training Reliable AI

AI models face significant challenges when applied to nuanced, high-stakes fields like medicine and science. Standard training techniques, such as Reinforcement Learning from Human Feedback (RLHF), often struggle to ensure models develop true subject-matter understanding. Scale AI’s Rubrics as Rewards (RaR) introduces a promising new framework that guides language models using detailed, criteria-driven rubrics rather than vague or preference-based signals.

Understanding the Limitations of Traditional Methods

While RLHF refines AI behavior through subjective human preferences, it tends to produce opaque reward systems. This can lead models to prioritize surface-level persuasiveness over genuine comprehension. Reinforcement Learning with Verifiable Rewards (RLVR), on the other hand, is effective for objective domains but falls short in subjective, complex fields. RaR addresses these gaps by combining the strengths of both approaches and extending verifiable reward concepts to subjective, real-world challenges.

The Mechanics of Rubrics as Rewards

The core of RaR is a meticulously designed rubric evaluated against four guiding principles:

• Expert-Guided: Reference answers from domain experts or robust models inform the rubric’s content.
  
  
• Comprehensive: Rubrics assess multiple dimensions, like factual accuracy and logical coherence, while penalizing errors.
  
  
• Semantic Weighting: Each rubric criterion is prioritized, with labels such as “Essential” or “Important.”
  
  
• Self-Contained: Items are crafted for independent evaluation, requiring no extra context or specialized knowledge.

Models generate responses, receive immediate feedback via the rubric, and iteratively improve through the GRPO algorithm. This process can be further optimized using adaptive guidance for faster, more efficient learning cycles.

Turning Rubrics into Actionable Feedback

Researchers explored two strategies to convert rubric scores into a single reward signal:

• Explicit Aggregation: A checklist approach, scoring each criterion individually.
• Implicit Aggregation: An AI judge holistically reviews both the answer and rubric to assign an overall score.

Implicit aggregation emerged as the superior method, capturing the nuanced, expert-like judgment necessary for complex domains, and outperforming rigid checklists.

Proving the Framework: Medical and Scientific Applications

To validate RaR, researchers created two demanding datasets, RaR-Medicine-20k for medical reasoning and RaR-Science-20k for scientific problem-solving. Each dataset features expert-level prompts requiring deep reasoning, demonstrating the framework’s value in real-world, consequential settings.

Key Outcomes and Insights

When tested on OpenAI’s HealthBench-1k, RaR with implicit aggregation delivered up to a 28% improvement over methods using simple Likert scale rewards. It not only matched but often surpassed approaches based on expert reference answers. Structured rubrics also enabled smaller, cost-effective AI judges to evaluate model outputs with accuracy rivaling much larger systems benefiting enterprise scalability.

Crucially, rubrics crafted by human experts led to better model performance than those generated solely by AI. This finding underscores the indispensable role of human judgment in setting high-quality evaluation standards and ensures that expert knowledge is distilled and amplified, not replaced.

Implications for Safer, More Transparent AI

By anchoring model training in explicit rubrics, RaR enhances transparency, interpretability, and resistance to reward hacking where models might otherwise exploit loopholes in ambiguous signals. This explicitness is essential for teaching AI to handle complex, multi-step tasks that otherwise provide sparse or inadequate feedback.

Rubrics as Rewards signals a substantial leap forward in AI post-training. It empowers experts to shape evaluation criteria, enabling AI to reason more reliably and safely in areas where quality matters most. The approach sets the stage for scalable, transparent, and trustworthy AI deployments in the real world.

Source: Scale AI Blog