Research
Enterprise Reinforcement Learning with Rubrics as Rewards
Many enterprise problems lack simple yes/no solutions, causing common AI training methods to fall short. Scale’s Rubrics as Rewards (RaR) method solves this by using a detailed, multi-faceted rubric for evaluation instead of a simple reward signal. This approach enables smaller, fine-tuned models to match or outperform much larger, general-purpose models on specialized tasks. For instance, on a legal analysis test set, a small Qwen3-4B model trained with RaR surpassed the performance of the much larger GPT-4.1. For enterprises, this translates directly to lower costs, more transparency, and tighter control, delivering superior performance on the complex workflows that matter most.
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