healthbench

Rubricon is a pipeline from Handshake AI that automatically generates and refines discriminative rubrics for LLM evaluation and post-training without human labels or fine-tuning, achieving 9% better alignment with expert rubrics than prior synthetic methods and 4.5% better HealthBench performance when training Qwen3.5-9B.

This paper introduces ConRub-Med, a reinforcement learning approach that uses consensus rubrics from multiple language models to reward open-ended medical question answering, achieving state-of-the-art results on several benchmarks including HealthBench-Hard.

This paper introduces POW3R, a policy-aware rubric reward framework for reinforcement learning with verifiable rewards (RLVR). It shows that static rubric aggregation misallocates learning signal, and POW3R achieves faster convergence and better performance across multiple settings.