RISED: Rubrics for Agentic Multi-Environment Selection and Self-Distillation

RISED introduces a rubric-based approach for agentic multi-environment training. It uses an LLM judge to tag rollouts with a shared rubric vocabulary, enabling data selection based on behavioral composition and limiting overlap. Positive rubrics facilitate self-distillation for token-level supervision, and negative rubrics help avoid failure modes. This method improves agent performance and provides behavioral insights.