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

Researchers developed RISED, a method using rubric-based textual feedback to improve training of large language model agents across multiple interactive environments by guiding data selection and policy supervision. This approach enhances learning beyond scalar rewards, achieving top performance across diverse tasks.