Apple Researchers Propose RISED for Agentic Multi-Environment Selection and Self-Distillation
Apple researchers introduced RISED, a method for training a single LLM agent across diverse interactive environments. It uses rubrics to select prompt groups based on relationships between rollouts across environments, rather than environment-level or local reward signals, and applies self-distillation to address all-failure and all-success rollout groups that lack group-relative reward signals.
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