RISED Optimizes Agent Training via Multi-Environment Self-Distillation
October 5, 2026
RISED introduces rubrics for selecting and distilling agentic data across diverse interactive environments. It moves beyond simple reward-based selection by analyzing relationships between rollouts to solve the problem of unbalanced success/failure groups in training batches.
HOW THIS AFFECTS YOU
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builderYou can improve agent robustness by more effectively managing diverse training data distributions.
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researcherThis method offers a more nuanced approach to curriculum learning for generalist agents.