RISE Enables Recursive LLM Improvement via Policy Distillation
September 3, 2026
RISE improves LLM post-training by constructing a synthetic teacher from a model's own RLVR training trajectories. It converts sparse parameter updates into dense, token-level targets by extrapolating displacements in parameter or logit space.
HOW THIS AFFECTS YOU
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builderThis technique can be used to enhance model performance through more efficient post-training loops.
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researcherThis method bypasses the need for external teachers by using self-extrapolating distillation.