KETCHUP: K-Step Return Estimation for Sequential Knowledge Distillation

Name: KETCHUP: K-Step Return Estimation for Sequential Knowledge Distillation
Uploaded: 2026-10-22T04:48:27.212339+00:00
Duration: 1011 s
Description: KETCHUP is a new method for Reinforcement Learning knowledge distillation that uses a K-step return estimation based on the Bellman Optimality Equation. It extends standard RL to look K steps ahead, which significantly reduces gradient variance and stabilizes training. This leads to better performance in text generation tasks compared to existing methods like LLMR, especially for larger models.

KETCHUP is a new method for Reinforcement Learning knowledge distillation that uses a K-step return estimation based on the Bellman Optimality Equation. It extends standard RL to look K steps ahead, which significantly reduces gradient variance and stabilizes training. This leads to better performance in text generation tasks compared to existing methods like LLMR, especially for larger models.

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