Papers by Michael Bowling
KETCHUP: K-Step Return Estimation for Sequential Knowledge Distillation (2026.findings-eacl)
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| Challenge: | Empirical evaluation shows that our approach yields superior performance in both standard task metrics and large language model (LLM)-based evaluation. |
| Approach: | They propose a K-step return estimation method for reinforcement learning (RL)-based knowledge distillation in text generation tasks using the Bellman Optimality Equation. |
| Outcome: | The proposed method performs better on standard task metrics and large language model evaluations on three text generation tasks. |