Rethinking Large Language Model Distillation: A Constrained Markov Decision Process Perspective

ICLR 2026 Conference Submission19970 Authors

Keywords: distillation, reinforcement learning, large language models, constrained reinforcement learning

TL;DR: We use Constrained MDPs to achieve principled and efficient Large Language Model distillation, effectively balancing performance and constraints.

Abstract:

We introduce a novel approach to large language model (LLM) distillation by formulating it as a constrained reinforcement learning problem. While recent work has begun exploring the integration of task-specific rewards into distillation processes, existing methods typically rely on ad-hoc reward weighting.
We propose a principled optimization framework that maximizes task-specific rewards while constraining the divergence from the teacher model to remain below a specified threshold. Our approach adapts constrained state augmented reinforcement learning to the distillation setting, introducing a modified reward function that maintains theoretical guarantees of constraint satisfaction without requiring state augmentation or teacher model access during deployment and without the computational overhead of the dual Lagrangian methods. Through extensive experiments on mathematical reasoning tasks, we demonstrate that our method achieves better constraint satisfaction rates and better reasoning compared to the soft Lagrangian relaxation baselines while maintaining competitive task performance. Our framework provides a theoretically grounded and practically efficient solution for reward-aware distillation in resource-constrained settings.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 19970