Dynamics Distillation for Efficient and Transferable Control Learning

• URL: http://arxiv.org/abs/2605.01516v1
• Date: Sat, 02 May 2026 16:08:26 GMT
• Title: Dynamics Distillation for Efficient and Transferable Control Learning
• Abstract summary: We introduce Sim2Sim2Sim, a framework that bridges high-fidelity vehicle simulation and scalable reinforcement learning.<n>We demonstrate more efficient policy optimization and reliable transfer under challenging dynamics.
• Score: 24.318649378170075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulators provide only in isolation. We introduce Sim2Sim2Sim, a framework that bridges high-fidelity vehicle simulation and scalable reinforcement learning by distilling simulator dynamics into a highly parallelizable learned dynamics model. By training control policies purely within this distilled environment and deploying them back into the high-fidelity source simulator, we demonstrate more efficient policy optimization and reliable transfer under challenging dynamics. We further show that predictive accuracy alone does not fully characterize a learned dynamics model's suitability as a reinforcement learning training environment, which should also be assessed by the quality of the policies it enables.

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