On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning

• URL: http://arxiv.org/abs/2602.02762v1
• Date: Mon, 02 Feb 2026 20:13:43 GMT
• Title: On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
• Abstract summary: Semi-supervised imitation learning (SSIL) consists in learning a policy from action-labeled trajectories and a much larger dataset of action-free trajectories.<n>Some SSIL methods learn an inverse dynamics model (IDM) to predict the action from the current state and the next state.<n>An IDM can act as a policy when paired with a video model (VM-IDM) or as a label generator to perform behavior cloning on action-free data.
• Score: 8.802022729323635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised imitation learning (SSIL) consists in learning a policy from a small dataset of action-labeled trajectories and a much larger dataset of action-free trajectories. Some SSIL methods learn an inverse dynamics model (IDM) to predict the action from the current state and the next state. An IDM can act as a policy when paired with a video model (VM-IDM) or as a label generator to perform behavior cloning on action-free data (IDM labeling). In this work, we first show that VM-IDM and IDM labeling learn the same policy in a limit case, which we call the IDM-based policy. We then argue that the previously observed advantage of IDM-based policies over behavior cloning is due to the superior sample efficiency of IDM learning, which we attribute to two causes: (i) the ground-truth IDM tends to be contained in a lower complexity hypothesis class relative to the expert policy, and (ii) the ground-truth IDM is often less stochastic than the expert policy. We argue these claims based on insights from statistical learning theory and novel experiments, including a study of IDM-based policies using recent architectures for unified video-action prediction (UVA). Motivated by these insights, we finally propose an improved version of the existing LAPO algorithm for latent action policy learning.

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