When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning
2026-07-31 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors study how teaching a computer to imitate experts can be improved. They find that asking experts for help during the learning process lets the computer focus on understanding the expert's value of actions, not every detail of the expert's behavior. They introduce a new method called OVI that works well when the learner can estimate these values and is efficient to run. Their results also show that learning without expert interaction needs much more complex models. Experiments confirm OVI is better than other methods, especially when the learning model is simpler than the expert's.
Imitation LearningBehavior CloningValue FunctionPolicyExpert InteractionDAggerOffline LearningOn-policy LearningStatistical EfficiencyComputational Efficiency
Authors
Luca Viano, Antoine Moulin, Audrey Huang, Volkan Cevher, Philip Amortila, Dylan J. Foster
Abstract
Imitation learning (IL)---training an agent to replicate expert behavior from demonstrations---underpins applications from robotics to language model training. Standard approaches such as Behavior Cloning (BC) are known to suffer from compounding errors and performance plateaus, particularly when the learner cannot perfectly represent the expert's policy (as is typical, e.g., in distillation). Two interventions are widely understood empirically to improve performance: querying the expert interactively along the learner's own trajectories, and using value function estimation en route to generating a policy rather than directly fitting the expert's full action distribution. We investigate the nature of these improvements and their potentially surprising interplay. Our main finding is that expert interaction relaxes the representational demands on the learner: one only needs a model capable of realizing the expert's value function, bypassing the (often stricter) requirement of realizing the expert's policy itself. Concretely, we introduce OVI, an interactive on-policy IL algorithm that is statistically efficient whenever the learner can represent the expert's value function and computationally efficient given access to a linear maximization oracle. We complement this with a negative result showing that interaction is necessary. Namely, without stronger assumptions beyond expert-value realizability alone, any offline IL algorithm must scale with the complexity of the expert policy class. Our findings bear out empirically. OVI outperforms offline policy-based (BC), interactive policy-based (DAgger), and offline value-based IL methods, with the largest gains when the learner network is substantially less expressive than the expert's.