When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning
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 interac
Record details
Published: 31 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 3 August 2026
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ethics.ai (31 July 2026), “When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning,” evidence record 15759, https://ethics.ai/record/15759 (originally published by arXiv cs.AI).
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