这篇论文把模仿学习里交互到底帮不帮忙讲清楚了，还给了能跑的OVI算法，做RL或蒸馏的值得看一眼。
该论文研究专家交互在模仿学习中的作用，发现交互可放宽对学习者模型的表征要求，只需实现专家价值函数而非完整策略。作者提出OVI算法，在能表示专家价值函数时统计高效，并借助线性最大化预言机实现计算高效。负向结果表明，在仅具备价值可实现性假设下，离线模仿学习算法必须依赖策略类复杂度。实验显示OVI优于BC、DAgger及离线价值法，在模型表达能力弱于专家时增益最大。
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 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.