Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
arXiv:2606.30923v1 Announce Type: new Abstract: Imitation Learning is a natural framework for learning in sequential decision-making systems and has emerged as the dominant paradigm through which we understand language model training. A central puzzle is that, while in theory offline IL can be horizon-free and optimal, in practice online methods such as on-policy distillation often outperform offline methods such as supervised fine-tuning. We propose a noisy expert model to explain this gap, in which the learner only has access to a noisy version of the expert's policy, but wishes to compete a
This research addresses a persistent performance gap between theoretical offline learning and practical online methods in AI, particularly relevant as language models become central to decision-making systems.
Understanding the mechanisms behind superior online learning performance helps optimize AI training, leading to more robust and efficient autonomous systems and language models.
The proposed noisy expert model offers a theoretical explanation for the practical effectiveness of on-policy distillation, potentially guiding future AI research towards more effective training paradigms.
- · AI researchers
- · Generative AI developers
- · Robotics engineers
- · Software companies leveraging AI agents
- · Developers solely relying on naive offline imitation learning
Improved training methodologies for AI models, especially in complex sequential decision-making tasks.
Faster development and deployment of more capable AI agents across various industries, from autonomous vehicles to customer service.
Enhanced competition in AI development as more reliable and efficient training techniques become widely adopted, impacting the landscape of AI-driven products and services.
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Read at arXiv cs.LG