A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training
arXiv:2607.04574v1 Announce Type: cross Abstract: For LLM agents, supervised fine-tuning is not only about teacher labels' quality, but also about which interaction contexts those labels condition on. Pure behavioral cloning uses full teacher demonstrations, creating a mismatch between teacher-induced contexts seen in training and student-induced contexts encountered at test time. Recent work addresses this mismatch by querying a teacher at contexts reached by the student, often with increasingly elaborate filtering of the teacher's continuations. We instead frame on-policy data construction a
The proliferation of LLM agents and the demand for more robust and cost-effective training methods are driving innovation in data augmentation techniques.
Improving the efficiency and effectiveness of agent training directly impacts the scalability and real-world applicability of AI agents, potentially accelerating their deployment.
The proposed on-policy data augmentation method offers a more cost-efficient way to train LLM agents, addressing a key challenge in current supervised fine-tuning approaches.
- · AI agent developers
- · Companies deploying LLM agents
- · Reinforcement learning researchers
- · Inefficient data annotation services
- · Organizations relying solely on pure behavioral cloning
Faster and cheaper development cycles for sophisticated AI agents due to improved data efficiency.
Increased complexity and capability of AI agents as the cost of reaching high performance decreases.
Broader adoption of AI agents across industries, leading to significant automation of white-collar tasks.
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Read at arXiv cs.AI