arXiv:2607.05804v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training. However, its application to long-horizon agentic tasks remains insufficiently explored. We identify two key inefficiencies in vanilla agent OPD: (1) full-horizon rollouts often waste wall-clock resources on tail turns that provide weak and noisy KL supervision, and (2) trajectory-level KL objectives concentrate most of the loss on shallow tokens, leaving deeper decision
The increasing complexity and length of tasks assigned to AI agents necessitate more efficient and scalable training methods.
Efficient training for long-horizon AI agents will accelerate their development and deployment in complex, real-world scenarios, collapsing more workflows.
This research outlines a method to significantly improve the efficiency and quality of on-policy distillation, making it more practical for training advanced language agents.
- · AI agent developers
- · Companies implementing AI agents
- · Cloud computing providers
- · Inefficient AI training methodologies
- · SaaS layers vulnerable to agentic automation
More capable and robust AI agents can be developed with fewer computational resources.
Accelerated AI agent deployment across industries, leading to increased automation of complex tasks.
Broader adoption of AI agents could further consolidate market power among platforms that can effectively leverage them, potentially impacting labor markets.
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Read at arXiv cs.AI