arXiv:2607.04763v1 Announce Type: cross Abstract: We study on-policy distillation (OPD) for agentic tasks, where an LLM agent interacts with an environment over multiple turns and a student imitates a teacher over these multi-turn interaction histories. Fully online OPD is costly because each update requires fresh student rollouts through the environment and teacher queries at visited histories. We propose Replayed-Prefix On-Policy Distillation (ReOPD), an off-environment alternative that reuses pre-collected teacher trajectories as replayed prefixes: the student acts at selected steps, while
The rapid advancement of LLMs and agentic systems necessitates more efficient and cost-effective training methods to scale their deployment.
This development addresses critical computational and environmental costs associated with training sophisticated AI agents, making their widespread adoption more feasible.
Training AI agents, especially for multi-turn interactions, can now be significantly more efficient, reducing the need for constant fresh environment rollouts and teacher queries.
- · AI developers
- · Cloud computing providers (reduced cost for users)
- · Enterprises adopting AI agents
- · Companies with inefficient AI training pipelines
- · Energy grids (due to potential for increased training volume at lower cost)
More complex and capable AI agents can be developed and deployed faster due to reduced training overhead.
This efficiency could accelerate the integration of AI agents into various white-collar workflows, leading to broader automation.
The reduced cost of agent training might lower barriers to entry for AI development, fostering innovation and potentially increasing global AI competition.
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Read at arXiv cs.AI