arXiv:2606.30044v1 Announce Type: new Abstract: A key step toward artificial general intelligence is to train models that can perform multiple tasks. In this paper, we study how to build such models by first training separate RL experts for individual tasks and then consolidating them via distillation, as an alternative to directly training a single model on mixed tasks. We show that off-policy distillation degrades in multi-task settings due to the mode-covering nature of forward KL: aggregating data from multiple tasks introduces a large number of behavioral modes that can exceed the student
This research is emerging as AI development moves beyond single-task models and seeks more generalized capabilities, particularly with the rise of agentic systems.
Improving multi-task learning through distillation offers a potential pathway to more robust and scalable AI agents, crucial for commercial deployment and complex automation.
The proposed two-phase distillation method provides an alternative and potentially more effective approach to building multi-task models compared to direct multi-task training.
- · AI research labs
- · AI platform developers
- · Automation software companies
- · Companies relying on brittle single-task AI systems
More efficient and capable multi-task AI models become possible, accelerating agentic system development.
The improved efficiency of training could lower barriers to entry for developing complex AI agents, fostering more competition.
Generalized AI agents become more prevalent, impacting white-collar workflows and the demand for specialized SaaS solutions.
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Read at arXiv cs.LG