arXiv:2605.28354v1 Announce Type: new Abstract: Training large language models as retrieval-augmented reasoning agents typically combines reinforcement learning with an SFT cold start distilled from a stronger model. However, this paradigm overlooks two fundamental factors: the dependency structure among sub-skills, and the possibility that distillation is not the only route to capability acquisition. We study this through Plan, a structured agentic behavior for multi-hop retrieval that decomposes a question into ordered sub-questions before any retrieval is performed, so that each search step
The rapid advancement of large language models is leading to increased research into agentic systems that can perform complex, multi-step tasks more efficiently and autonomously.
This research suggests a more effective paradigm for training AI agents, moving beyond simple distillation to methods that incorporate structured planning and dependency understanding, directly impacting the capabilities of future AI systems.
The approach to developing and training AI agents shifts from purely reinforcement learning and distillation to including explicit planning and decomposition of tasks, potentially yielding more robust and capable agents.
- · AI software developers
- · Companies implementing AI agents
- · Research institutions in AI
- · Legacy AI development methodologies
- · Companies relying on less structured AI agent approaches
More capable and reliable AI agents will emerge, able to tackle more complex real-world problems.
The cost and time required to develop and deploy highly autonomous AI systems could decrease significantly.
Wider adoption of advanced AI agents could accelerate automation in various white-collar industries, leading to significant shifts in workforce demands.
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Read at arXiv cs.AI