arXiv:2604.14054v2 Announce Type: replace-cross Abstract: Deep search agents have emerged as a promising paradigm for addressing complex information-seeking tasks, but their training remains challenging due to sparse rewards, weak credit assignment, and limited labeled data. Self-play offers a scalable route to reduce data dependence, but conventional self-play optimizes students only through sparse outcome rewards, leading to low learning efficiency. In this work, we observe that self-play naturally produces a question construction path (QCP) during task generation, an intermediate artifact t
The increasing complexity of AI tasks and the limitations of traditional supervised learning are pushing researchers to develop more autonomous and data-efficient training methods.
This work addresses a core challenge in scaling AI capabilities by proposing a method for self-improving agents that requires less external data and human oversight, accelerating AI development.
The efficiency and scalability of training advanced AI agents for complex information-seeking tasks are improved, potentially leading to more robust and generalized AI systems.
- · AI research labs
- · Companies building AI agents
- · SaaS providers leveraging AI
- · Sectors requiring complex information processing
- · Data labeling companies (long term)
More capable and autonomous AI agents can be developed with less reliance on large, hand-labeled datasets.
This methodology could enable AI systems to acquire new skills and knowledge more independently, accelerating the development of general-purpose AI.
The reduced need for external data could democratize advanced AI development, making it accessible to organizations with fewer data resources.
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Read at arXiv cs.CL