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This paper introduces DeepSearch-World, a deterministic and verifiable environment designed to train tool-use agents through self-distillation, addressing the limitations of traditional supervised fine-tuning and sparse-reward reinforcement learning. The framework, DeepSearch-Evolve, enables agents to iteratively generate and refine their own training trajectories, leading to significant improvements in performance on multi-hop question answering tasks without relying on external teacher models. Key results demonstrate that the DeepSearch-World-9B agent achieves competitive scores on various benchmarks, highlighting the potential for scalable self-evolution in long-horizon web agents.
Verifiable environments can empower web agents to self-evolve, achieving competitive performance without the need for external teacher models.
Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and supports key agentic cognitive behaviors useful for self-evolving, including progress verification, grounded reflection, and failure recovery. DeepSearch-Evolve iteratively performs trajectory generation, filtering, data mixing, and fine-tuning to train stronger agents. Without distillation from more capable models, DeepSearch-World-9B achieves competitive performance compared with open-source agents, reaching 31.2% on BrowseComp, 61.5% on GAIA, and 93.4% on HotpotQA, showing that verifiable environments enable scalable self-evolution for long-horizon web agents. We will release the environment, 420K training pool, validation set, model, and code to facilitate future research on self-improving deep search agents.
