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DeepSearch-Evolve framework trains web agents via self-distillation in verifiable environment

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced DeepSearch-Evolve, a self-distillation framework designed to train web agents. This framework utilizes DeepSearch-World, a verifiable environment containing 420,000 multi-hop question-answering tasks. The system supports agentic behaviors like progress verification and failure recovery, enabling agents to improve from their own experiences without relying on external models. The DeepSearch-World-9B model, trained using this method, has demonstrated competitive performance on benchmarks such as BrowseComp and HotpotQA. AI

IMPACT Enables scalable self-evolution for long-horizon web agents, potentially improving their performance on complex tasks.

RANK_REASON The cluster describes a new research paper detailing a novel framework and environment for training AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepSearch-Evolve framework trains web agents via self-distillation in verifiable environment

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The cluster describes a new research paper detailing a novel framework and environment for training AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment

    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-E…

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