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New ZEST-Search framework trains efficient AI search agents without labels

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ZEST-Search, a novel framework for training deep search agents that enhances both answer accuracy and tool efficiency without requiring labeled data. This method uses a heterogeneous co-evolutionary approach where an adaptive proposer constructs training tasks around the solver's current capabilities, while the solver learns to reason with external retrieval. ZEST-Search incorporates intermediate-difficulty and tool-aware rewards to encourage informative tasks and minimize redundant searches, demonstrating improved performance and efficiency across multiple question-answering benchmarks. AI

IMPACT This framework could lead to more efficient and capable AI search agents, reducing computational costs and improving performance in information retrieval tasks.

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

Read on arXiv cs.IR (Information Retrieval) →

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

New ZEST-Search framework trains efficient AI search agents without labels

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

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sheng-Jun Huang ·

    Heterogeneous Co-Evolution Learning for Tool-Efficient Search Agents with Zero Labels

    Deep search agents extend language models by interleaving reasoning with external retrieval. However, effective search-agent training requires addressing two challenges beyond final-answer optimization. First, the capability frontier of agents is largely determined by training qu…

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