PulseAugur
EN
LIVE 05:50:51

Dr. Zero: LLM search agents self-evolve without training data

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Dr. Zero, a novel framework designed to enable large language model (LLM) search agents to self-evolve without relying on curated training data. This approach utilizes an external search engine as the agent's knowledge environment and incorporates a self-evolution feedback loop where a proposer agent generates diverse questions to train a solver agent. To improve training efficiency, the framework employs hop-grouped relative policy optimization (HRPO), which clusters similar questions to reduce computational overhead. Experiments show that Dr. Zero can match or exceed the performance of supervised search agents on question-answering benchmarks, demonstrating the potential for strong agentic search and reasoning to emerge solely through self-evolution. AI

IMPACT This research could significantly reduce the need for large, curated datasets in training AI agents, potentially accelerating development and deployment.

RANK_REASON Academic paper detailing a new AI framework and method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

Dr. Zero: LLM search agents self-evolve without training data

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhenrui Yue, Kartikeya Upasani, Xianjun Yang, Suyu Ge, Shaoliang Nie, Yuning Mao, Zhe Liu, Dong Wang ·

    Dr. Zero: Self-Evolving Search Agents without Training Data

    arXiv:2601.07055v2 Announce Type: replace Abstract: As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm. This approach allows large language models (LLMs) to autonomously generate and solv…

RELATED ENTITIES

RELATED TOPICS