Dr. Zero: Self-Evolving Search Agents without Training Data
As high-quality data becomes increasingly difficult to obtain, data-free self-evolution has emerged as a promising paradigm. This approach allows large language models (LLMs) to autonomously generate and solve complex problems, thereby improving their reasoning capabilities. However, multi-turn search agents struggle in data-free self-evolution due to the limited question diversity and the substantial compute required for multi-step reasoning and tool using. In this work, we introduce Dr. Zero, a framework enabling search agents to effectively self-evolve without any training data. In particular, we design a self-evolution feedback loop where a proposer generates diverse questions to train a solver initialized from the same base model. As the solver evolves, it incentivizes the proposer to produce increasingly difficult yet solvable tasks, thus establishing an automated curriculum to refine both agents. To enhance training efficiency, we also introduce hop-grouped relative policy optimization (HRPO). This method clusters structurally similar questions to construct group-level baselines, effectively minimizing the sampling overhead in evaluating each query's individual difficulty and solvability. Consequently, HRPO significantly reduces the compute requirements for solver training without compromising performance or stability. Extensive experiment results demonstrate that the data-free Dr. Zero matches or surpasses fully supervised search agents, proving that complex reasoning and search capabilities can emerge solely through self-evolution.
6965c753fc8c4ecc02c7f9c6
Zhenrui Yue
claimed_verified
2026-01-14T09:53:09.699Z
644c4eff9a7c19cfaf0c8f4a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/2gHtfhPFjSsnwxVtBnxe2.jpeg
Zhenrui Yue
yueeeeeeee2837
user
yueeeeeeee2837
6965c753fc8c4ecc02c7f9c7
Kartikeya Upasani
6965c753fc8c4ecc02c7f9c8
Xianjun Yang
6965c753fc8c4ecc02c7f9c9
Suyu Ge
6965c753fc8c4ecc02c7f9ca
Shaoliang Nie
6965c753fc8c4ecc02c7f9cb
Yuning Mao
6965c753fc8c4ecc02c7f9cc
Zhe Liu
6965c753fc8c4ecc02c7f9cd
Dong Wang
Dr. Zero: Self-Evolving Search Agents without Training Data
As high-quality data becomes increasingly difficult to obtain, data-free self-evolution has emerged as a promising paradigm. This approach allows large language models (LLMs) to autonomously generate and solve complex problems, thereby improving their reasoning capabilities. However, multi-turn search agents struggle in data-free self-evolution due to the limited question diversity and the substantial compute required for multi-step reasoning and tool using. In this work, we introduce Dr. Zero, a framework enabling search agents to effectively self-evolve without any training data. In particular, we design a self-evolution feedback loop where a proposer generates diverse questions to train a solver initialized from the same base model. As the solver evolves, it incentivizes the proposer to produce increasingly difficult yet solvable tasks, thus establishing an automated curriculum to refine both agents. To enhance training efficiency, we also introduce hop-grouped relative policy optimization (HRPO). This method clusters structurally similar questions to construct group-level baselines, effectively minimizing the sampling overhead in evaluating each query's individual difficulty and solvability. Consequently, HRPO significantly reduces the compute requirements for solver training without compromising performance or stability. Extensive experiment results demonstrate that the data-free Dr. Zero matches or surpasses fully supervised search agents, proving that complex reasoning and search capabilities can emerge solely through self-evolution.
A data-free self-evolution framework enables large language models to autonomously improve reasoning capabilities through iterative question generation and solving, achieving performance comparable to supervised methods.
large language models self-evolution search agents proposer solver automated curriculum hop-grouped relative policy optimization HRPO multi-turn reasoning tool using question diversity compute requirements training efficiency performance stability