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

TL;DR

Dr. Zero introduces a data-free self-evolving framework for search agents, enabling autonomous question generation and solving, which improves reasoning without training data, using a novel feedback loop and HRPO for efficiency.

Contribution

The paper presents Dr. Zero, a novel self-evolution framework with a feedback loop and HRPO, allowing search agents to improve reasoning without training data.

Findings

Matches or surpasses supervised agents in reasoning tasks

Reduces compute requirements significantly

Demonstrates emergence of complex reasoning through self-evolution

Abstract

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.