SFR-DeepResearch: Towards Effective Reinforcement Learning for
  Autonomously Reasoning Single Agents
Authors: name: Xuan-Phi Nguyen, name: Shrey Pandit, name: Revanth Gangi Reddy, name: Austin Xu, name: Silvio Savarese, name: Caiming Xiong, name: Shafiq Joty

Equipping large language models (LLMs) with complex, interleaved reasoning
and tool-use capabilities has become a key focus in agentic AI research,
especially with recent advances in reasoning-oriented (``thinking'') models.
Such capabilities are key to unlocking a number of important applications. One
such application is Deep Research (DR), which requires extensive search and
reasoning over many sources. Our work in this paper focuses on the development
of native Autonomous Single-Agent models for DR featuring minimal web crawling
and Python tool integration. Unlike multi-agent systems, where agents take up
pre-defined roles and are told what to do at each step in a static workflow, an
autonomous single-agent determines its next action dynamically based on
context, without manual directive. While prior work has proposed training
recipes for base or instruction-tuned LLMs, we focus on continual reinforcement
learning (RL) of reasoning-optimized models to further enhance agentic skills
while preserving reasoning ability. Towards this end, we propose a simple RL
recipe with entirely synthetic data, which we apply to various open-source
LLMs. Our best variant SFR-DR-20B achieves up to 28.7% on Humanity's Last Exam
benchmark. In addition, we conduct key analysis experiments to provide more
insights into our methodologies.