SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents
- Published
- Source
- arXiv
- Paper number
- 078
- Field
- Research Agents / RL
- arXiv ID
- 2509.06283
Key points
- It equips large language models, or LLMs, with advanced reasoning and tool-use capabilities so they can carry out complex, long-horizon tasks while mitigating issues such as hallucination.
- It develops an effective autonomous single-agent system for deep research that works without rigid multi-agent orchestration and generalizes better.
- It stabilizes reinforcement learning, or RL, for multi-turn agentic tasks, where existing methods struggle with stability and can degrade the base model's reasoning ability.
- It develops an agentic reasoning scaffold for a specific LLM family with the minimal set of three tools, Internet search, page viewing, and a code interpreter, and an iterative single-turn workflow.
- It builds a new synthetic data generation pipeline that produces difficult multi-hop QA and long-form report-writing tasks for agentic training.
- It implements an end-to-end RL recipe with a length-normalized REINFORCE objective, trajectory filtering, and partial rollouts to ensure stable policy optimization over multi-turn interactions.
Paper links
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