PokeeResearch: Effective Deep Research via Reinforcement Learning from
  AI Feedback and Robust Reasoning Scaffold
Authors: name: Yi Wan; name: Jiuqi Wang; name: Liam Li; name: Jinsong Liu; name: Ruihao Zhu; name: Zheqing Zhu
Published: 2025-10-17 17:53:06

Abstract: Tool-augmented large language models (LLMs) are emerging as deep research
agents, systems that decompose complex queries, retrieve external evidence, and
synthesize grounded responses. Yet current agents remain limited by shallow
retrieval, weak alignment metrics, and brittle tool-use behavior. We introduce
PokeeResearch-7B, a 7B-parameter deep research agent built under a unified
reinforcement learning framework for robustness, alignment, and scalability.
PokeeResearch-7B is trained by an annotation-free Reinforcement Learning from
AI Feedback (RLAIF) framework to optimize policies using LLM-based reward
signals that capture factual accuracy, citation faithfulness, and instruction
adherence. A chain-of-thought-driven multi-call reasoning scaffold further
enhances robustness through self-verification and adaptive recovery from tool
failures. Among 10 popular deep research benchmarks, PokeeResearch-7B achieves
state-of-the-art performance among 7B-scale deep research agents. This
highlights that careful reinforcement learning and reasoning design can produce
efficient, resilient, and research-grade AI agents. The model and inference
code is open-sourced under MIT license at
https://github.com/Pokee-AI/PokeeResearchOSS.