PokeeResearch: Effective Deep Research via Reinforcement Learning from
  AI Feedback and Robust Reasoning Scaffold
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.
68f6c9a224c44893631116d4
Yi Wan
68f6c9a224c44893631116d5
Jiuqi Wang
68f6c9a224c44893631116d6
Liam Li
68f6c9a224c44893631116d7
Jinsong Liu
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Ruihao Zhu
68f6c9a224c44893631116d9
Zheqing Zhu
PokeeResearch: Effective Deep Research via Reinforcement Learning from
  AI Feedback and Robust Reasoning Scaffold
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.
PokeeResearch-7B, a 7B-parameter deep research agent, achieves state-of-the-art performance using reinforcement learning and chain-of-thought reasoning to enhance robustness and alignment.
deep research agents reinforcement learning annotation-free Reinforcement Learning from AI Feedback (RLAIF) LLM-based reward signals chain-of-thought-driven reasoning self-verification adaptive recovery deep research benchmarks