Model Card for PokeeResearch

Model Details

Model Description

PokeeResearch-7B is a 7-billion-parameter deep research agent developed by Pokee AI to advance reliable, aligned, and scalable research-grade reasoning in tool-augmented LLMs.
The model integrates Reinforcement Learning from AI Feedback (RLAIF) with a robust reasoning scaffold, enabling it to conduct complex, multi-step research workflows that include self-correction, verification, and synthesis across multiple independent research threads.

• Developed by: Pokee AI
• Model type: Tool-augmented large language model (LLM) research agent
• Language(s): English, Chinese and many more
• License: Apache 2.0
• Finetuned from model: Qwen2.5-7B-Instruct

Model Sources

• Repository: https://github.com/Pokee-AI/PokeeResearchOSS
• Paper: PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold, Pokee AI, October 2025
• Project Page: https://pokee.ai/deepresearch-preview

Uses

Direct Use

PokeeResearch-7B is designed for deep research automation, where the model autonomously:

• Decomposes complex user queries
• Retrieves and reads from external sources
• Synthesizes factual, verifiable, and grounded answers

It can be used as a standalone research assistant or integrated into multi-agent systems to support academic, enterprise, or product-level research tasks.

Downstream Use

PokeeResearch-7B can be fine-tuned or extended for:

• Domain-specific scientific discovery
• Autonomous document retrieval and synthesis
• Multi-source verification and summarization pipelines
• Integration into reinforcement learning research agents (RLHF/RLAIF frameworks)

Out-of-Scope Use

The model should not be used for:

• Generating unverified or speculative claims
• Automated decision-making in high-stakes domains (medical, legal, or financial)
• Applications requiring strict factual precision without external verification
• Generating content without citation or evidence tracing

Bias, Risks, and Limitations

PokeeResearch-7B is optimized for factual grounding and robustness, but limitations include:

• Dependence on external data quality and retrieval accuracy
• Potential semantic bias introduced by AI-based feedback signals
• Limited coverage for non-English or multi-modal reasoning tasks
• Risk of hallucinated synthesis when sources conflict or lack clarity

Recommendations

Users should:

• Cross-verify answers, especially in multi-hop reasoning cases
• Monitor output for citation accuracy and alignment with source data
• Refrain from using outputs as sole evidence in decision-critical contexts

How to Get Started with the Model

please refer to the following codebase for how to use PokeeResearch-7B https://github.com/Pokee-AI/PokeeResearchOSS/blob/main/README.md

Training Details

Training Data

• Dataset: MiroRL-GenQA dataset (MiroMind AI, 2025)
• Data characteristics: Complex, multi-turn question–answer pairs requiring multi-step reasoning
• Data filtering: No benchmark data used for testing; the model was trained only on open-domain text Q&A samples

Training Procedure

Preprocessing

• Normalization and tokenization aligned with Qwen2.5 tokenizer
• Structured prompt–response pairs in research/verification format (<tool_call>, <answer>, <verification>)

Training Hyperparameters

• Algorithm: RLOO (REINFORCE Leave-One-Out)
• Batch size: 64
• Research threads per prompt: 8
• Learning rate: 3e-6
• Context limit: 32,768 tokens
• Steps: 140 fine-tuning iterations
• Regularization: None (no entropy or KL regularization)
• Precision regime: bf16 mixed precision

Reward Design

• Combined reward signal from:
  • AI feedback (semantic equivalence via external LLM judge)
  • Format adherence reward (ensures correct agent behavior)

Speeds, Sizes, Times

• Model size: 7 billion parameters
• Training duration: ~5 days on 8 × A100 80G GPUs
• Checkpoint size: ~13 GB

Evaluation

Testing Data, Factors & Metrics

Testing Data

10 open-domain research and QA benchmarks:

• NQ, TriviaQA, PopQA, HotpotQA, 2WikiMultiHopQA, Musique, Bamboogle, GAIA, BrowseComp, Humanity’s Last Exam

Factors

• Benchmarks differ by reasoning depth, retrieval dependence, and factual precision requirements.
• Evaluations disaggregate by dataset difficulty and task type (single-hop vs multi-hop).

Metrics

• Mean accuracy (mean@4 across independent research threads) based on

Results

PokeeResearch-7B (RTS variant) and PokeeResearch-7B outperforms all baselines at 7B scale across 10 benchmarks.
Highlights (mean@4 accuracy):

Summary

PokeeResearch-7B variants achieves state-of-the-art performance among 7B-scale open deep research agents, validating RLAIF and reasoning scaffold design for robust, verifiable research workflows.

Technical Specifications

Model Architecture and Objective

• Base Architecture: Transformer decoder (Qwen2.5-7B-Instruct backbone)
• Objective: Reinforcement learning with AI feedback to maximize semantic correctness and alignment with human-style reasoning

Compute Infrastructure

Hardware

• NVIDIA A100 80GB GPUs ×8 for training and x1 for inference

Citation

BibTeX:

@article{pokee2025deepresearch,
  title={PokeeResearch: Effective Deep Research via
          Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold},
  author={Yi Wan* and Jiuqi Wang* and Liam Li
          and Jinsong Liu and Ruihao Zhu and Zheqing Zhu},
  journal={Pokee AI Technical Report},
  year={2025},
  url={https://arxiv.org/pdf/2510.15862}
}

APA: Wan, Y., Wang, J., Li, L., Liu, J., Zhu, R., & Zhu, Z. (2025). PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold. Pokee AI.

Glossary

• RLAIF: Reinforcement Learning from AI Feedback – optimization using LLM-based reward signals.
• RLOO: REINFORCE Leave-One-Out – unbiased policy gradient variant for on-policy learning.
• RTS: Research Threads Synthesis – synthesis of multiple independent reasoning threads at inference time.

More Information

For technical details, visit: https://github.com/Pokee-AI/PokeeResearchOSS
For inquiries, contact: hello@pokee.ai

Model Card Authors

Yi Wan, Jiuqi Wang, Liam Li, Jinsong Liu, Ruihao Zhu, and Zheqing Zhu — Pokee AI Research Team

Model Card Contact

Pokee AI Team — hello@pokee.ai