Technical Principles
DeepResearcher's core technical architecture is based on two pillars: large-scale language modeling (LLM) and reinforcement learning (RL) frameworks.
- model-based: Adoption of customized LLM supporting 7B parameters with complex semantic understanding and text generation capabilities
- Strengthening learning mechanisms: End-to-end training through the Ray framework to continuously optimize search strategies in real network environments
- Dynamic Adaptation System: Includes a self-reflective adjustment module that automatically adjusts keywords and search paths based on the confidence level of the search results.
Compared with traditional research tools, its innovativeness is reflected in the combination of RL's decision optimization capabilities with LLM's knowledge processing capabilities to form a closed-loop learning system. The technology stack contains the PyTorch deep learning framework, FlashAttention acceleration technology, and a specially developed validation and evaluation module.
This answer comes from the articleDeepResearcher: driving AI to study complex problems based on reinforcement learningThe
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