Adaptive optimization mechanisms
This feature implements a unique three-stage optimization based on the Policy Gradient approach to reinforcement learning:
- Initial assessment phase: Scoring the quality of search results by a pre-trained Reward model (0-1 interval)
- strategy adjustment phase: Trigger the query reconstruction module when confidence score < 0.7, possibly:
- Expand/shrink search scope (e.g. "AI medical" → "AI-assisted diagnosis")
- Add qualifiers (add filters for time, geography, etc.)
- Switching data source types (from news to academic databases)
- final validation phase: Adjusted strategies need to generate significantly higher reward signals to be included in the long-term strategy pool
The key technological breakthrough lies in expanding the discrete action space of traditional RL into a continuous strategy space that includes semantic understanding, which brings the adjustment process closer to the human researcher's thinking mode.
This answer comes from the articleDeepResearcher: driving AI to study complex problems based on reinforcement learningThe
May not be reproduced without permission:AI productivity tools " What are the characteristics of the technical implementation of the DeepResearcher self-reflective adjustment feature?
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