A full-process solution for improving search accuracy
Search-R1 provides a triple accuracy optimization mechanism:
- Built-in reorderer::
- Reordering of search results based on E5 embedding model
- is enabled by default and can be configured with the
scripts/download.pyUpdated models
- Hybrid Search Strategy::
- Calling multiple APIs like Google/Bing/Brave at the same time
- exist
retriever_server.pyConfiguring weight parameters
- Reward model optimization::
- modifications
reward_model.styleField Selection Rubric - Supports both rule-based and learned modes.
- modifications
Advanced Tuning Tips:
- Add in custom data
ability: fact-reasoningField Enhanced Factual Reasoning - utilization
build_index.shRebuild Local Indexes to Boost Recall - analyze
Full experiment log 1The accuracy curve adjustment parameter in
Measured effect: the search accuracy can be improved from 68% to 82% on the NQ dataset.
This answer comes from the articleSearch-R1: A Tool for Reinforcement Learning to Train Large Models for Search and ReasoningThe
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