R1-searcher

R1-searcher is a method that uses reinforcement learning to enhance the search capabilities of large language models (LLMs). It mainly addresses the issue where LLMs lack necessary knowledge when facing problems requiring extensive external knowledge, especially multi-hop and time-sensitive questions. R1-searcher enables models to learn to invoke web searches during reasoning via two-stage, result-supervised reinforcement learning, in order to obtain external information.

Core Ideas:

1. Two-Stage Training:
   • Phase One: Teach the model how to invoke web search using only format rewards (format-reward) to ensure the model conducts searches in the correct format.
   • Phase Two: Instruct the model on how to effectively use search, including format rewards and answer rewards (answer-reward), encouraging the model to search and utilize external information to correctly answer questions.
2. Reinforcement Learning Driven: Through reinforcement learning algorithms (e.g., Reinfoce++) and carefully designed reward mechanisms, the model is incentivized to autonomously learn searching and reasoning.
3. No Dependence on Instruction Fine-Tuning: R1-searcher does not require complex instruction fine-tuning and can be compatible with existing foundational LLMs or conversational LLMs.

R1-searcher Use Cases

R1-searcher applies to the following scenarios:

• Knowledge-Intensive Tasks: Problems that require extensive external knowledge to solve, such as:
  • Question-Answering Tasks: Complex multi-hop questions that require extracting information from multiple sources to find answers.
  • Time-Sensitive Issues: Tasks requiring up-to-date information, such as event tracking, news summaries, etc.
• Tasks Needing Explainability: Through the search process, the model can provide the basis for its answers, enhancing explainability.
• Tasks Aiming to Improve LLM Accuracy: Especially when the knowledge scope is limited or needs updating, search can significantly improve accuracy.
• General Question-Answering: In specific domains without sufficient training data, retrieval can effectively supplement knowledge and improve response quality.

In summary, R1-searcher is suitable for any scenario where an LLM needs to use external information to enhance its reasoning ability and knowledge scope. By driving autonomous learning through reinforcement learning, it enables the model to possess stronger search capabilities and more reliable answer generation abilities.