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LMSys聊天机器人竞技场排行榜 VS Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration

LMSys聊天机器人竞技场排行榜与Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration对比，LMSys聊天机器人竞技场排行榜与Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration有什么不同？

LMSys聊天机器人竞技场排行榜

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什么是LMSys聊天机器人竞技场排行榜

LMSys聊天机器人竞技场排行榜是一个针对大型语言模型（LLM）性能评估的众包开放平台。利用Elo排名系统对LLM进行客观排名，依据超过30万用户投票结果。用户可在平台上与不同LLM进行互动，并根据对话质量投票。可用于追踪LLM发展趋势，为研究人员和开发者提供基准测试。

LMSys聊天机器人竞技场排行榜的功能亮点

1. 利用Elo排名系统进行客观评估。2. 为用户提供与不同LLM进行互动的平台。3. 收集用户投票数据，以持续更新排行榜。
通过众包投票、Elo排名系统等方式，客观评估不同LLM的性能，为用户提供真实有效的排行榜，可追踪LLM发展趋势。

LMSys聊天机器人竞技场排行榜的使用案例

1. 研究人员和开发者评估和比较不同LLM的性能，追踪发展趋势。2. 企业选择最佳LLM用于开发聊天机器人或其他人工智能应用。3. 普通用户体验不同LLM功能，参与LLM性能评测。

使用LMSys聊天机器人竞技场排行榜的好处

1. 提供真实有效的LLM排行榜。2. 可追踪LLM发展趋势。3. 为研究人员、开发者和企业提供有用的性能评估工具。

LMSys聊天机器人竞技场排行榜的局限性

依赖用户投票数据，排名结果可能受到个体主观因素的影响。

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration

Train LLMs to reason and call search engines efficiently using reinforcement learning
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什么是Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration

Search-R1 is a powerful reinforcement learning framework designed for training language models (LLMs) that can reason and make tool calls—such as to search engines—in a coordinated manner. It builds on the concepts of DeepSeek-R1(-Zero) and incorporates cutting-edge tools like veRL, a reinforcement learning library that facilitates efficient training of models with complex tool interactions. This framework allows LLMs to access external information via search engines, boosting their ability to handle reasoning tasks dynamically and effectively.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration怎么用？

To use Search-R1, follow these steps: 1. Set up the environment using the provided conda commands and install necessary libraries like PyTorch, vLLM, and Flash Attention. 2. Train an LLM (e.g., Llama3 or Qwen2.5) with reinforcement learning methods like PPO. 3. Use your own dataset or pre-built datasets for training. 4. Integrate local or online search engines and make sure the LLM can call these engines during training for information retrieval. 5. Run the model on the inference server and ask the trained model questions to observe its reasoning ability in real-time.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration核心功能

  • Search-R1 offers a range of powerful features:
  • Support for local sparse and dense retrievers (BM25, ANN, etc.)
  • Integration with major search engines like Google and Bing
  • Flexible RL methods (PPO, GRPO, reinforce)
  • Compatibility with various LLMs (e.g., Llama3, Qwen2.5)
  • Open-source RL training pipeline for easy customization and experimentation

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration使用案例

  • Here are some example use cases for Search-R1:
  • Train a reasoning-based LLM using the NQ dataset, integrating the E5 retriever and Wikipedia corpus for real-world information retrieval.
  • Conduct multi-turn reasoning tasks where the model interacts with search engines and refines its answers based on subsequent search results.
  • Implement a custom search engine setup for specialized domain-specific tasks and incorporate it into the RL training loop.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration价格

Search-R1 is an open-source project, and its codebase can be freely accessed on GitHub. The cost of using it is minimal for small-scale training but may scale with larger datasets and LLMs. For instance, large models like 30B+ parameter LLMs can incur additional computational costs, particularly when running distributed training across multiple nodes.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration公司名称

Search-R1 is developed and maintained by PeterGriffinJin, a contributor to open-source machine learning research.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration联系方式

For inquiries, you can reach the Search-R1 team at the email address: [email protected].

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration社交媒体

Stay connected with the Search-R1 team on social media: Twitter: @PeterGriffinJin Instagram: @petergriffinjin