R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

• URL: http://arxiv.org/abs/2503.05592v1
• Date: Fri, 07 Mar 2025 17:14:44 GMT
• Title: R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
• Authors: Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, Ji-Rong Wen,
• Abstract summary: textbfR1-Searcher is a novel two-stage outcome-based RL approach designed to enhance the search capabilities of Large Language Models.<n>Our framework relies exclusively on RL, without requiring process rewards or distillation for a cold start.<n>Our experiments demonstrate that our method significantly outperforms previous strong RAG methods, even when compared to the closed-source GPT-4o-mini.
• Score: 87.30285670315334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing Large Reasoning Models (LRMs) have shown the potential of reinforcement learning (RL) to enhance the complex reasoning capabilities of Large Language Models~(LLMs). While they achieve remarkable performance on challenging tasks such as mathematics and coding, they often rely on their internal knowledge to solve problems, which can be inadequate for time-sensitive or knowledge-intensive questions, leading to inaccuracies and hallucinations. To address this, we propose \textbf{R1-Searcher}, a novel two-stage outcome-based RL approach designed to enhance the search capabilities of LLMs. This method allows LLMs to autonomously invoke external search systems to access additional knowledge during the reasoning process. Our framework relies exclusively on RL, without requiring process rewards or distillation for a cold start. % effectively generalizing to out-of-domain datasets and supporting both Base and Instruct models. Our experiments demonstrate that our method significantly outperforms previous strong RAG methods, even when compared to the closed-source GPT-4o-mini.

Related papers

• Toward Efficient Exploration by Large Language Model Agents [14.712532175418884]
  Large language models (LLMs) can be used to explicitly implement an existing reinforcement learning algorithm. We show how our LLM-based implementation of a known, data-efficient RL algorithm can be considerably more effective in natural language tasks.
  arXiv  Detail & Related papers  (2025-04-29T17:59:48Z)
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning [50.419872452397684]
  Search-R1 is an extension of reinforcement learning for reasoning frameworks. It generates search queries during step-by-step reasoning with real-time retrieval. It improves performance by 41% (Qwen2.5-7B) and 20% (Qwen2.5-3B) over various RAG baselines.
  arXiv  Detail & Related papers  (2025-03-12T16:26:39Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains.<n>Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities.<n>We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement [85.08223786819532]
  Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks.<n>We propose textbfRAG-Star, a novel RAG approach that integrates retrieved information to guide the tree-based deliberative reasoning process.<n>Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods.
  arXiv  Detail & Related papers  (2024-12-17T13:05:36Z)
• Reinforcement Learning Enhanced LLMs: A Survey [45.57586245741664]
  We will make a systematic review of the most up-to-date state of knowledge on RL-enhanced large language models (LLMs)<n>Specifically, we detail the basics of RL; (2) introduce popular RL-enhanced LLMs; (3) review researches on two widely-used reward model-based RL techniques: Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF)
  arXiv  Detail & Related papers  (2024-12-05T16:10:42Z)
• Efficient Reinforcement Learning with Large Language Model Priors [18.72288751305885]
  Large language models (LLMs) have recently emerged as powerful general-purpose tools. We propose treating LLMs as prior action distributions and integrating them into RL frameworks. We show that incorporating LLM-based action priors significantly reduces exploration and complexity optimization.
  arXiv  Detail & Related papers  (2024-10-10T13:54:11Z)
• Reinforcement Learning Problem Solving with Large Language Models [0.0]
  Large Language Models (LLMs) have an extensive amount of world knowledge, and this has enabled their application in various domains to improve the performance of Natural Language Processing (NLP) tasks. This has also facilitated a more accessible paradigm of conversation-based interactions between humans and AI systems to solve intended problems. We show the practicality of our approach through two detailed case studies for "Research Scientist" and "Legal Matter Intake"
  arXiv  Detail & Related papers  (2024-04-29T12:16:08Z)
• How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
  Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
  arXiv  Detail & Related papers  (2024-02-25T20:07:13Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Rethinking with Retrieval: Faithful Large Language Model Inference [91.66406351103484]
  We propose a novel post-processing approach, rethinking with retrieval (RR) RR retrieves relevant external knowledge based on the reasoning steps obtained from the chain-of-thought prompting. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks.
  arXiv  Detail & Related papers  (2022-12-31T22:35:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.