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The official repository of "A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications".
| Date | Stars |
|---|---|
| 2026-07-31 | 281 |
| 2026-08-01 | 281 |
| 2026-08-02 | 281 |
| 2026-08-05 | 282 |
| 2026-08-06 | 282 |
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# Awesome RL-based Agentic Search Papers
This repository summarizes recent research on reinforcement‑learning (RL)‑based agentic search systems. These systems treat information‑seeking as a decision process: when a large language model (LLM) faces a complex question, it can plan and act by issuing search queries, revising those queries, and integrating evidence into its reasoning. RL techniques allow these agents to learn when to search, how intensively to search and how to integrate retrieved evidence into reasoning.
For more details, please check out our survey paper: [A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications](https://arxiv.org/abs/2510.16724). If you find this repository helpful, please cite our survey paper.
```
@article{lin2025comprehensive,
title={A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications},
author={Minhua Lin, Zongyu Wu, Zhichao Xu, Hui Liu, Xianfeng Tang, Qi He, Charu Aggarwal, Hui Liu, Xiang Zhang, Suhang Wang},
journal={arXiv preprint arXiv:2510.16724},
year={2025}
}
```
We are actively maintaining this repository!
## Contents
* [Overview of RL-based Agentic Search](#overview-of-rl-based-agentic-search)
* [Illustrative Framework of RL-based Agentic Search](#illustrative-framework-of-rl-based-agentic-search)
* [Representative Survey](#representative-survey)
* [Method](#method)
* [Evaluation](#evaluation)
## Overview of RL-based Agentic Search
## Illustrative Framework of RL-based Agentic Search
## Representative Survey
| Time | Paper Title | Venue |
| :---- | :----------- | :---- |
| 2026.3 | [SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions](https://arxiv.org/abs/2603.07379) | *arXiv* |
| 2026.3 | [Deep Research of Deep Research: From Transformer to Agent, From AI to AI for Science](https://arxiv.org/abs/2603.28361) | *arXiv* |
| 2026.1 | [Agentic Reasoning for Large Language Models](https://arxiv.org/abs/2601.12538) | *arXiv* |
| 2025.12 | [Deep Research: A Systematic Survey](https://arxiv.org/abs/2512.02038) | *arXiv* |
| 2025.10 | [A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications](https://arxiv.org/abs/2510.16724) | *arXiv* |
| 2025.9 | [Reinforcement Learning Foundations for Deep Research Systems: A Survey](https://arxiv.org/abs/2509.06733) | *arXiv* |
| 2025.9 | [The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://arxiv.org/abs/2509.02547) | *TMLR* |
| 2025.8 | [Deep Research: A Survey of Autonomous Research Agents](https://arxiv.org/abs/2508.12752) | *arXiv* |
| 2025.8 | [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://arxiv.org/abs/2508.05668) | *arXiv* |
| 2025.6 | [Deep Research Agents: A Systematic Examination And Roadmap](https://arxiv.org/abs/2506.18096) | *arXiv* |
| 2025.6 | [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://arxiv.org/abs/2506.18959) | *arXiv* |
| 2025.6 | [Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges](https://arxiv.org/abs/2506.10408) | *arXiv* |
| 2025.4 | [Synergizing RAG and Reasoning: A Systematic Review](https://arxiv.org/abs/2504.15909) | *arXiv* |
| 2025.1 | [Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG](https://arxiv.org/abs/2501.09136) | *arXiv* |
---
## Method
### How RL is Used: Optimization Strategies
The below table summarizes representative works with corresponding optimization strategies. Specifically, ORM and PRM denote the OutcomeExcerpt of 161,209 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cdf250171eb58a43, name:agentic, desc:agentic