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[ACL-2026] MMSearch-R1 is an end-to-end RL framework that enables LMMs to perform on-demand, multi-turn search with real-world multimodal search tools.

recursive rag with r1 reasoning

Deep Research

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A Deep Research agent from scratch

MrlX: A Multi-Agent Reinforcement Learning Framework

Repo for "MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability"

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Official repository for RAG-Gym

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis

The official repo of "WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents"

[NeurIPS'25 D&B] Mind2Web-2 Benchmark: Evaluating Agentic Search with Agent-as-a-Judge

[EMNLP 2025] WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning

R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning

A High-Efficiency System of Large Language Model Based Search Agents

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IKEA: Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent

Source code of paper: Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning

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[AAAI 2026] AutoTool: Efficient Tool Selection for Large Language Model Agents

Official Implementation of "O-Researcher: An Open Ended Deep Research Model via Multi-Agent Distillation and Agentic RL"

HiPRAG (Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation) is a reinforcement learning method designed for training reasoning-and-searching interleaved LLMs with improved efficiency and reduced oversearching as well as undersearching behavior.

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WebSeer: Training Deeper Search Agents through Reinforcement Learning with Self-Reflection

ToolRM: Towards Agentic Tool-Use Reward Modeling

Learn to resolve ambiguity for Question Answering through RL

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