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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
No description.
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"
No description.
No description.
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
No description.
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
No description.
No description.
[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.
No description.
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
No description.