most citedAgentic Reinforced Policy Optimization

4 citations · 12 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2025

GPG: Generalized Policy Gradient Theorem for Transformer-based Policies

Hangyu Mao, Guangting Dong, Zhicheng Dou

We present the Generalized Policy Gradient (GPG) Theorem, specifically designed for Transformer-based policies. Notably, we demonstrate that both standard Policy Gradient Theorem a…

cs.LG2025★ 1 cited

Agentic Entropy-Balanced Policy Optimization

Guanting Dong, Licheng Bao, Zhongyuan Wang +11

Recently, Agentic Reinforcement Learning (Agentic RL) has made significant progress in incentivizing the multi-turn, long-horizon tool-use capabilities of web agents. While mainstr…

cs.LG2025★ 4 cited

Agentic Reinforced Policy Optimization

Guanting Dong, Hangyu Mao, Kai Ma +11

Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn rea…

cs.CL2025★ 4 cited

Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning

Guanting Dong, Yifei Chen, Xiaoxi Li +7

Recently, large language models (LLMs) have shown remarkable reasoning capabilities via large-scale reinforcement learning (RL). However, leveraging the RL algorithm to empower eff…

cs.LG2025

DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering

Rong Cheng, Jinyi Liu, Yan Zheng +6

Multi-Hop Question Answering (MHQA) tasks permeate real-world applications, posing challenges in orchestrating multi-step reasoning across diverse knowledge domains. While existing…

cs.CL2025

From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models

Jinyi Liu, Yan Zheng, Rong Cheng +8

Recent advances in large language models (LLMs) have shown remarkable progress, yet their capacity for logical ``slow-thinking'' reasoning persists as a critical research frontier.…

cs.IR2024★ 2 cited

DMQR-RAG: Diverse Multi-Query Rewriting for RAG

Zhicong Li, Jiahao Wang, Zhishu Jiang +7

Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these i…

cs.AI2024★ 1 cited

SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models

Yibin Chen, Yifu Yuan, Zeyu Zhang +6

Spreadsheets are ubiquitous across the World Wide Web, playing a critical role in enhancing work efficiency across various domains. Large language model (LLM) has been recently att…