LLM Agent Training

LLM: Large Language Model

LLM agent training studies how to make language models act reliably over multiple steps, using tools, software, websites, or simulated worlds to complete tasks. Common approaches combine supervised fine-tuning on interaction traces with reinforcement learning from execution-verified outcomes, alongside distillation, preference learning, and feedback on individual steps. Researchers build executable environments and benchmarks such as SWE-bench, WebArena, Terminal-Bench, and τ²-Bench to provide repeatable practice and test tool use, coding, search, and long-horizon workflows. A central challenge is transferring gains beyond training environments while balancing task success with efficiency, safety, and robustness to changing tools and users.

Latest papers

590 papers130 papers in the last four weeks, up 132% on the four weeks before. 0.8% of all new papers.

SSR: Can Simulated Patients Learn to Stigmatize Themselves? Modeling Self-Stigma through Internal Monologue
Jun 6, 2026Kunyao Lan, Bingrui Jin, Zichen Zhu +1Human Behavior Simulation Mental Health
X-LANCE Lab, Dept. of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, China
SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
Jun 6, 2026Yichen Chen, Siying Li, Yuhang Liang +2Multi-Agent LLM Systems Financial Fraud Detection
National University of Singapore · School of Computing, Singapore 117417 · University of Chinese Academy of Sciences +4
Agentopia: Long-Term Life Simulation and Learning in Agent Societies
Jun 5, 2026Xintao Wang, Sirui Zheng, Hongqiu Wu +10Multi-Agent LLM Systems Human Behavior Simulation
Fudan University · Independent Researcher · Johns Hopkins University +1
SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating
Jun 5, 2026Zequn Xie, Junjie Wang, Dan Yang +4Reward Shaping LLM Agent Training
Zhejiang University
From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents
Jun 4, 2026Yuhao Sun, Jiacheng Zhang, Shaanan Cohney +3LLM Guardrails LLM Agent Training
1The University of Melbourne · 2Tsinghua University · †Corresponding author.
CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement
Jun 4, 2026Hong Qian, Yuanhao Liu, Zihan Zhou +7Game-Playing Agents Human Behavior Simulation
Shanghai Institute of AI for Education, and School of Computer Science and Technology, East China Normal University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · Tencent Inc., Shenzhen, China
AdaMEM: Test-Time Adaptive Memory for Language Agents
Jun 4, 2026Yunxiang Zhang, Yiheng Li, Ali Payani +1Agent Memory Test-Time Adaptation
University of Michigan · 2Cisco Research.
Self-Evolving Deep Research via Joint Generation and Evaluation
Jun 3, 2026Han Zhu, Chengkun Cai, Yuanfeng Song +3LLM Evaluation LLM Agent Training
1The Hong Kong University of Science and Technology · 2ByteDance, China · University College London
AgentJet: A Flexible Swarm Training Framework for Agentic Reinforcement Learning
Jun 3, 2026Qingxu Fu, Boyin Liu, Shuchang Tao +2Multi-Agent LLM Systems Agentic RL
Tongyi Lab, Alibaba Group
SaliMory: Orchestrating Cognitive Memory for Conversational Agents
Jun 2, 2026Kai Zhang, Xinyuan Zhang, Hongda Jiang +11Long-Term Conversational Memory Memorization in Generative Models
Meta Reality Labs
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
Jun 2, 2026Ibrahim Abdelaziz, Asim Munawar, Kinjal Basu +4Synthetic Data Generation Reinforcement Learning with Verifiable Rewards
IBM Research
What Makes Interaction Trajectories Effective for Training Terminal Agents?
Jun 2, 2026Sidi Yang, Chaofan Tao, Jierun Chen +11AI Agent Benchmarks LLM Agent Training
1The University of Hong Kong · 2Huawei Technologies · 3Nanyang Technological University
RUBAS: Rubric-Based Reinforcement Learning for Agent Safety
Jun 2, 2026Xian Qi Loye, Qinglin Su, Zhexin Zhang +5Reinforcement Learning AI Agent Safety
The Conversational AI (CoAI) group, DCST, Tsinghua University · Huawei Noah’s Ark Lab
Uncertainty-Aware Clarification in LLM Agents with Information Gain
Jun 2, 2026Mengyi Deng, Zhiwei Li, Xin Li +4Clarification Question Generation Tool-Using Agents
Information Hub, HKUST(GZ) · Jilin University · et al.
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Jun 2, 2026Guhong Chen, Yingcheng Shi, Yongbin Li +6RL for Code Generation Agentic RL
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · 2Tongyi Lab , Alibaba Group · 3Alibaba Group +1
Libra: Efficient Resource Management for Agentic RL Post-Training
Jun 2, 2026Kaiwen Chen, Xin Tan, Jingzong Li +6Agentic RL Reinforcement Learning
The Chinese University of Hong Kong · The Hang Seng University of Hong Kong · Independent Researcher
WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents
Jun 1, 2026Hengrui Gu, Xiaotian Han, Kaixiong ZhouTool-Augmented Language Model Agents Tool-Using Agents
North Carolina State University · Case Western Reserve University
Adaptive Latent Agentic Reasoning
Jun 1, 2026Dongwon Jung, Peng Shi, Yi Zhang +2Agentic Reasoning LLM Agents
University of California, Davis · University of Waterloo · Greenshoe, Inc.
Policy and World Modeling Co-Training for Language Agents
Jun 1, 2026Ning Lu, Baijiong Lin, Shengcai Liu +9Agentic RL World Model Learning
Southern University of Science and Technology · Hong Kong University of Science and Technology · Hong Kong University of Science and Technology (Guangzhou) +2
CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents
Jun 1, 2026Youwei Liu, Jian Wang, Hanlin Wang +1LLM World Models LLM Agent Training
Central South University · College of Computer Science, Sichuan University · Department of Computing, The Hong Kong Polytechnic University
Scaling Agentic Capabilities via Grounded Interaction Synthesis
Jun 1, 2026Wenhang Shi, Jinhao Dong, Yiren Chen +4Synthetic Data Generation Synthetic Data Generation for LLM Agents
Renmin University of China · Peking University · Tencent
HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
May 31, 2026Yi Gu, Huacan Wang, Shuo Zhang +10AI Agent Benchmarks LLM Agent Training
1Midea Group · 2Beijing University of Posts and Telecommunications · 3Donghua University +1
CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation
May 31, 2026Ruihui Hou, Ziyue Huai, Chennuo Zhang +5Tool-Using Agents Clinical Reasoning
East China University of Science and Technology, Shanghai, China · Zhongshan Hospital, Fudan University, Shanghai, China
BAGEN: Are LLM Agents Budget-Aware?
May 29, 2026Yuxiang Lin, Zihan Wang, Mengyang Liu +9LLM Agent Evaluation AI Agent Benchmarks
1Northwestern University · 2O2 Lab · 3Independent +5
Learning to Construct Practical Agentic Systems
May 29, 2026Aditya Kumar, Zhihan Lei, Jerry Yan +6LLM Agent Orchestration Agentic Workflows
Carnegie Mellon University · Dept. of Computer Science Emory University
Used Car Salesbots? Honesty and Credulity of LLMs as Bargaining Agents under Partial Information
May 29, 2026Antonio Valerio Miceli-Barone, Vaishak Belle, Shay B. CohenMulti-Agent LLM Systems Multi-Agent Negotiation
University of Edinburgh
Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration
May 29, 2026Weile Chen, Bingchen Miao, Qifan Yu +6LLM Agent Training Self-Improving Agents
1Zhejiang University
COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents
May 29, 2026Wenkai Shen, Pengyang Zhou, Jiahe Xu +5LLM Safety Alignment LLM Agents
Zhejiang University
AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
May 28, 2026Dongrui Liu, Yu Li, Zhonghao Yang +47LLM Safety Alignment AI Agent Security
LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents
May 28, 2026Xiaoxuan Peng, Kaiqi Zhang, Xinyu Lu +5RL for Language Models Terminal Agents
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences

Jun 6, 2026J/K move · Enter open · S save

SSR: Can Simulated Patients Learn to Stigmatize Themselves? Modeling Self-Stigma through Internal Monologue

Kunyao Lan, Bingrui Jin, Zichen Zhu, Mengyue Wu

X-LANCE Lab, Dept. of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, China

Human Behavior Simulation Mental Health Synthetic Data Generation LLM Agent Training

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Simulating patients with large language models (LLMs) is a promising tool for mental health training, but existing approaches fail to capture a key clinical reality: self-stigma. Patients experiencing self-stigma, the internalization of negative stereotypes, often exhibit context-sensitive resistance, such as avoidance, denial, or self-blame, which current models render as static or uniformly compliant behavior. To address this, we introduce a novel simulation framework grounded in the psychological 3A1H model of self-stigmatization. Our core innovation is the creation of a Stigmatized Self-Reflection (SSR) dataset, where we augment mental health dialogues with internal monologues that reflect stigma-aware reasoning. By fine-tuning LLMs with this data using a chain-of-thought approach, we train patient agents to dynamically adjust their level and expression of stigma based on conversational triggers. Evaluations demonstrate that our approach significantly outperforms specialized baselines, generating more authentic and situationally appropriate patient responses. This work provides a crucial step towards realistic stigma simulation for clinical training and empathetic dialogue systems.