LLM Agent Training

LLM: Large Language Model

Follow this topic Ask about this topic

Momentum

54 papers in the last four weeks, up 145% on the four weeks before. 0.5% of all new papers.

Jul 13Week of Sep 28

Latest papers 354

All topics

Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents
Jun 30, 2026Utsav Garg, Sungjin Hong, Jason Jung +6Multilingual Language Models LLM Fine-Tuning
1Cohere
AutoTrainess: Teaching Language Models to Improve Language Models Autonomously
Jun 30, 2026Zhaojian Yu, Penghao Yin, Shuzheng Gao +3Tool-Augmented Language Model Agents LLM Tool Use
1Tsinghua University · 2The Chinese University of Hong Kong · 3Simple Agent Lab
Calibrating the Evaluator: Does Probability Calibration Mitigate Preference Coupling in LLM Agent Feedback Loops?
Jun 30, 2026Zewen LiuLLM-as-a-Judge Pairwise Preference Evaluation
Qilu Institute of Technology, School of Software Engineering Tai’an, Shandong, China
Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents
Jun 30, 2026Keyu Zhao, Lingyan Kong, Fengli Xu +1Multi-Agent LLM Systems AI Agents for Scientific Discovery
Department of Electronic Engineering, Tsinghua University.
Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
Jun 29, 2026Lei Bai, Zongsheng Cao, Yang Chen +50LLM Agent Training Long-Horizon Agent Tasks
DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation
Jun 29, 2026Peyman Hosseini, Ondrej Bohdal, Ahmed Alajrami +6LLM Agents LLM Agent Training
Samsung R&D Institute UK · Queen Mary University of London
MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling
Jun 28, 2026Yizhe Yang, Palakorn Achananuparp, Heyan Huang +2Mental Health Counseling Emotional Support Conversation
Beijing Institute of Technology · Singapore Management University · Australian National University
Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline
Jun 27, 2026Dianwei Chen, Yuan-Zheng Lei, Zifan Zhang +2Domain-Adaptive Pretraining LLM Agent Training
An AI agent for treatment reasoning over a biomedical tool universe
Jun 27, 2026Shanghua Gao, Ayush Noori, Richard Zhu +13Clinical Decision-Making LLM Agent Training
Department of Biomedical Informatics, Harvard Medical School, Boston, MA · Department of Engineering Science, University of Oxford, Oxford, UK · The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA +16
LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior
Jun 26, 2026Qinhong Zhou, Chuang Gan, Anoop CherianMulti-Agent Coordination AI Alignment
University of Massachusetts Amherst, USA. · 2Mitsubishi Electric Research Laboratories (MERL), USA.
Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
Jun 25, 2026Xuan Zhang, Zhijian Zhou, Lingfeng Qiao +6World Model-Based Planning Model-Based Planning
Fudan University · Shanghai Innovation Institute · Tencent Youtu Lab
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Jun 25, 2026Tianyi Men, Zhuoran Jin, Pengfei Cao +3GUI Agents LLM Agent Training
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
Jun 25, 2026Shicheng Ye, Chao YuContinual Learning for LLM Agents LLM Agent Training
Sun Yat-sen University
Where Do CoT Training Gains Land in LLM based Agents?
Jun 25, 2026Jingyu Liu, Zhiwen Wang, Yuxin Jing +2CoT Reasoning LLM Agents
Gaoling School of Artificial Intelligence Renmin University of China, Beijing, China · ByteDance · Beijing Key Laboratory of Research on Large Models and Intelligent Governance +1
Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making
Jun 24, 2026Guangfeng Cai, Kaibing Yang, Shuo He +4World Model Learning World Models
Southeast University · Meituan
OpenThoughts-Agent: Data Recipes for Agentic Models
Jun 23, 2026Negin Raoof, Richard Zhuang, Marianna Nezhurina +47Training Data Curation LLM Agent Training
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
Jun 22, 2026Haggai RoitmanMulti-Agent LLM Systems LLM Agent Orchestration
[cs.AI]
Tmax: A simple recipe for terminal agents
Jun 22, 2026Hamish Ivison, Junjie Oscar Yin, Rulin Shao +3Synthetic Data Generation Terminal Agents
α Allen Institute for AI · ω University of Washington
PhoneBuddy: Training Open Models for Agentic Phone Use
Jun 22, 2026Zhengyang Tang, Xin Lai, Pengyuan Lyu +23Computer-Use Agents Mobile GUI Agents
Tencent Hunyuan · Gaoling School of Artificial Intelligence, Renmin University of China · The Chinese University of Hong Kong, Shenzhen +1
CLI-Universe: Towards Verifiable Task Synthesis Engine for Terminal Agents
Jun 22, 2026Zhanbo Hua, Yifan Yao, Weihao Xie +14LLM Agent Trajectory Synthesis LLM Agents
Nanjing University · StepFun · ZODA +1
MetaPS: Adaptive Programmatic Strategy Selection for Market Agents
Jun 21, 2026Jiaxiang Chen, Aotian Luo, Zhouyi Zheng +3Algorithm Selection LLM Agent Training
1Fudan University · 2TensorPacific
Drowning in Routine: Signal Dilution in Multi-Turn Agent Training
Jun 20, 2026Yann Pernot, Vi RetaultReinforcement Learning Credit Assignment in RL
Mila - Québec AI Institute · Polytechnique Montréal
Steer, Don't Solve: Training Small Critic Models for Large Code Agents
Jun 20, 2026Shubham Gandhi, Yiqing Xie, Atharva Naik +2LLM-as-a-Judge AI Coding Agents
Carnegie Mellon University
Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents
Jun 19, 2026Rajesh Mangannavar, Zachary Coalson, Pranay Dugar +1LLM Planning Symbolic Planning
Oregon State University
Sakana Fugu Technical Report
Jun 19, 2026Yujin Tang, Edoardo Cetin, Jinglue Xu +11LLM Agent Orchestration LLM Agent Training
Fugu Team, Sakana AI 1
Fara-1.5: Scalable Learning Environments for Computer Use Agents
Jun 18, 2026Ahmed Awadallah, Sahil Gupta, Yash Lara +12Computer-Use Agents LLM Agent Training
When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents
Jun 18, 2026Kaiyue Yang, Yuyan Bu, Jingwei Yi +5LLM Agent Security AI Agent Security Benchmarks
Institute of Information Engineering, Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · The Chinese University of Hong Kong +2
Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
Jun 18, 2026Yanxi Chen, Weijie Shi, Boyi Hu +6Continual Learning for LLM Agents Cross-Domain Generalization
Alibaba Group · Hong Kong University of Science and Technology · The University of Hong Kong
SAGE-OPD: Selective Agent-Guided Intervention for Multi-Turn On-Policy Distillation
Jun 17, 2026Yuhang Zhou, Lizhu Zhang, Yifan Wu +5Selective Knowledge Distillation LLM Agent Training
Meta AI
ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
Jun 17, 2026Tengfei Lyu, Zirui Yuan, Xu Liu +4User Modeling LLM Agent Training
HKUST(GZ) · Didichuxing Co. Ltd

Jun 30, 2026, cs.AIJ/K move · Enter open · S save

Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents

Utsav Garg, Sungjin Hong, Jason Jung, Justin Lee+5

1Cohere

Multilingual Language Models LLM Fine-Tuning Text-to-SQL LLM Agent Training RL for LLM Agents

Open paper AskPDF arXiv

We present LuckyStar 111B, a 111B-parameter hybrid reasoning model developed through a collaboration between Cohere and LG CNS for Korean-English enterprise agents under practical memory and serving constraints. The model trains from Cohere's fully post-trained Command A model rather than a new pretraining run, and uses preamble conditioning to switch between concise non-reasoning behavior and longer tool-oriented reasoning. We study four choices for scaling tool-using agents efficiently: multilingual supervised fine-tuning, reinforcement learning with verifiable rewards for multi-step tool-use tasks, language-consistency rewards for Korean user-facing responses, and 4-bit quantization for single-GPU serving. The adapted model improves mathematical reasoning, function calling, and agentic natural-language-to-SQL (NL2SQL) performance while preserving general Korean and English instruction-following quality. These results provide a practical recipe and failure-mode analysis for adapting post-trained multilingual models to verifiable agentic workflows under memory-constrained deployment.

Momentum

Related topics

Latest papers 354