10 citations · 18 across the 10 of their papers we have counts for
10 papers
LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback
Thai Hoang, Kung-Hsiang Huang, Shirley Kokane +12
Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data, especially for multi-steps tasks that involv…
ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs
Shirley Kokane, Ming Zhu, Tulika Awalgaonkar +15
Evaluating Large Language Models (LLMs) is one of the most critical aspects of building a performant compound AI system. Since the output from LLMs propagate to downstream steps, i…
PRACT: Optimizing Principled Reasoning and Acting of LLM Agent
Zhiwei Liu, Weiran Yao, Jianguo Zhang +13
We introduce the Principled Reasoning and Acting (PRAct) framework, a novel method for learning and enforcing action principles from trajectory data. Central to our approach is the…
xLAM: A Family of Large Action Models to Empower AI Agent Systems
Jianguo Zhang, Tian Lan, Ming Zhu +19
Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing speci…
Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents
Kexun Zhang, Weiran Yao, Zuxin Liu +13
Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27%…
Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research
Tian Lan, Huan Wang, Caiming Xiong +1
We introduce WarpSci, a domain agnostic framework designed to overcome crucial system bottlenecks encountered in the application of reinforcement learning to intricate environments…
APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets
Zuxin Liu, Thai Hoang, Jianguo Zhang +14
The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed t…
AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System
Zhiwei Liu, Weiran Yao, Jianguo Zhang +10
The booming success of LLMs initiates rapid development in LLM agents. Though the foundation of an LLM agent is the generative model, it is critical to devise the optimal reasoning…
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Jianguo Zhang, Tian Lan, Rithesh Murthy +15
Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presen…
Text2Data: Low-Resource Data Generation with Textual Control
Shiyu Wang, Yihao Feng, Tian Lan +6
Natural language serves as a common and straightforward signal for humans to interact seamlessly with machines. Recognizing the importance of this interface, the machine learning c…