5 papers
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates
Hy Dang, Tianyi Liu, Zhuofeng Wu +9
Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities, yet they often fail in real-world tool-interactions due to incorrect parameterization, po…
RRO: LLM Agent Optimization Through Rising Reward Trajectories
Zilong Wang, Jingfeng Yang, Sreyashi Nag +5
Large language models (LLMs) have exhibited extraordinary performance in a variety of tasks while it remains challenging for them to solve complex multi-step tasks as agents. In pr…
IHEval: Evaluating Language Models on Following the Instruction Hierarchy
Zhihan Zhang, Shiyang Li, Zixuan Zhang +11
The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent an…
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
Yuchen Zhuang, Jingfeng Yang, Haoming Jiang +16
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce ne…
Two-Step Offline Preference-Based Reinforcement Learning with Constrained Actions
Yinglun Xu, Tarun Suresh, Rohan Gumaste +10
Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework where one app…