GUO, Zhijiang - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Spring 2025-26 Winter 2025-26 Fall 2024-25 Summer
2025-26 Spring
Common Core Course
The class information on this page will normally be updated every 15 to 20 minutes during class enrollment period.
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COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	Large language model (LLM) based-Agents have been an important frontier in AI, however, they still fall short critical skills, such as complex reasoning, for solving hard problems and enabling applications in real-world scenarios. This course covers the foundations and applications of language agents that can continuously improve themselves through interaction with the environment. The course will start with inference and post-training techniques for building language agents, such as scaling test-time compute, combining tools with LLMs, and reinforcement learning with verified rewards, etc. We focus on two application domains: mathematics and programming. Our goal is that the students learn from the latest research papers, discuss the suggested readings in each class, work on an original research project in this area, and learn from invited academic and industry speakers about applications in building language agents.

VECTOR

[3-0-0:3]

DESCRIPTION

Large language model (LLM) based-Agents have been an important frontier in AI, however, they still fall short critical skills, such as complex reasoning, for solving hard problems and enabling applications in real-world scenarios. This course covers the foundations and applications of language agents that can continuously improve themselves through interaction with the environment. The course will start with inference and post-training techniques for building language agents, such as scaling test-time compute, combining tools with LLMs, and reinforcement learning with verified rewards, etc. We focus on two application domains: mathematics and programming. Our goal is that the students learn from the latest research papers, discuss the suggested readings in each class, work on an original research project in this area, and learn from invited academic and industry speakers about applications in building language agents.

DSAA 6000J - Foundations of Language Agents (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6301)	We 04:30PM - 07:20PM	Rm 102, E1	GUO, Zhijiang	40 Quota/Enrol/Avail For PhD (DSA) students only: 20/0/20	0	40	0