Agent Environment is Key: A Survey towards Scaling Interactive Experience Collection

PhD Qualifying Examination


Title: "Agent Environment is Key: A Survey towards Scaling Interactive 
Experience Collection"

by

Mr. Yuchen HUANG


Abstract:

LLM-based agents can autonomously accomplish complex tasks across various 
domains. However, to further cultivate capabilities such as adaptive 
behavior and long-term decision-making, training on static datasets built 
from human-level knowledge is insufficient. These datasets are costly to 
construct and lack dynamism, failing to capture the interactive nature of 
real-world scenarios. A growing consensus is that agents should instead 
interact directly with environments and learn from experience through 
reinforcement learning. We formalize this process as the 
Generation-Execution-Feedback (GEF) loop, where environments generate 
tasks to challenge agents, return observations in response to agents' 
actions during task execution, and provide feedback on trajectories, 
yielding experience for subsequent training. Under this paradigm, 
environments become the primary engines for experience generation, making 
their scaling in complexity, dynamism, and interactivity a prerequisite for 
collecting high-quality trajectories at scale. In this survey, we adopt an 
environment-centric lens and organize existing advances into a unified 
taxonomy along the GEF loop. Beyond taxonomy, we further analyze underlying 
implementation frameworks, identify open problems, and outline critical 
future directions, arguing that the deliberate engineering of interactive 
environments will be a decisive frontier in the pursuit of generalist 
agents.


Date:                   Thursday, 30 January 2026

Time:                   4:00pm - 6:00pm

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Dr. May Fung (Supervisor)
                        Dr. Yangqiu Song (Chairperson)
                        Dr. Junxian He