By Victoria Gayton
Publication Date: 2026-10-02 20:12:00
Agent workload isolation is becoming a practical infrastructure challenge as research teams move beyond training models to running code and testing agents. Systems built for intensive computation must now accommodate workloads that interact with tools, storage and other services.
IBM Research’s infrastructure must support increasingly varied workloads as its model development practices evolve. Reinforcement learning adds a task-execution stage that changes what those systems must accommodate, according to Brian Belgodere (pictured), senior technical staff member at International Business Machines Corp.
“The RL process is [that] you are in the middle of training a model,” he said. “At some point, you take that checkpoint and then actually load it into inference, ask it to do something and you’re measuring. That’s your testing phase.”
Belgodere spoke with theCUBE Research’s Dave Vellante and John Furrier at the Fully Connected event, during an exclusive broadcast on…