Using a curriculum for reinforcement learning to train an llm-based network troubleshooting agent

An apparatus, comprising:

• one or more network interfaces;
• a processor coupled to the one or more network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process that is executable by the processor, the process when executed configured to:
• determine how well a large language model-based troubleshooting agent for a network was able to perform during a first test having a first difficulty;
• update the large language model-based troubleshooting agent using reinforcement learning based on how well the large language model-based troubleshooting agent was able to perform during the first test;
• select a second difficulty for a second test based on how well the large language model-based troubleshooting agent was able to perform during the first test;
• and
• initiate the second test to assess how well the large language model-based troubleshooting agent is able to perform.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:

• determining, by the device, how well a large language model-based troubleshooting agent for a network was able to perform during a first test having a first difficulty;
• updating, by the device, the large language model-based troubleshooting agent using reinforcement learning based on how well the large language model-based troubleshooting agent was able to perform during the first test;
• selecting, by the device, a second difficulty for a second test based on how well the large language model-based troubleshooting agent was able to perform during the first test;
• and
• initiating, by the device, the second test to assess how well the large language model-13 based troubleshooting agent is able to perform.