Learning, Adaptation, and Continual Improvement
How Agents Learn to Become Better Agents
Learning Paradigms
Agentic improvement can arise through supervised fine-tuning, reinforcement learning, preference optimization, imitation learning, online adaptation, and meta-learning. The central challenge is to improve behavior without destabilizing safety or prior competencies.
Reward and Preference Modeling
When direct rewards are sparse or noisy, agents may learn from preferences over trajectories. Let and be two behaviors; a preference model estimates which is better and uses that signal to shape future policy updates.
Continual Learning Risk
Agents that learn online can suffer catastrophic forgetting, reward hacking, or rapid drift from intended behavior.
Adaptation Methods
Which learning approach is most directly based on expert demonstrations?
Imitation learning trains an agent to reproduce expert behavior.
Correct answer: Imitation learning
What is catastrophic forgetting?
Continual adaptation can overwrite earlier competencies if not controlled.
Correct answer: The loss of previously learned skills or knowledge when learning new tasks.
Research Challenge
A major open problem is how to enable robust long-term learning from deployment while preserving alignment, privacy, and auditability. This requires careful data governance, update constraints, and evaluation protocols.
Why is preference modeling useful in agentic AI?
Preferences are often easier to collect than precise scalar rewards.
Correct answer: It can provide supervision when direct rewards are hard to define