LLM Agent Self-Improvement

This field studies how language-model agents can improve across tasks by learning from their own executions, failures, human feedback, or interaction with an environment. Common approaches update prompts, harnesses, skills, memory, or tools while keeping model weights fixed; others use reinforcement learning, self-distillation, or co-evolving tasks and agents. Methods often analyze trajectories to diagnose weaknesses, then test candidate changes with benchmarks, paired comparisons, or held-out regression gates. A central challenge is making gains transfer to new settings without eroding previously reliable behavior, while keeping evaluation trustworthy and improvement costs manageable.