Parameterized policy updates directly modify model weights (e.g., online updating of LoRA fine-tuning layers), offering a higher capability ceiling but introducing the risk of catastrophic forgetting; non-parameterized approaches change behavior by modifying prompts, retrieved content, or tool configurations, offering strong reversibility but limited expressiveness.