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Self-improvement is defined as an update process induced by the system itself, in which the system generates learning signals while executing strategies and interacting with tasks, and uses these signals to make substantive modifications to the system's internal configuration, thereby changing the strategies that govern its future behavior.
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9/24/2026
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UnverifiedThe self-improvement mechanism achieves behavioral optimization primarily through context engineering at the inference stage, which differs from online fine-tuning that involves updating model parameters.72% similarUnverifiedSelf-reflection is achieved by having the model evaluate the quality of its own output and iteratively improve it; the Reflexion framework demonstrates that an agent can learn from failed attempts and adjust its strategy in the next iteration.68% similarUnverifiedThe Gödel Machine can execute any self-modification as long as a formal proof confirms that the modification will improve future expected utility, including modifying the very mechanism used to prove the improvement.66% similarUnverifiedThe "generate-evaluate-refine" loop is referred to in academia as "Self-Refinement," and research shows it can improve LLM output quality, especially in code generation and mathematical reasoning tasks.65% similarUnverifiedThe core mechanism of Self-Refine is to have the large language model critically evaluate its own initial output, then generate an improved version based on the evaluative feedback, iterating until requirements are met or the iteration limit is reached, with the entire process relying solely on a single model's reasoning capability and requiring no external reward model or human feedback.65% similar
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