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The 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.
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8/31/2026
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UnverifiedParameterized 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.76% 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.75% similarUnverifiedThe essence of reasoning-enhanced models is trading inference-time compute for accuracy74% similarUnverifiedPrompt engineering is evolving from general techniques toward model-specific optimization74% similarUnverified使用高能力模型进行优化的推荐交互模式包括：明确定义成功指标、提供基线方案、让模型提出改进假设并预估收益、将执行结果反馈给模型迭代调整73% similar
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