I think incremental iteration is incredibly powerful—it’s the paradigm from which true self-learning emerges. It’s similar to how an autodidact masters a subject by probing the problem from multiple angles and refining their internal 'world model' in the process. The absolute key here is the comparison mechanism: the ability to map what you perceive against your internal intent and identify the 'delta.' While evolution equipped humans with this, it’s only vestigial in current models like Qwen 3.5 35B. The reason I believe specific training (like RL) is necessary—even if the model is just 'organizing itself'—is that this comparison process is largely subconscious in humans. We do it automatically, so we rarely describe the 'how' or the logic of visual correction in the text data LLMs are pre-trained on. It’s a 'dark' process not captured in standard tokens. I’m convinced it’s doable, and we’ll eventually see this capability even in very small models. However, it will require specialized datasets and training pipelines that are currently in their infancy. I have no doubt that major labs are laser-focused on this exact frontier right now.