Page 1: 第1页 Welcome. In our discourse today, we will dissect a significant advancement in artificial intelligence: the concept of self-adapting language models. We will proceed logically, from the foundational problem to the proposed solution, its mechanisms, empirical results, and future implications. Let us begin. Page 2: 第2页 Let us first consider the fundamental limitation of contemporary large language models. They are, in essence, static entities. Imagine a brilliant scholar whose entire library of knowledge was finalized the day they graduated, with no capacity to add new books or revise existing ones. This is the state of most LLMs. The SEAL framework addresses this by proposing a mechanism for self-adaptation, allowing the model to generate its own "self-edits" to persistently update its internal knowledge structure. Page 3: 第3页 The engine driving SEAL is a reinforcement learning loop. The process is analogous to a scientist testing hypotheses. Given a new problem, the model proposes several potential "self-edits"—hypotheses on how to improve. It then temporarily applies each edit and evaluates the outcome. Edits that lead to improved performance are "rewarded," reinforcing the model's ability to generate effective updates in the future. This iterative cycle of generation, application, evaluation, and reinforcement allows the model to learn how to teach itself. Page 4: 第4页 The theoretical elegance of SEAL is matched by its practical utility, which we have tested in two domains. First, in knowledge incorporation, the model learns to assimilate new facts, much like a scholar updating an encyclopedia. Second, in few-shot learning, the model demonstrates the ability to master new abstract reasoning tasks from minimal examples. In both cases, the model does not just passively receive information; it actively generates the necessary materials to facilitate its own learning process. Page 5: 第5页 A framework's value is ultimately determined by its performance. The results for SEAL are compelling. In knowledge incorporation tasks, SEAL demonstrates a superior ability to integrate new facts, significantly outperforming alternative methods. Similarly, in the challenging domain of few-shot abstract reasoning, SEAL's learned adaptation strategy yields a dramatic improvement in success rate. These results provide strong evidence that SEAL is a viable and effective step towards truly adaptive models. Page 6: 第6页 We must also be rigorous in acknowledging the framework's limitations. The most significant is the well-known problem of catastrophic forgetting. As the model adapts to new knowledge, it risks overwriting or corrupting prior knowledge. The provided data clearly visualizes this decay in performance on older tasks as new ones are learned. The path forward requires developing mechanisms for knowledge retention, ensuring that learning is cumulative rather than destructive. Future work envisions models that not only adapt, but reason about when and how to do so, transforming ephemeral reasoning into permanent capability. Page 7: 第7页 In summary, we have journeyed from identifying the problem of static knowledge in LLMs to exploring the SEAL framework as a potent solution. Through a reinforcement learning process of self-editing, the model learns to adapt. While empirical results are highly promising, the challenge of catastrophic forgetting highlights the path that still lies ahead. SEAL represents a principled and promising step toward creating more dynamic, continuously learning intelligent systems.