Core Insights - The article discusses the recent advancements in large language models (LLMs) regarding their ability to achieve continual learning and self-evolution, addressing criticisms about their lack of genuine learning capabilities [1][2]. Group 1: Paths to Continual Learning - The ability of LLMs to learn continuously is fundamentally linked to their memory depth and plasticity, with three main paths identified for enhancing this capability [2]. - The first path involves modifying the "context" or "working memory" of the model through In-Context Learning (ICL), where new information is provided in prompts to help the model learn to solve specific problems [4][6]. - The second path introduces an "external memory bank" (RAG), allowing models to access and maintain an external database for comparison and retrieval, exemplified by Google's DeepMind's "Reasoningbank" [7]. - The third path focuses on parameter-level continual learning, which has faced challenges due to the complexities and instabilities associated with methods like Reinforcement Learning (RL) and Low-Rank Adaptation (LoRA) [10][11]. Group 2: Sparse Memory Fine-Tuning - Meta AI's recent paper introduces Sparse Memory Fine-Tuning (SFT) as a solution to the challenges of traditional SFT, particularly addressing the issue of catastrophic forgetting [11][28]. - The proposed method involves a three-step process: modifying the architecture to include a memory layer, using TF-IDF to identify which parameters to update, and performing sparse updates to only the most relevant parameters [12][22][23]. - This new approach has shown significant improvements, with models experiencing only an 11% drop in performance on original tasks after learning new facts, compared to 71% and 89% drops with LoRA and full fine-tuning, respectively [23][25]. Group 3: Implications for the Future of LLMs - The advancements in SFT suggest a potential shift in how models can be updated safely and effectively, moving away from static tools to dynamic agents capable of continuous learning [31][32]. - The successful implementation of these methods could mark the beginning of a new era for self-evolving models, aligning with the vision of models that grow and adapt through experience [31][32].