从 SEAL 自适应学习到 DFT 奖励矫正，LLM 泛化能力的实质提升又有多少？

Core Insights - The article discusses the challenges and advancements in the generalization capabilities of large language models (LLMs), highlighting various strategies to improve these capabilities, such as adaptive fine-tuning and dynamic gradient adjustment [7][11]. Group 1: Generalization in LLMs - Generalization in AI refers to a model's ability to apply learned knowledge to new, unseen scenarios, distinguishing it from mere memorization of training data [8]. - Recent studies indicate that as the complexity and scale of models increase, the understanding of "generalization" is being questioned, with some suggesting it may be a form of data memorization rather than true abstraction [9][10]. - Research shows that while increasing model size can enhance performance on reasoning tasks, it may also lead to stronger memorization of factual knowledge, raising concerns about the true nature of generalization [9][10]. Group 2: CoT Reasoning and Its Limitations - Chain-of-Thought (CoT) reasoning has been criticized for its fragility, as performance drops significantly when tested outside the training distribution, suggesting reliance on memory rather than genuine logical reasoning [10]. - Some experts argue that what is perceived as generalization may simply be the result of training data sufficiently covering the test scenarios, challenging the notion of true generalization [10]. Group 3: Research Trends and Focus Areas - The volume of research related to LLMs has surged, with a nearly sixfold increase in relevant studies from 2022 to 2025, particularly focusing on reasoning, generalization, and model safety [11]. - Recent research has shifted from merely examining data distribution and model size to exploring training strategies, model update mechanisms, and data design to enhance generalization capabilities [11].