刚刚！北大校友Lilian Weng最新博客来了：Why We Think

Core Insights - The article discusses advancements in utilizing "thinking time" during model inference, aiming to enhance the reasoning capabilities of AI models like GPT, Claude, and Gemini [2][3][16]. Group 1: Thinking Mechanisms - The concept of "thinking time" is analogous to human cognitive processes, where complex problems require reflection and analysis before arriving at a solution [6]. - Daniel Kahneman's dual process theory categorizes human thinking into fast (System 1) and slow (System 2) modes, emphasizing the importance of slower, more deliberate thought for accurate decision-making [12]. Group 2: Computational Resources - In deep learning, neural networks can be characterized by the computational and storage resources they utilize during each forward pass, impacting their performance [8]. - The efficiency of models can be improved by allowing them to perform more computations during inference, particularly through strategies like Chain of Thought (CoT) prompting [8][18]. Group 3: Chain of Thought (CoT) and Learning Strategies - CoT prompting significantly enhances the success rate of solving mathematical problems, with larger models benefiting more from extended "thinking time" [16]. - Early research focused on supervised learning from human-written reasoning paths, evolving into reinforcement learning strategies that improve CoT reasoning capabilities [14][41]. Group 4: Test-Time Computation Strategies - Two main strategies for improving generation quality are parallel sampling and sequential revision, each with distinct advantages and challenges [19][20]. - Parallel sampling is straightforward but relies on the model's ability to generate correct answers in one go, while sequential revision allows for targeted corrections but is slower [20][21]. Group 5: Reinforcement Learning Applications - Recent studies have successfully employed reinforcement learning to enhance reasoning capabilities in language models, particularly in STEM-related tasks [41][46]. - The training process often involves a cold-start phase followed by reasoning-oriented reinforcement learning, optimizing performance through structured feedback [42][43]. Group 6: External Tools and Integration - Utilizing external tools, such as code interpreters or APIs, can enhance the reasoning process by offloading certain computational tasks [52][56]. - The ReAct method combines external operations with reasoning trajectories, allowing models to incorporate external knowledge into their inference paths [56][57]. Group 7: Model Interpretability and Trustworthiness - The article highlights the importance of model interpretability, particularly through CoT, which allows for monitoring and understanding model behavior [59]. - However, there are concerns regarding the fidelity of CoT outputs, as biases and errors can affect the reliability of the reasoning process [62][64]. Group 8: Adaptive Computation and Token Utilization - Adaptive computation time allows models to dynamically adjust the number of computation steps during inference, enhancing their reasoning capabilities [81]. - Introducing special tokens, such as thinking tokens, can provide additional processing time and improve model performance on complex tasks [85][89].