LLM总是把简单任务复杂化，Karpathy无语：有些任务无需那么多思考

Core Insights - The emergence of reasoning large models and thinking chains has significantly enhanced the deep thinking capabilities of large models, improving their versatility across different tasks [1][2] - However, there is a growing concern that these models are becoming overly specialized, leading to excessive reasoning even for simple tasks, which complicates their usability [3][4] Group 1: Model Capabilities - The introduction of thinking chains allows large models to conduct in-depth analysis and task breakdown, making them suitable for long-term and complex tasks [1] - The reasoning models have evolved to possess various auxiliary functions and autonomous capabilities, enhancing their overall performance [2] Group 2: Overthinking Phenomenon - Users have observed that enabling deep thinking often results in unnecessarily lengthy reasoning processes for simple tasks, making it difficult to obtain desired responses [3][4] - This overthinking tendency is particularly pronounced in coding tasks, where models may engage in extensive reasoning and analysis, leading to delays in providing results [6][11] Group 3: User Experience - Andrej Karpathy has highlighted the challenges posed by the models' inclination towards excessive reasoning, particularly in coding scenarios, where simple checks become overly complicated [6][9] - Users have expressed a desire for a more straightforward approach to task execution, allowing them to specify urgency and intent more clearly [9][12] Group 4: Benchmarking Issues - Karpathy attributes the overthinking issue to the optimization of large models for long-term tasks in benchmark testing, which negatively impacts their responsiveness to ordinary tasks [11][13] - The models struggle to differentiate between varying contexts of user queries, leading to a tendency to assume a more complex scenario than intended [12]