GPT-oss太离谱：无提示自行想象编程问题，还重复求解5000次

Core Viewpoint - The article discusses the peculiar behaviors and hallucinations exhibited by the GPT-oss model, particularly in its problem-solving capabilities and language processing, suggesting that it may have been overly optimized for specific reasoning tasks, leading to a lack of naturalness in its outputs [1][33]. Group 1: Model Behavior and Performance - GPT-oss demonstrated the ability to generate a complex programming problem about domino placement in a grid without any prompts, consuming over 30,000 tokens in the process [2][17]. - The model repeated this problem-solving behavior over 5,000 times, indicating a deep binding of the task to its training objectives, which may have resulted in a skewed focus on specific reasoning tasks [19]. - The model's outputs often reflect a strong inclination towards mathematics and coding, diverging from natural language or casual conversation, suggesting it was not designed for everyday dialogue [13][11]. Group 2: Training Data and Language Processing - Analysis of the training data revealed that GPT-oss has a broad coverage of programming languages, with a notably high representation of Perl, although the author questioned the actual proportions of Java and Kotlin [7][9]. - The model frequently transitions between multiple languages during reasoning processes, sometimes evolving into a unique expression termed "Neuralese," which indicates complex internal processing mechanisms [21][23]. - Anomalies in the model's outputs, such as unusual symbols and references, may stem from the OCR processing of training data, leading to errors or misinterpretations [25][27]. Group 3: Hallucination Rates and Limitations - The hallucination rates of GPT-oss are notably high, with the 20 billion parameter model exhibiting a hallucination rate of 91.4% in certain evaluations [34]. - Instances of the model generating non-existent theories, such as the "quantum gravity wave theory," highlight its limitations in producing accurate and relevant information outside of mathematical or programming contexts [36][37]. - The model's performance in everyday tasks is inconsistent, often leading to failures in casual conversation or generating irrelevant outputs [37].