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Generative language models face many challenges during the transition from training to practical applications. One major issue is how to achieve optimal performance during the inference stage. Current strategies, such as Reinforcement Learning from Human Feedback (RLHF), primarily focus on improving the model's win rate but often overlook decoding strategies during inference, such as Best-of-N sampling and controlled decoding. This gap between training objectives and actual usage may lead to inefficiencies and affect the quality and reliability of outputs. To address these issues, Google D

Recently, a simple elementary school math question has stumped many AI large models, with 8 out of 12 internationally renowned AI large models incorrectly answering the question "Which is larger, 9.11 or 9.9?"During the test, most large models mistakenly believed that 9.11 was greater than 9.9 when comparing numbers after the decimal point. Even under explicit mathematical context constraints, some large models still provided incorrect answers, revealing their shortcomings in mathematical abilit