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机器之心· 2025-07-28 10:45

Core Viewpoint - The article discusses the shift in AI research from "can it be done" to "is it effective," emphasizing the need for new evaluation methods that assess the long-term adaptability and learning capabilities of models, particularly in the context of achieving general artificial intelligence [1][4]. Group 1: New Evaluation Paradigm - A new evaluation paradigm called EvaLearn has been proposed to assess the learning ability and efficiency of large language models (LLMs), providing a fresh perspective on understanding their human-like learning potential [5][6]. - EvaLearn focuses on "sequential problem-solving," redefining the evaluation logic for large language models, and has gained significant attention since its open-source release [6][8]. Group 2: Limitations of Traditional Benchmarks - Traditional benchmarks treat problems as isolated samples, failing to evaluate models' learning efficiency and adaptability, which are crucial for understanding their performance [8][9]. - EvaLearn constructs 648 challenging problems organized into 182 sequences, requiring models to solve them in order, thus allowing for a systematic assessment of their learning capabilities [9][11]. Group 3: Key Findings from EvaLearn - The research team found that models exhibit diverse learning abilities across different task types, with most models better leveraging prior experience for mathematical and logical reasoning tasks, while tasks like summarization rely more on pre-trained knowledge [14]. - Models based on chain-of-thought reasoning generally outperform those that are not, demonstrating better stability and the ability to solve multiple related problems consecutively [15]. - Feedback learning, which incorporates evaluations from a verifier, significantly enhances models' learning abilities and efficiency compared to example-based learning [16]. - Learning ability and efficiency metrics provide a comprehensive assessment of models' learning potential, revealing that high static performance does not guarantee superior learning capabilities [17]. Group 4: Evaluation Metrics - EvaLearn employs a comprehensive set of evaluation metrics to characterize models' dynamic learning abilities, including summary accuracy, classification skills, information extraction, logical reasoning, mathematical reasoning, and sequence reasoning [20]. - Overall accuracy, learning speed, first correct position, consecutive correct answers, and post-warm-up accuracy are key indicators used to assess models' performance [21]. Group 5: Learning Efficiency and Methods - The study indicates significant differences in learning efficiency among models and task types, with non-thinking models often showing faster progress in experience accumulation, while thinking models demonstrate more stable gains [44]. - Different problem-solving methods, such as example learning and feedback learning, significantly impact model performance, with feedback learning generally yielding higher accuracy and learning efficiency [46][48]. - The average position of the first correct answer varies across models and tasks, highlighting the models' learning potential and the importance of feedback in enhancing learning outcomes [51][53]. Group 6: Conclusion - EvaLearn represents a novel benchmark framework for sequentially evaluating models' learning abilities and efficiencies across various tasks, revealing significant performance differences among leading models [55][56]. - The findings underscore the importance of understanding models' learning capabilities and efficiencies as a new perspective for evaluating their performance and bridging the gap between current models and human capabilities [57].