System 2 Reasoning via Generality and Adaptation

• URL: http://arxiv.org/abs/2410.07866v2
• Date: Fri, 22 Nov 2024 07:18:19 GMT
• Title: System 2 Reasoning via Generality and Adaptation
• Authors: Sejin Kim, Sundong Kim,
• Abstract summary: This paper explores the limitations of existing approaches in achieving advanced System 2 reasoning. We propose four key research directions to address these gaps. We aim to advance the ability to generalize and adapt, bringing computational models closer to the reasoning capabilities required for Artificial General Intelligence (AGI)
• Score: 5.806160172544203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While significant progress has been made in task-specific applications, current models struggle with deep reasoning, generality, and adaptation -- key components of System 2 reasoning that are crucial for achieving Artificial General Intelligence (AGI). Despite the promise of approaches such as program synthesis, language models, and transformers, these methods often fail to generalize beyond their training data and to adapt to novel tasks, limiting their ability to perform human-like reasoning. This paper explores the limitations of existing approaches in achieving advanced System 2 reasoning and highlights the importance of generality and adaptation for AGI. Moreover, we propose four key research directions to address these gaps: (1) learning human intentions from action sequences, (2) combining symbolic and neural models, (3) meta-learning for unfamiliar environments, and (4) reinforcement learning to reason multi-step. Through these directions, we aim to advance the ability to generalize and adapt, bringing computational models closer to the reasoning capabilities required for AGI.

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