通过对话反思提升智能体的推理能力，而非依赖规模扩张。
Position: Introspective Experience from Conversational Environments as a Path to Better Learning
- 让智能体在对话中自我反思，从社会互动中内化思维过程。
- 对话质量决定推理深度，高质量对话使学习脱离原始数据流。
- 适合研究通用人工智能与人机交互的学者参考。
当前人工智能训练将推理视为规模带来的涌现现象。我们提出，稳健的推理源于语言自我反思，而这种反思又来自高质量的社会互动。基于维果茨基发展心理学，我们提出三个核心观点：第一，私有心智的社会起源——从对话环境中的学习逐渐成为理解世界的新方式；与另一主体（无论是否真实）的互动摩擦，能细化并固化推理过程。第二，对话式支架下的内省体验使智能体实现脱离即时数据流的意义建构，将原始环境数据转化为可学习的丰富叙事。第三，对话质量即新数据质量——智能体私有推理的深度及其测试时计算效率，取决于其掌握对话的多样性与严谨性。结论指出，优化这些对话架构是下一代通用智能的关键杠杆。
原文摘要 · Abstract (English)
Current approaches to AI training treat reasoning as an emergent property of scale. We argue instead that robust reasoning emerges from linguistic self-reflection, itself internalized from high-quality social interaction. Drawing on Vygotskian developmental psychology, we advance three core positions centered on Introspection. First, we argue for the Social Genesis of the Private Mind: learning from conversational environments rises to prominence as a new way to make sense of the world; the friction of aligning with another agent, internal or not, refines and crystallizes the reasoning process. Second, we argue that dialogically scaffolded introspective experiences allow agents to engage in sense-making that decouples learning from immediate data streams, transforming raw environmental data into rich, learnable narratives. Finally, we contend that Dialogue Quality is the New Data Quality: the depth of an agent's private reasoning, and its efficiency regarding test-time compute, is determined by the diversity and rigor of the dialogues it has mastered. We conclude that optimizing these conversational scaffolds is the primary lever for the next generation of general intelligence.
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