Position: Introspective Experience from Conversational Environments as a Path to Better Learning

TL;DR

This paper proposes that robust reasoning in AI emerges from social interaction and linguistic self-reflection, emphasizing the importance of dialogue quality and introspective experiences in learning.

Contribution

It introduces a new perspective that learning from conversational environments and dialogue quality are key to developing better reasoning in AI, inspired by Vygotskian psychology.

Findings

Social interaction refines reasoning processes.

Dialogically scaffolded introspection enables decoupling from immediate data.

Dialogue quality correlates with private reasoning depth.

Abstract

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.