Self-Supervised Learning

What if an LLM could learn not by reading more, but by thinking harder? That’s the radical premise behind Self-Questioning Language Models (SQLM), a framework that transforms large language models from passive learners into active generators of their own training data. No curated datasets. No labeled answers. Just a prompt — and a model that gets smarter by challenging itself. From Self-Play in Robotics to Reasoning in Language The inspiration for SQLM comes from asymmetric self-play, a technique used in robotics where one agent proposes tasks and another learns to solve them. Here, that paradigm is adapted to LLMs: ...

Cognaptus Insights introduces Retrieval-Augmented Learning (RAL), a new approach proposed by Zongyuan Li et al.¹, allowing large language models (LLMs) to autonomously enhance their decision-making capabilities without adjusting model parameters through gradient updates or fine-tuning. Understanding Retrieval-Augmented Learning (RAL) RAL is designed for situations where fine-tuning large models like GPT-3.5 or GPT-4 is impractical. It leverages structured memory and dynamic prompt engineering, enabling models to autonomously refine their responses based on previous interactions and validations. ...