Cognitive Training for Language Models: Towards General Capabilities via Cross-Entropy Games

2026-03-23

AI Summary

This study addresses the challenge of automating the development of general-purpose capabilities in language models through curriculum learning. It proposes a "cognitive training" framework that treats language models as agents and leverages cross-entropy games to generate task curricula, iteratively expanding their skill boundaries via greedy optimization combined with a meta-sampling mechanism. The core contribution lies in the first formalization of the meta-objective for building general capabilities, whose uniqueness—under natural assumptions and involving only a few hyperparameters—is rigorously established, thereby providing a theoretical foundation for curriculum learning. This work identifies cognitive training as a viable pathway toward autonomous, continuous discovery and evolution of skills in language models.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Defining a constructive process to build general capabilities for language models in an automatic manner is considered an open problem in artificial intelligence. Towards this, we consider the problem of building a curriculum of tasks that grows a model via relevant skill discovery. We provide a concrete framework for this task, using a family of tasks called cross-entropy games, which we postulate is universal in a suitable sense. We show that if it is possible to grow the curriculum for relevant skill discovery by iterating a greedy optimization algorithm, then, under natural assumptions, there is essentially only one meta-objective possible (up to a few hyperparameters). We call the resulting process cognitive training. We postulate that, given sufficiently capable language models as players and meta-samplers and sufficient training time, cognitive training provides a principled way to relevant skill discovery; and hence to the extent general capabilities are achievable via greedy curriculum learning, cognitive training would be a solution.

Problem

• cognitive training
• language models
• general capabilities
• curriculum learning
• skill discovery

Innovation

• cognitive training
• cross-entropy games
• curriculum learning
• skill discovery
• language models