Cognitive Training for Language Models: Towards General Capabilities via Cross-Entropy Games
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 hyper-parameters). We call the r...
| Authors | Clément Hongler, Franck Gabriel, Valentin Hartmann et al. |
|---|---|
| Primary category | math.OC |
| Published | 23 March 2026 |
| Last updated at source | 12 April 2026 |
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