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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.22479 |
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| _version_ | 1866908956633333760 |
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| author | Hongler, Clément Gabriel, Franck Hartmann, Valentin Renard, Arthur Emil, Andrew |
| author_facet | Hongler, Clément Gabriel, Franck Hartmann, Valentin Renard, Arthur Emil, Andrew |
| contents | 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 resulting process cognitive training.
We postulate that, given sufficiently capable language models as players and meta-samplers, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22479 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cognitive Training for Language Models: Towards General Capabilities via Cross-Entropy Games Hongler, Clément Gabriel, Franck Hartmann, Valentin Renard, Arthur Emil, Andrew Optimization and Control Artificial Intelligence 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 resulting process cognitive training. We postulate that, given sufficiently capable language models as players and meta-samplers, 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. |
| title | Cognitive Training for Language Models: Towards General Capabilities via Cross-Entropy Games |
| topic | Optimization and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2603.22479 |