arXiv:2606.03080v1 Announce Type: new Abstract: Causal language models factorize sequence probabilities using only preceding context, leaving future information unexploited during training despite its availability in the training data. This paper introduces Regret Pre-training, a self-supervised framework grounded in the Learning Using Privileged Information (LUPI) paradigm. The framework employs a dual-view architecture in which a single model generates both a causal Student distribution and a future-conditioned Teacher distribution. The training objective augments standard language modeling
The continuous push for more efficient and robust pre-training methods in large language models drives innovation at the foundational research level, exemplified by new conceptual frameworks like Regret Pre-training.
This research introduces a novel training paradigm that could significantly enhance the knowledge grounding and overall performance of causal language models by leveraging future context during training.
Current causal language models, which primarily rely on preceding context, could be superseded by models incorporating 'Regret Pre-training,' leading to more capable and context-aware AI systems.
- · AI research institutions
- · Large language model developers
- · Cloud AI providers
- · Data scientists
- · Developers of less efficient pre-training methods
- · Companies unable to adapt to new training paradigms
Improved performance and reduced training costs for advanced AI models.
Faster development cycles for specialized AI applications and agents due to more robust foundational models.
Increased competition among foundational model providers as new architectures emerge, potentially democratizing access to powerful AI capabilities.
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Read at arXiv cs.CL