arXiv:2607.04846v1 Announce Type: cross Abstract: Transformers follow implicit curricula whereby some tasks are learned before others. However, how explicit pretraining curricula influence learning, generalization, and the selectivity of fine-tuning is unclear. This is important for AI safety, where fine-tuning is used to selectively suppress misaligned behaviors. Here, we compare curricula that pretrain tasks in a balanced (sampled uniformly) or an imbalanced (one task early, the other late) fashion. We show that imbalanced learning of two conflicting copy tasks promotes in-context learning a
The increasing sophistication and widespread deployment of large language models necessitate advanced methods for controlling their behavior and ensuring alignment.
This research provides a mechanism for improving AI safety by enabling more selective and robust fine-tuning, directly addressing the challenge of suppressing undesirable AI behaviors.
The ability to strategically sequence pretraining tasks offers a new lever for influencing model learning and behavior, moving beyond simple fine-tuning techniques.
- · AI safety researchers
- · Developers of AI agents
- · AI platform providers
- · Malicious actors exploiting misaligned AI
Improved methods for aligning AI models with human values, reducing unintended consequences.
Accelerated development and adoption of AI agents in sensitive applications due to enhanced safety and control.
Potential for specialized, highly controlled AI systems in critical infrastructure or defense applications, impacting geopolitical stability.
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Read at arXiv cs.AI