arXiv:2606.09471v1 Announce Type: new Abstract: On-policy distillation (OPD) provides dense token-level supervision by asking a teacher to score student-generated rollouts. However, when the student drifts into an unrecoverable prefix, the teacher may locally agree with the degraded state, producing low reverse KL but little corrective training signal. We identify this persistent regime as a low-KL agreement trap. Further analyses show that tokens during and after such traps produce less useful supervision signals. We propose KAT (KL Agreement Trap Termination), an online OPD termination rule
This research addresses a critical limitation in on-policy distillation, a technique gaining prominence for efficient large language model training, as the field pushes for more robust and scalable AI development.
Improving on-policy distillation means more efficient and effective training of AI models, which can accelerate the development and deployment of advanced AI systems and agents.
The identification and proposed solution for the 'KL Agreement Trap' will lead to more stable and performant on-policy distillation techniques, directly improving model quality and training efficiency.
- · AI researchers
- · Generative AI developers
- · Cloud AI providers
- · Inefficient AI training methods
- · Organizations with limited compute budgets
On-policy distillation becomes a more reliable tool for training large language models.
This improvement could lead to faster iteration cycles and more sophisticated capabilities for AI agents.
More efficient AI development could lower the barrier to entry for certain advanced AI applications, democratizing access to powerful models.
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Read at arXiv cs.LG