arXiv:2605.26844v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts with token-level teacher supervision. Recent selective OPD methods exploit the non-uniformity of OPD signals by prioritizing high-entropy or high-disagreement tokens. We revisit this principle and ask: which token-level teacher signals are actually learnable? Using a fixed-context diagnostic that measures same-context teacher-student KL reduction, we show that raw KL disagreement is a coarse proxy for learning value. It conflates learnable disagreement, where the teacher assigns co
This research is part of ongoing efforts to refine AI training techniques, specifically addressing the efficiency and effectiveness of knowledge distillation in large language models as the field matures.
Improving on-policy distillation directly impacts the efficiency of training smaller, more performant AI models, which can accelerate AI development and reduce computational costs.
The understanding of 'learnability' in token-level supervision for on-policy distillation is refined, guiding future research and practical application towards more effective training signals.
- · AI researchers
- · AI foundational model developers
- · Companies seeking efficient AI deployment
- · Inefficient AI training methodologies
- · Models reliant on naive distillation techniques
More efficient and resource-friendly methods for training sophisticated AI models emerge.
This efficiency could accelerate the deployment of advanced AI agents by reducing compute and development cycles.
Widely available, highly performant, and efficiently trained AI models contribute to broader adoption and potentially transform various industry sectors.
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