arXiv:2606.06021v1 Announce Type: new Abstract: On-policy distillation (OPD) supervises the student only in output space by matching next-token probabilities. This output-only paradigm has two limits: (1) sampling variance from Monte Carlo KL estimates over large vocabularies (e.g., Qwen's ~150k tokens) persists throughout training, and (2) it treats the teacher as a black-box, discarding all intermediate hidden states after the LM head. We propose On-Policy Representation Distillation (OPRD), which lifts distillation into hidden-state space by aligning student and teacher representations acro
The paper directly addresses known limitations in current on-policy distillation techniques for large language models, suggesting a timely technical advancement.
Improving distillation efficiency and effectiveness is crucial for developing smaller, more deployable, and computationally less demanding AI models, lowering barriers to entry and accelerating iteration.
The focus shifts from output-only supervision to representation-level alignment, potentially leading to more robust and higher-performing smaller models derived from larger teachers.
- · AI model developers
- · Companies seeking to deploy custom, efficient LLMs
- · Hardware manufacturers benefiting from increased model deployment
- · None
More efficient and cost-effective deployment of advanced AI capabilities.
Accelerated development cycles for specialized AI agents and applications due to easier model customization and deployment.
Increased proliferation of highly capable, smaller AI models contributing to the 'AI Agents' narrative.
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Read at arXiv cs.LG