Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
arXiv:2602.21103v2 Announce Type: replace Abstract: Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To address these limitations, we introduce Prompt-Level Distillation (PLD). We extract explicit reasoning patterns from a Teacher model and organize them into a structured list of expressive instructions for the Student model's System Pr
The increasing computational demands of large language models for advanced reasoning are driving research into more efficient prompting and training methods.
This development offers a non-parametric alternative to fine-tuning, potentially democratizing access to advanced AI reasoning by reducing computational costs and preserving model interpretability.
Prompt-Level Distillation (PLD) changes how advanced reasoning capabilities from large 'Teacher' models can be transferred to smaller 'Student' models, reducing latency and inference costs without full fine-tuning.
- · AI developers
- · Companies with limited compute budgets
- · Edge AI applications
- · Users of advanced AI
- · AI compute providers (potentially reduced demand for inference)
- · Traditional large-scale fine-tuning services
Wider deployment of advanced reasoning AI due to reduced operational costs.
Increased competition among AI service providers as barriers to entry are lowered.
Acceleration of AI integration into diverse business processes currently constrained by cost or latency.
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Read at arXiv cs.CL