arXiv:2509.25837v3 Announce Type: replace Abstract: Large language models (LLMs) deliver remarkable performance but are costly to deploy, motivating knowledge distillation (KD) for efficient inference. Existing KD objectives typically match student and teacher probabilities via softmax, which blurs valuable logit information. While direct logit distillation (DLD) mitigates softmax smoothing, it fails to account for logit shift invariance, thereby restricting the solution space. We propose Concrete Score Distillation (CSD), a discrete score-matching objective that overcomes both softmax-induced
The increasing scale and cost of Large Language Models necessitate more efficient deployment methods, making distillation techniques a high-priority research area.
Efficient distillation methods like CSD can significantly reduce the computational and energy costs associated with deploying powerful AI models, broadening their accessibility and application.
The proposed Concrete Score Distillation (CSD) offers a more robust and efficient method for AI model distillation, potentially allowing for smaller, faster, and cheaper LLMs without significant performance loss.
- · AI compute providers
- · Developers of custom LLMs
- · Startups utilizing LLMs
- · Users of AI-powered applications
- · Companies reliant on large, expensive LLM deployments
More efficient LLMs become widely deployable across various edge devices and resource-constrained environments.
The reduced cost of inference for LLMs could accelerate the development and adoption of AI agents and personalized AI experiences.
Increased LLM accessibility might democratize advanced AI capabilities, leading to unforeseen applications and a more competitive AI ecosystem globally.
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