Scaling Laws for Task-Specific LLM Distillation

Abstract:Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications where latency and cost constraints are critical. This paper derives empirical scaling laws for domain-specific LLM compression, quantifying how in-domain and general knowledge performance scale with dataset size, compression ratio, supervision format, and iterative pruning schedule. Using quantitative finance as our application domain, we compare logit-based and LoRA-based distillation under iterative structural pruning, introducing a blended chain-of-thought supervision loss that stabilizes KL-divergence distillation over reasoning traces. In-domain task quality degrades predictably under compression while general-knowledge benchmarks collapse well before the same point; supervision format is the key driver of this tradeoff, with chain-of-thought supervision actively recovering general knowledge that pruning erases. We release the headline dataset FinHeadlineMix, scaling law results, and practical recommendations to provide a reusable framework for domain-specific compression decisions.

Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)

arXiv:2606.24747 [cs.AI]

(or arXiv:2606.24747v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.24747 Focus to learn more arXiv-issued DOI via DataCite

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