arXiv:2512.22671v3 Announce Type: replace-cross Abstract: Structured width pruning of GLU-MLP layers in Llama-3.2 models, guided by the Peak-to-Peak Magnitude (PPM) criterion, reveals a systematic dichotomy in how reducing the expansion ratio affects different model capabilities. While performance on tasks relying on parametric knowledge (e.g., MMLU, GSM8K) and perplexity metrics degrades predictably with decreasing expansion ratios, instruction-following capabilities improve at the 2.4x equilibrium ratio (IFEval: +4.8 points / +46% in Llama-3.2-1B and +3.7 points / +39% in Llama-3.2-3B), and
Ongoing research into large language model (LLM) efficiency and performance optimization is revealing fundamental trade-offs in model architecture, making this discovery timely.
This research reveals that instruction-following capabilities in LLMs can paradoxically improve even as parametric knowledge degrades, challenging assumptions about model scaling and optimal design.
The understanding of LLM pruning is refined, suggesting that models optimized for certain task types, like instruction-following, may not require maximum parameter counts or traditional performance metrics.
- · AI researchers and developers
- · Companies using LLMs for agentic tasks
- · Hardware manufacturers focused on efficient inference
- · Developers solely focused on maximizing parametric knowledge scores
- · Over-parameterized LLM architectures for specific use cases
More specialized and efficient LLM architectures will emerge, tailored for specific instruction-following applications.
This could accelerate the development and deployment of AI agents that are highly proficient in understanding and executing complex commands.
The focus on instruction-following efficiency over raw knowledge might lead to new evaluation benchmarks and design philosophies for AI, influencing future research directions significantly.
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Read at arXiv cs.AI