Generalization to Additional LLM Model Families (e.g., Llama)

Determine whether SkillReducer’s compression and restructuring retain functional quality and token-efficiency benefits on additional large language model families such as Llama, beyond the five models from four families evaluated in the paper.

Background

The evaluation reports cross-model transfer across five models spanning four families, with an average retention of 0.965, and shows robustness when the compressor model is changed. Despite this, the authors highlight that several prominent model families were not included in the experiments.

Establishing whether SkillReducer’s results extend to additional families (e.g., Llama) remains an open question, especially given potential differences in context handling, tool-use capabilities, and susceptibility to distraction effects across model architectures.