LLMs can Compress LLMs: Adaptive Pruning by Agents
TL;DR
An LLM acts as a coach to prune another LLM, shrinking it ~45% while preserving key knowledge and accuracy.
Traditional pruning uses fixed rules and often wipes out facts. This paper lets a foundation model adaptively choose which layers to trim each round. It reads layer sensitivity snapshots—combining weight–activation cues (à la Wanda) with gradient importance—normalized for easy comparison. The agent self-reflects on past pruning outcomes and adjusts. If quality (perplexity) drops too much, a rollback restores the last good checkpoint.
- Results on Qwen3 4B/8B at ~45% sparsity: 56% relative better MMLU accuracy vs structured pruning, 19× stronger factual retention on FreebaseQA, and 69% less perplexity degradation.
- No retraining, model-agnostic, and only 2–4 rollbacks across 21–40 iterations.
Takeaway: foundation models can intelligently compress other foundation models—cutting costs without gutting knowledge.
Paper by Sai Varun Kodathala and Rakesh Vunnam. Link: https://arxiv.org/abs/2601.09694v1
Paper: https://arxiv.org/abs/2601.09694v1
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AI LLM ModelCompression Pruning SparseML MMLU Qwen Factuality Efficiency NLP
