Demystifying When Pruning Works via Representation Hierarchies
Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. However, this expectation does not consistently hold across language tasks: pruned models can perform well on non-generative tasks but frequently fail in generative settings. To understand this discrepancy, we analyze network pruning from a representation-hierarchy perspective, decomposing the internal computation of language models into three sequential spaces: embedding (hidden representations), logit (pre-softmax outputs), and probability (post-softmax distributions). We find that representations in the embedding and logit spaces are largely r...
| Authors | Shwai He, Guoheng Sun, Haichao Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 11 May 2026 |
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