LegoLM: Structured Weight Sharing for Large Language Models
Quick summary
arXiv:2608.08652v1 Announce Type: cross Abstract: We present \LegoLM{}, a structured weight-sharing compression framework for large language models grounded in a systematic study of why global weight sharing fails and how to fix it. We identify two distinct failure modes. Distributional mismatch: for vector blocks of dimension d
Key takeaways
- arXiv:2608.08652v1 Announce Type: cross Abstract: We present \LegoLM{}, a structured weight-sharing compression framework for large language models grounded in a systematic study of why global weight sharing fails and how to fix it.
- We identify two distinct failure modes.
- Distributional mismatch: for vector blocks of dimension d
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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