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LOCUS: Task-Aware Low-Rank Post-Training for Token-Efficient Language Generation
Large language model serving costs scale directly with output sequence length, yet standard preference alignment often inflates response verbosity without improving utility. We study whether the parameterization of post-training updates affects generation length: low-rank subspaces alter sequence le
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