Papers › Balancing LoRA Performance and Efficiency with Simple Shard Sharing
Balancing LoRA Performance and Efficiency with Simple Shard Sharing
Jiale Kang, Qingyu Yin
Parameter-Efficient Fine-Tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), effectively reduce the number of trainable parameters in Large Language Models (LLMs). However, as model scales continue to grow, the demand for computational resources remains a significant challenge. Existing LoRA variants often struggle to strike an optimal balance between adaptability (model performance and convergence speed) and efficiency (computational overhead, memory usage, and initialization time). This paper introduces FOSSIL(\textbf{F}ramework for \textbf{O}ptimal \textbf{S}hard \textbf{S}haring \textbf{I}ntegration in \textbf{L}oRA), a novel PEFT approach that addresses this trade-off through a simple shard-sharing mechanism. FOSSIL leverages the insight that a low-rank adaptation can be achieved by decomposing the weight matrix into multiple fragment matrices and utilizing a shared, trainable common fragment. This method constructs the low-rank update matrix through the replication of these shared, partitioned shards. We also propose a hardware-efficient and broadly applicable implementation for FOSSIL. Extensive experiments conducted on a range of tasks, alongside a systematic analysis of computational performance, demonstrate FOSSIL's superiority. The results show that FOSSIL significantly outperforms standard LoRA and its prominent variants in both model performance metrics and computational efficiency, including initialization speed and training throughput. By effectively balancing expressive power and resource utilization, FOSSIL offers a compelling solution for efficiently adapting large-scale models.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections