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LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging

Kari Jaaskelainen

— 1 min read
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging

TL;DR

LoRA adapters are small plug-in modules that fine-tune big language models cheaply. But one adapter per task doesn’t fit messy, real-world inputs.

What’s new

LoRA on the Go (LoGo) lets a model pick and blend the best adapters for each individual input—no labels, no extra training, no slowdown.

How it works

  • Runs a single forward pass through available LoRA adapters.
  • Uses simple signals to judge which adapters matter.
  • Dynamically selects and merges them on-the-fly.

Why it matters

  • Training-free and plug-and-play.
  • Handles mixed, unpredictable tasks.
  • Keeps inference throughput.
  • Across 5 benchmarks, 27 datasets, 3 model families: competitive overall and up to 3.6% better than training-heavy methods on some tasks.

Paper: http://arxiv.org/abs/2511.07129v1

Paper: http://arxiv.org/abs/2511.07129v1

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