# FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts

arXiv: https://arxiv.org/abs/2510.08396 · HTML: https://mlsanity.com/paper/2510.08396/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Mixture of LoRA experts, one of 12 directions in Mixture-of-experts.
- Builds on 75 of its 98 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Placed in

- Mixture-of-experts / [Mixture of LoRA experts](https://mlsanity.com/moe/mixture-of-lora-experts/)

## Builds on (most cited first, 30 of 75)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [Group Sequence Policy Optimization](https://mlsanity.com/paper/2507.18071/) (2025)
- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://mlsanity.com/paper/2311.03099/) (2023)
- [Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://mlsanity.com/paper/2012.13255/) (2020)
- [Merging Models with Fisher-Weighted Averaging](https://mlsanity.com/paper/2111.09832/) (2021)
- [Measuring the Intrinsic Dimension of Objective Landscapes](https://mlsanity.com/paper/1804.08838/) (2018)

## Built on by (most cited first, 30 of 35)

- [Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning](https://mlsanity.com/paper/2510.16882/) (2025)
- [Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning](https://mlsanity.com/paper/2510.16877/) (2025)
- [Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning](https://mlsanity.com/paper/2602.04998/) (2026)
- [TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning](https://mlsanity.com/paper/2606.11119/) (2026)
- [Disentangling Task Conflicts in Multi-Task LoRA via Orthogonal Gradient Projection](https://mlsanity.com/paper/2601.09684/) (2026)
- [Towards Specialized Generalists: A Multi-Task MoE-LoRA Framework for Domain-Specific LLM Adaptation](https://mlsanity.com/paper/2601.07935/) (2026)
- [CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging](https://mlsanity.com/paper/2603.00573/) (2026)
- [NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation](https://mlsanity.com/paper/2603.12378/) (2026)
- [Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection](https://mlsanity.com/paper/2603.19145/) (2026)
- [Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA](https://mlsanity.com/paper/2602.20492/) (2026)
- [Representation Collapse in Sequential Post-Training of Large Language Models](https://mlsanity.com/paper/2605.30524/) (2026)
- [Predicting Mergeability of Parameter-Efficient Fine-Tuning Updates](https://mlsanity.com/paper/2606.19549/) (2026)
- [Spend Experts Where You Are Unsure: Confidence-Adaptive Routing for Mixture-of-Experts LoRA](https://mlsanity.com/paper/2607.26052/) (2026)
- [Uncertainty Is Not Enough: Value-of-Information Routing for Mixtures of LoRA Experts](https://mlsanity.com/paper/2608.02528/) (2026)
- [Zipper-LoRA: Dynamic Parameter Decoupling for Speech-LLM based Multilingual Speech Recognition](https://mlsanity.com/paper/2603.17558/) (2026)
- [The Long-Term Effects of Data Selection in LLM Fine-Tuning](https://mlsanity.com/paper/2605.30537/) (2026)
- [Online Data Selection Is Implicit Alignment](https://mlsanity.com/paper/2607.07023/) (2026)
- [FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models](https://mlsanity.com/paper/2602.08818/) (2026)
- [CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies](https://mlsanity.com/paper/2602.15367/) (2026)
- [Early Intervention for VFM-based Multimodal Medical Image Classification](https://mlsanity.com/paper/2603.17514/) (2026)
- [M2-VLA: Boosting Vision-Language Models for Generalizable Manipulation via Layer Mixture and Meta-Skills](https://mlsanity.com/paper/2604.24182/) (2026)
- [MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation](https://mlsanity.com/paper/2605.17743/) (2026)
- [Knowledge-Preserved Model Tuning in Null-Space for Robust Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2606.03539/) (2026)
- [Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning](https://mlsanity.com/paper/2606.22878/) (2026)
- [Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation](https://mlsanity.com/paper/2607.03522/) (2026)
- [Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning](https://mlsanity.com/paper/2607.20913/) (2026)
- [PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification](https://mlsanity.com/paper/2608.15504/) (2026)
- [SPARCL: Spectral Partitioned Analytic Continual Learning](https://mlsanity.com/paper/2608.21307/) (2026)
- [LoRA-RC: Reservoir Computing with Low-Rank Adaptation](https://mlsanity.com/paper/2609.12327/) (2026)
- [Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents](https://mlsanity.com/paper/2609.12896/) (2026)

## Abstract (arXiv)

Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for foundation models, but it suffers from parameter interference, resulting in suboptimal performance. Although Mixture-of-Experts (MoE)-based LoRA variants show promise in mitigating intra-task correlations in single-task instruction tuning, they introduce additional router parameters and remain ineffective in multi-task model merging where inter-task interference arises. Inspired by the fly olfactory circuit, we propose FlyLoRA, an implicit MoE-based LoRA variant that introduces: (1) rank-wise expert activation in the up-projection matrix, and (2) an implicit router that unifies expert routing and down-projection, where a frozen sparse random projection matrix replaces the traditional dense trainable version. This design resolves the trade-off between intra-task decorrelation and computational efficiency by eliminating the need for an explicit router, while inherently mitigating inter-task interference due to the orthogonality property of random matrices. Extensive experiments across four domains -- general knowledge understanding, scientific question answering, mathematical reasoning, and code generation -- demonstrate consistent performance improvements over existing methods. Beyond empirical gains, FlyLoRA highlights how biological structures can inspire innovations in AI technologies. Code is available at https://github.com/gfyddha/FlyLoRA.