CoLoRA: A Competitive Learning Approach for Enhancing LoRA
Parametric-efficient fine-tuning
Yes
No ethics review needed.
1. Could the authors clarify how CoLoRA differentiates itself from other competitive or multi-component frameworks, such as those seen in recent work on MultiLoRA or m-LoRA? Specifically, how does CoLoRA’s competitive learning provide unique benefits over these methods, both in terms of architecture and practical outcomes?
2. Could the authors provide more insights into how the selector’s decision-making is validated, especially under varying task complexities or noisy data environments? Additional results or qualitative analysis here could better illustrate the selector’s effectiveness and robustness.
3. How would CoLoRA perform in more practical, multi-task scenarios? Testing in a realistic multi-task setting or domain-specific application (e.g., medical or legal language tasks) would better substantiate CoLoRA’s practical utility.
4. Could the authors include or discuss comparisons with baselines like MultiLoRA and m-LoRA to ensure that CoLoRA’s claimed improvements are significant and unique?
5. How do individual LoRA components contribute to overall performance in the CoLoRA framework? A more detailed ablation or component-level analysis could reveal how each competing LoRA component affects the model’s learning process and generalization.
1. Dynamic Selector Mechanism: The dynamic selection mechanism is designed to assess the performance of each LoRA component, selecting the best-performing component dynamically. This theoretically enables a more targeted model adaptation, which may contribute to achieving a balance between efficiency and expressiveness.
2. Extensive Experiments Across Benchmarks: The paper provides a variety of experimental results across benchmarks like commonsense reasoning and the MMLU, which serve to showcase the claimed advantages of CoLoRA. These evaluations attempt to establish CoLoRA’s capacity to perform well across a range of tasks, adding empirical evidence to support its potential robustness.
3. Comparison with Established Methods: CoLoRA is compared not only with standard LoRA but also with mixtures of experts (MoE) methods. This situates CoLoRA within the broader context of parameter-efficient fine-tuning techniques, allowing readers to gauge its potential advantages and limitations relative to other established approaches.
This paper presents the Competitive Low-Rank Adaptation (CoLoRA) method, aimed at enhancing the Low-Rank Adaptation (LoRA) framework for fine-tuning large language models (LLMs). Traditional LoRA models often face limitations: a single low-rank configuration lacks capacity, while higher ranks introduce inefficiency. CoLoRA attempts to address this by introducing multiple competing LoRA components and a dynamic selection mechanism to evaluate them during training, aiming to improve model adaptability without increasing inference complexity. The primary contributions claimed by the authors include establishing a competitive learning framework for LoRA, implementing a selector to optimize component selection, and demonstrating CoLoRA’s purported advantage over conventional LoRA in tasks without additional inference overhead. However, the practical significance of these contributions remains unclear, and it is debatable whether the competitive framework provides a tangible benefit over simpler LoRA configurations. Furthermore, the experiments, while suggesting performance gains, may lack sufficient depth in real-world application scenarios to substantiate CoLoRA’s effectiveness fully.
1. Limited novelty in competitive learning mechanism: The competitive learning framework introduced here lacks substantial novelty, as similar multi-component strategies have been explored in recent works on parameter-efficient fine-tuning. For example, recent studies on multi-component or competitive mechanisms that optimize LoRA components across tasks potentially reduce the novelty of CoLoRA’s contribution [1, 2].
2. Insufficient evaluation on practical multi-task scenarios: While the paper presents evaluations on commonsense reasoning and language understanding tasks, it lacks assessment in realistic, multi-task settings. The effectiveness of CoLoRA’s competitive learning could be better demonstrated through domain-specific applications or real-world multi-task benchmarks, as seen in recent research [3]. Adding experiments in diverse, practical applications would strengthen the paper’s claim of broad applicability.
3. Overlooked baseline comparisons: Although the paper compares CoLoRA against standard LoRA and certain MoE models, it does not include several relevant baselines that implement advanced multi-component LoRA optimizations, as presented in recent studies [2, 3]. Including these baselines would provide a more balanced evaluation, helping to demonstrate if CoLoRA truly offers unique advantages or whether existing methods meet or exceed its performance.
4. Lack of detailed analysis on selector mechanism: The dynamic selector is a core component of CoLoRA, yet there is minimal analysis on its decision-making process or robustness across task complexities. A deeper exploration of how the selector operates in various scenarios, along with sensitivity testing, would add value and practical insight to the claims of its efficacy. Such analysis could also improve the model’s interpretability and assist in refining the selector’s parameters for better performance across diverse tasks.

Ref:
[1] Q. Liu, X. Wu, X. Zhao, Y. Zhu, D. Xu, F. Tian, and Y. Zheng, "When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications."
[2] Y. Wang, Y. Lin, X. Zeng, and G. Zhang, "MultiLoRA: Democratizing LoRA for Better Multi-Task Learning."
[3] Z. Ye, D. Li, J. Tian, T. Lan, Y. Liang, Y. Jiang, J. Zuo, H. Lu, L. Duan, and M. Tang, "m-LoRA: Efficient LLM Model Fine-tune and Inference via Multi-LoRA Optimization."
Yes
No ethics review needed.
1.	How does the training overhead of CoLoRA compare to LoRA, MoELoRA, and full fine-tuning? Quantitative data on training time, resource usage, and convergence rates would clarify the practical feasibility of CoLoRA.

2.	Why is there no direct comparison between CoLoRA and MoELoRA in terms of inference efficiency? Can you provide specific data on inference speed, parameter load, and memory consumption?

3.	How does the competitive mechanism benefit LoRA, and are there unique modifications (e.g., loss functions, selection criteria) that make this approach innovative? Could you provide a theoretical justification for its use?
1. Attempt to Address LoRA's Limitations: CoLoRA aims to improve LoRA by adding competitive learning, which, in theory, can improve model adaptability without increasing inference costs.
2. Empirical Results on Language Tasks: The experiments show that CoLoRA performs better than baseline LoRA configurations on a range of language tasks, which is promising in terms of model capability within this domain.
This manuscript introduces CoLoRA, a competitive learning framework designed to improve the expressiveness and effectiveness of LoRA (Low-Rank Adaptation) in fine-tuning large language models. CoLoRA deploys multiple low-rank components that compete during training, with the most effective component selected for inference. The authors claim that CoLoRA outperforms traditional LoRA configurations on commonsense reasoning and multitask language understanding tasks while maintaining inference efficiency.
1. Training Overhead Not Addressed: A core objective of LoRA is to reduce training and computational costs. By introducing multiple components and a competitive selection mechanism, CoLoRA inherently increases the complexity and computational demand during training. The paper lacks any quantitative comparison of training overhead relative to LoRA, MoELoRA, or full fine-tuning (FFT), making it difficult to assess CoLoRA’s practicality in real-world scenarios. Without a thorough comparison of training times, convergence rates, and hardware requirements, the claimed efficiency is unsubstantiated.

2. Lack of Quantitative Comparison with MoELoRA in Inference Efficiency: The paper claims that CoLoRA achieves higher inference efficiency than MoELoRA by selecting only a single component for inference. However, the authors do not provide any quantitative evidence to substantiate this claim. Given that both frameworks target parameter efficiency, a direct comparison in terms of inference time, memory usage, and computational cost is necessary to validate CoLoRA’s purported advantage.

3. Weak Theoretical Justification for the Competitive Mechanism: The paper lacks a rigorous theoretical foundation explaining why competitive learning should significantly enhance LoRA performance. There is no analysis of how the competitive mechanism improves selection or how it specifically addresses LoRA's limitations. Without a theoretical framework
Yes
No ethics review needed.
As well as addressing the weaknesses described above the authors might also consider:

- "The CoLoRA’s code will be released later." Why not release the code now? 
- Fonts in Figure 1 are much too small making it very difficult to follow.
- Table 1 is not easy to follow. Perhaps Low, Medium, High could be used instead of the current symbols? 
- The unit (accuracy, f-score, ...) being used in Table 2 and other results tables is never stated. It should be.
The main strengths of the paper are:

- The CoLoRA apporach is a novel and effective extension of LoRA. 

- The CoLoRA approach is clearly explained and includes interesting ideas well grounded in the literature. 

- The experiments are carefully designed and properly executed. 

- The ablation studies are comprehensive. 

- Models trained using the CoLoRA approach clearly out-perform models trained using standard LoRA.
This paper introduces CoLoRA, an extension of the LoRA approach that uses ideas from mixture of expert models. Rather than train a single LoRA component the CoLoRA approach trains multiple LoRA components, allowing them to compete during training. The technique is clearly described and a series of experiments are performed to compare the performance of models trained using CoLoRA with models trained using LoRA. These evaluation experiments show that the models trained using CoLoRA outperform those trained using LoRA alone. A series of ablation studies also show the value of different parts of the approach.
The main weaknesses of the paper are:

- Although computational efficiency is claimed as an advantage of CoLoRA no evaluation of computational requirements is included. The paper would really benefit from this. 

- The performance measures used in Tables 2-10 are never stated. This would really help. 

- The paper would benefit from some more careful review and revision.
Yes
No ethics review needed.
- How does your approach compare with MoE and LoRA works discussed in the Weaknesses section?
- What is the time complexity for equation 4? Do we have to do multiple forward passes through the entire model to get each LoRA's LM loss?  
- How does your approach compare to the naive approach of fine-tuning multiple vanilla LoRAs (i.e., have multiple individual training runs)? 
- In Table 6, as the number of LoRAs increases the performance does not continue to improve. Can the authors provide insights as to why a selector model is needed. Would using a stronger selector (i.e., with more parameters) improve performance?
The competitive learning approach to train LoRA is quite interesting. The authors show that it consistently outperforms vanilla LoRA on a variety of tasks.
The paper proposes a competitive learning approach for training LoRA. Multiple LoRAs are initialized in parallel. A selector model is trained to select the most suitable LoRA for a given input. 
During training, the LoRAs and selector are trained jointly using the following losses:
1) the standard LM loss is applied for each LoRA. 
2) the top-N selected LoRAs will get an additional loss gradient update. This is similar to weighing the LoRAs LM loss. The authors refer to this as an alignment loss.
3) The selector is trained using a pairwise loss to ensure that it selects LoRAs with the best LM loss. 

Once training finishes, the model is evaluated on a validation dataset. The LoRA that is selected the most is then chosen as the LoRA to be used for production/downstream tasks.
- Given that the main contribution is training a more performant LoRA, the paper does not compare with MoE approaches or newer LoRA varints (eg., AdaLoRA [1], DoRA [2]). In particular, the LoRA variants have to be compared given their simpler training complexity and similar inference overhead. 
- In equation 4, the LM loss is calculated individually for each LoRA. A naive approach or simple baseline that the authors can consider is having multiple individual training runs. We would also be able to get multiple LoRAs and choose the best performing one using the validation set.
- I am not sure how useful the selector is given the lack of analysis on what the selector is doing. Unlike MoE, there is no specialization or ensembling being carried out. Since the selector is trained to select the best LoRAs, there might be a risk of certain LoRAs being undertrained. In addition, performance does not scale when more LoRAs are used (Table 6). 
- Only using the winner LoRA and discarding the other LoRAs feels kind of wasteful. The authors can consider merging the LoRAs together in a linear/non-linear combination based on the performance on the validation dataset.

[1] Zhang, Q., Chen, M., Bukharin, A., Karampatziakis, N., He, P., Cheng, Y., ... & Zhao, T. (2023). AdaLoRA: Adaptive budget allocation for parameter-efficient fine-tuning. arXiv preprint arXiv:2303.10512.
[2] Liu, S. Y., Wang, C. Y., Yin, H., Molchanov, P., Wang, Y. C. F., Cheng, K. T., & Chen, M. H. (2024). Dora: Weight-decomposed low-rank adaptation. arXiv preprint arXiv:2402.09353.
@inproceedings{
anonymous2024colora,
title={CoLo{RA}: A Competitive Learning Approach for Enhancing Lo{RA}},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=jFcNXJGPGh},
note={under review}
}
We propose a Competitive Low-Rank Adaptation (CoLoRA) framework to address the limitations of the LoRA method, which either lacks capacity with a single rank-$r$ LoRA or risks inefficiency and overfitting with a larger rank-$Kr$ LoRA, where $K$ is an integer larger than 1. The proposed CoLoRA method initializes $K$ distinct LoRA components, each with rank $r$, and allows them to compete during training. This competition drives each LoRA component to outperform the others, improving overall model performance. The best-performing LoRA is selected based on validation metrics, ensuring that the final model outperforms a single rank-$r$ LoRA and matches the effectiveness of a larger rank-$Kr$ LoRA, all while avoiding extra computational overhead during inference. To the best of our knowledge, this is the first work to introduce and explore competitive learning in the context of LoRA optimization. The CoLoRA's code will be released later.
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CoLoRA: A Competitive Learning Approach for Enhancing LoRA
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