LionW Outperforms AdamW in LoRA and Full Fine-Tuning Tasks

Table of Links

Abstract and 1 Introduction

2 Background

3 Experimental Setup and 3.1 Datasets for Continued Pretraining (CPT) and Instruction Finetuning (IFT)

3.2 Measuring Learning with Coding and Math Benchmarks (target domain evaluation)

3.3 Forgetting Metrics (source domain evaluation)

4 Results

4.1 LoRA underperforms full finetuning in programming and math tasks

4.2 LoRA forgets less than full finetuning

4.3 The Learning-Forgetting Tradeoff

4.4 LoRA’s regularization properties

4.5 Full finetuning on code and math does not learn low-rank perturbations

4.6 Practical takeaways for optimally configuring LoRA

5 Related Work

6 Discussion

7 Conclusion and References

Appendix

A. Experimental Setup

B. Learning rate searches

C. Training Datasets

D. Theoretical Memory Efficiency Gains with LoRA for Single and Multi-GPU Settings

A Experimental Setup

Code CPT.

Math CPT.

Code IFT.

Math IFT. Same as code IFT, except that

• maximum sequence length = 1024

We compared the two optimizers by training for two epochs of Magicoder-Evol-Instruct-110K using different learning rates. We found that Decoupled LionW outperformed DecoupledAdamW on HumanEval for both LoRA and full finetuning, and across learning rates, as seen in Fig. S1.

B Learning rate searches

For IFT we find that LoRA LRs should be an order of magnitude higher. For the longer CPT, these effects are more subtle.

B.1 Learning rate sensitivity analysis across optimizers

Figure S4: Same data as Fig. 3

Figure S5: Same data as in Fig. 4 plotted for individual tasks HellaSwag, ARC-Challenge and WinoGrande

1500 is needed to explain 90% of the variance.">