A process for choosing the learning rate

Start with commonly used learning rate values:

• Begin by considering the learning rate values commonly used in practice for LLM fine-tuning, such as 2e-5 or 5e-6 (as shown in Table VI of the paper).

• These values can serve as a starting point for your learning rate exploration.

Perform a grid search or random search:

• Utilize LRBench++ or a similar tool to perform a grid search or random search over a range of learning rate values.

• Grid search involves manually selecting a set of learning rate values to evaluate, while random search randomly samples learning rate values from a predefined range.

• The paper suggests that grid search based on top-k recommendations from LRBench++ can lead to better accuracy results compared to random search (Table IV).

Evaluate different learning rate policies:

• Experiment with various learning rate policies, such as fixed learning rate, linear decay, and cosine decay.

• The paper's findings (Table V) show that different learning rate policies can lead to varied performance on different NLP tasks when fine-tuning LLaMA-7B.

• Consider evaluating both fixed learning rate policies and decaying learning rate policies to identify the best-performing one for your specific LLM and task.

Monitor and compare performance metrics:

• During the fine-tuning process, monitor and compare the performance metrics of the LLM on the target task(s) for different learning rate values and policies.

• The paper evaluates fine-tuned LLMs on four representative tasks: ARC, HellaSwag, MMLU, and TruthfulQA (Table V).

• Select the learning rate value and policy that yield the best performance on your target task(s).

Consider the trade-off between performance and efficiency:

• Evaluate the impact of learning rate on both the performance and efficiency of LLM fine-tuning.

• The paper suggests that learning rate tuning can potentially enhance LLM fine-tuning efficiency by reducing the number of fine-tuning epochs required to achieve optimal performance (Table V).

• Strike a balance between achieving the desired performance and minimizing the computational cost and time required for fine-tuning.

Iterate and refine:

• Based on the initial results, you may need to iterate and refine your learning rate selection.

• Adjust the range of learning rate values explored or experiment with additional learning rate policies to further optimize the fine-tuning process.

• Continue monitoring the performance metrics and consider the trade-offs between performance and efficiency until you find the most suitable learning rate configuration for your LLM and task.

Remember that the optimal learning rate value and policy may vary depending on the specific LLM architecture, dataset, and target task. It's essential to conduct experiments and evaluate the impact of different learning rates on your particular setup.

Additionally, keep in mind the challenges and opportunities highlighted in the paper, such as the need for cost-effective learning rate tuning, accurate benchmarking of learning rates, and the potential limitations of relying solely on training/validation loss for evaluating LLM performance during fine-tuning.

By following this framework and considering the insights provided in the paper, you can systematically approach the selection of learning rates for fine-tuning large language models, leading to improved performance and efficiency.