The training process outlined for FLAN-T5 can serve as a general guideline for training other language models as well. However, the specific details of the training process may need to be adjusted based on the architecture and characteristics of the language model being used.

For example, different language models may require different values for the configuration parameters or different training methods. Additionally, the choice of dataset may also need to be adjusted to suit the specific task and characteristics of the language model being used.

Therefore, while the overall methodology for training a language model using RL for generic text-to-text tasks may be similar, the specific details of the training process may need to be tailored to the specific language model being used.
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