prompter: Hello,
I tried to use reinforcement learning (trlx library) to train a FLAN-T5. I have seen that as examples there are sentiment analysis or summarization tasks, but in my case it is a generic generation task. Trying to use my dataset, I tried the config: ppo_config_cnn_daily.yml.

The result I got is this, and it seems to be a summarization task:

Prompt: "[some text] ... from 1978, excluding finishing years incoming 62"
Output: "years incoming 62 incoming 62 incoming 62 incoming 62 incoming 62 incoming 62 incoming 62 incoming 62 incoming 62 incoming"
And I'm using this code with my personal prompts:

trlx.train(
    reward_fn=reward_fn,
    prompts=train_prompts,
    eval_prompts=valid_prompts,
    config=config
)
I have two problems, maybe caused by summarization task:

First the repetition at the end
Second, the expected output is very different
Should I change the code or are there generic examples?

My configuration is:
gen_kwargs:
    max_new_tokens: 100
  gen_experience_kwargs:
    max_new_tokens: 100
    do_sample: True
    temperature: 1.0
    top_k: 50
    top_p: 0.95

That works well for summarization. Any suggestion about how to change these parameters for generic text 2 text tasks and not summarization?

assistant: It seems that the problem you are facing is that the example configuration file you used (ppo_config_cnn_daily.yml) is designed for a summarization task and may not be suitable for your generic text generation task. Therefore, you may need to adjust the configuration file and hyperparameters to better suit your task.

Here are some suggestions for adjusting the configuration file and hyperparameters for a generic text generation task:

1. Adjust the maximum sequence length: In your gen_kwargs and gen_experience_kwargs sections, you have set the max_new_tokens parameter to 100. This sets the maximum number of tokens that can be generated for each prompt. Depending on the complexity of your text generation task, you may need to increase or decrease this value.

2. Adjust the sampling parameters: In your gen_kwargs section, you have set temperature to 1.0, top_k to 50, and top_p to 0.95. These parameters control the randomness and diversity of the generated text. Again, depending on your task, you may need to adjust these values to achieve the desired level of randomness and diversity.

3. Use a different reward function: The reward function you use will depend on the specific goals of your text generation task. For example, if you are generating product descriptions, you may want to use a reward function that maximizes the relevance and accuracy of the descriptions. If you are generating creative writing, you may want to use a reward function that maximizes the creativity and originality of the text.

4. Consider using a different architecture: The architecture of the FLAN-T5 model may not be optimal for your specific text generation task. You may want to experiment with different architectures or pre-trained models to see which one works best for your task.

Overall, the key is to experiment with different configurations and hyperparameters until you find the combination that works best for your specific text generation task.

assistant: Method for using RL to train a FLAN-T5 for generic text-to-text tasks:

1. Dataset selection: Select a dataset of generic text-to-text tasks that is representative of the task at hand. Ensure that the dataset is comprehensive and diverse, containing inputs and outputs that are representative of the task at hand. Avoid datasets that are biased towards a particular domain or style of writing.

2. Configuration selection: Modify the configuration parameters to achieve better results for generic text-to-text tasks. Consider the following parameters: max_new_tokens, do_sample, temperature, top_k, and top_p. To determine the appropriate values for these parameters, perform a thorough analysis of the task and dataset. Be aware that parameters that are effective for summarization tasks may not be the best for generic text-to-text tasks.

3. Training and evaluation: Train the FLAN-T5 using RL with the selected dataset and configuration parameters. Consider the trade-offs between RL and other training methods such as supervised learning or unsupervised learning. Evaluate the performance of the FLAN-T5 on a diverse set of inputs and outputs to ensure its effectiveness.

4. Conclusion: In conclusion, the use of RL to train a FLAN-T5 for generic text-to-text tasks is a complex process that requires careful consideration of the dataset, configuration parameters, training method, and evaluation process. A comprehensive and thorough analysis of these factors is necessary to achieve the best possible results. By following the methodology outlined above, you can improve the effectiveness of your FLAN-T5 for generic text-to-text tasks.

prompter: Would the same training process be applicable to other LLMs such as LLaMA as well?

assistant: Yes, the training process for reinforcement learning can be applied to other large language models (LLMs) such as LLaMA as well. However, the specific configuration and hyperparameters of the training process may need to be adjusted to account for differences in the architecture and performance of different LLMs. It is important to experiment with different configurations and hyperparameters to find the best settings for the particular LLM being used. Additionally, the training process may require more or less data depending on the LLM being used, and the quality and diversity of the data can also impact the performance of the model.

assistant: 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.