Comments (3)
observaton_list is a 2d array for all available input,it will be like observaton_list = [['Got any creative ideas for a 10 year old’s birthday?']], and you can either use a causal lm or seq2seq lm in hugging face. here is a small example: https://voidful.dev/jupyter/2022/12/10/textrl-elon-musk.html
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@voidful what purpose does observation list serve. I am just trying to understand .
And is this RL or RLHF ?
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observation_list
holds the observations that the agent receives from the environment at each step of the RL training. These observations are used by the agent to make decisions and to learn from the environment.
In an RLHF setup, you would need to collect human feedback for the generated text, and then use that feedback as the reward signal to guide the model's learning. You can make this adaptation by changing the reward module in TextRL to incorporate human feedback as the basis for the reward calculation. By doing so, you will create an RLHF setup using TextRL.
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Related Issues (20)
- Backward compatibility HOT 2
- About the compare_sample HOT 1
- i get error when i use elon example HOT 6
- Text generation models generating repeated/duplicate text/sentences. HOT 3
- Are there any examples for T5 or Bart? Why T5 and bart give the same output before/after training? HOT 2
- Support for AutoModelForSeq2SeqLM HOT 2
- Update interval HOT 3
- Documentation on Methodology HOT 1
- Support for other PFRL Algorithms HOT 2
- Text generation after period/full-stop (".")
- ValueError: Expected parameter logits HOT 5
- Reward policy agent environment is not training with Finetuned model HOT 1
- Does the package support automatic multi-gpu? HOT 2
- unfreeze_layer_from_past parameter HOT 4
- Problems in the inference process
- Fix Reward Calculation in `example/2022-12-10-textrl-elon-musk.ipynb` HOT 1
- AttributeError: 'MyRLEnv' object has no attribute 'num_envs' HOT 2
- AttributeError: module 'numpy' has no attribute '_no_nep50_warning' HOT 1
- It needs a license HOT 1
- AssertionError HOT 3
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