def __init__(
        self,
        config: "PPOConfig",
        reward_model: Union[str, "transformers.pipeline"],
        length_sampler_kwargs: dict,
        generation_kwargs: dict,
    ) -> None:
        """
        Additional arguments for PPO training process.

        Args:
            reward_model (Union[str, "transformers.pipeline"]): Reward model to use for creating PPO rewards for training.
            length_sampler_kwargs (dict): Arguments for the length sampler.
                min_value: Minimum length for the generated samples.
                max_value: Maximum length for the generated samples.
            generation_kwargs (dict): Arguments for the generation process.
                min_length: Minimum length for the generated samples.
                max_length: Maximum length for the generated samples.
                num_beams: Number of beams to use for the generation process.
                num_return_sequences: Number of sequences to generate.
                temperature: Temperature for the generation process.
                top_k: Top k for the generation process.
                top_p: Top p for the generation process.
        """
        if isinstance(reward_model, str):
            from transformers import pipeline

            reward_model = pipeline("text-classifcation", model=reward_model)
        self.config = config
        self.reward_model = reward_model
        self.length_sampler_kwargs = length_sampler_kwargs
        self.generation_kwargs = generation_kwargs

        Additional arguments for PPO training process.

        Args:
            reward_model (Union[str, "transformers.pipeline"]): Reward model to use for creating PPO rewards for training.
            length_sampler_kwargs (dict): Arguments for the length sampler.
                min_value: Minimum length for the generated samples.
                max_value: Maximum length for the generated samples.
            generation_kwargs (dict): Arguments for the generation process.
                min_length: Minimum length for the generated samples.
                max_length: Maximum length for the generated samples.
                num_beams: Number of beams to use for the generation process.
                num_return_sequences: Number of sequences to generate.
                temperature: Temperature for the generation process.
                top_k: Top k for the generation process.
                top_p: Top p for the generation process.
        
argilla-v1/src/argilla_v1/client/feedback/training/frameworks/trl.py