💡Reinforced self-training uses synthetic data generated by the model itself to improve reward.
🔄The data is then filtered using a reward model to retain only high-quality examples.
⬆️The iterative process of generating and filtering data boosts the reward of the language model.
📈The improved language model aligns better with human preferences.
🔁The process of generating and improving data can be repeated multiple times to further enhance the language model.
What is reinforced self-training?
—Reinforced self-training is a procedure that makes reward go up in language models by generating synthetic data and training on it.
How does reinforced self-training work?
—It uses a iterative process of generating synthetic data, filtering it based on a reward model, and training the language model on the high-quality examples.
What is the purpose of filtering the data?
—Filtering the data ensures that only high-quality examples are used for training, improving the output of the language model.
Why is reinforced self-training beneficial?
—Reinforced self-training improves the language model's output by aligning it with human preferences, resulting in better performance.
Can the process of generating and improving data be repeated?
—Yes, the process can be repeated multiple times to further enhance the language model's reward and performance.
00:00Reinforced self-training is a procedure that improves reward in language models by generating synthetic data and training on it.
05:00The generated data is filtered using a reward model to retain only high-quality examples.
10:00The iterative process of generating and filtering data boosts the reward of the language model and aligns it better with human preferences.
15:00The process of generating and improving data can be repeated multiple times to further enhance the language model's reward and performance.
