Reflections on Pretraining and Fine-Tuning in Reinforcement Learning
The world of reinforcement learning (RL) is continuously advancing and a recent study, titled "Reflections on Pretraining and Fine-Tuning in Reinforcement Learning" (source), further emphasizes the significance of pretraining. The authors surprisingly don't discuss open sourcing the weights, raising questions about their stance on knowledge sharing.
The study suggests that constructing a high-quality dataset for instruction fine-tuning could outshine larger, but less balanced datasets. This process could be optimized through a crowdsourcing approach to lessen prompt author overlap, thereby increasing the diversity of phrasing. The authors argue that with sufficient quality, you could develop an instruction model that excels at role-playing, storytelling, or other specific tasks.
The process the authors used involved training with prompts that described how they intended to solve a problem, followed by the actual solution. They speculate that the improvement comes from a type of step-by-step reasoning process. However, this approach could be equally effective for story writing prompts that include an initial planning explanation, with the main text being the visible output.
While the name of the study might be slightly misleading, its core idea focuses on the notion that the final fine-tuning of the Large Language Model (LLM) to transform into a chatbot forms a lesser part of the overall training. In essence, the optimal way to train an LLM is to expend most resources on the encoder and decoder language training, with only a small percentage focused on the final instruction fine-tuning. This concept aligns with other research in the field, but serves as a beneficial reminder.
Overall, the paper is an intriguing read and is reminiscent of early experiments with base llama acting as an assistant. It fosters hope for more experiments using this approach, leading to further advancement in reinforcement learning.
Tags: Reinforcement Learning, Pretraining, Fine Tuning,LLM