Deepseek's recent success highlights the potential of Large Language Models (LLMs) in AI, especially through Reinforcement Learning (RL). The article explains the stages of LLM training: pre-training on extensive datasets, supervised fine-tuning for targeted outputs, and finally, RL which focuses on aligning models with human feedback. Key RL methods, such as TRPO, PPO, and GRPO, are introduced in an accessible manner, making the concepts approachable even for those with basic Machine Learning knowledge. The essence of RL is illustrated through a robot navigating a maze, conceptualizing how agents learn from their actions and rewards.
Reinforcement Learning (RL) is a key focus in training Large Language Models, enhancing their alignment with human preferences through feedback mechanisms.
The three steps of training Large Language Models include pre-training on large datasets, supervised fine-tuning for specific tasks, and reinforcement learning for alignment.
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