Tag: Reinforcement Learning from Human Feedback
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Bridging the Gap: Reinforcement Learning from Human Feedback
Large language models (LLMs) are incredibly powerful, capable of generating coherent and creative text. Yet, left to their own devices, they can sometimes produce undesirable outputs such as factual inaccuracies, harmful content, or just unhelpful responses. The crucial challenge is alignment: making these powerful AIs behave in a way that is helpful, harmless, and honest.…
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Master of Control: Understanding Proximal Policy Optimization (PPO)
In the dynamic world of Reinforcement Learning (RL), an agent learns to make sequential decisions by interacting with an environment. It observes states, takes actions, and receives rewards, with the ultimate goal of maximizing its cumulative reward over time. One of the most popular and robust algorithms for achieving this is Proximal Policy Optimization (PPO).…
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Teaching AI What’s Good: Understanding Reward Model Training
Large language models (LLMs) have achieved incredible feats in understanding and generating human-like text. However, their initial training primarily focuses on predicting the next word, not necessarily on being helpful, harmless, or honest. This is where Reward Model training comes into play, a critical step in aligning LLMs with nuanced human values, typically as part…