Tag: Reinforcement Learning
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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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Unleashing Autonomous Intelligence: Exploring the World of Agentic AI
The field of artificial intelligence constantly pushes boundaries, and a particularly exciting area is Agentic AI. Moving beyond reactive systems, Agentic AI focuses on creating intelligent agents that can perceive, make autonomous decisions, take actions, and learn to achieve specific goals. Imagine AI that not only processes information but also proactively solves problems and navigates…