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Robots, Small Models, and RL with DeepSeek Alumnus Zihan Wang — #86

Manifold

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Reinforcement Learning and Model Efficiency

This chapter explores the complexities of reinforcement learning (RL) as it relates to automating problem-solving and enhancing model efficiency. It discusses the limitations and performance constraints of pre-trained models, while questioning the role of model size and data availability in achieving accurate predictions. Additionally, the conversation touches on the financial implications of advanced AI model development and the potential of RL in advancing towards artificial general intelligence.

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