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Sparse Rewards in NLP
In many Natural Language Processing (NLP) applications, such as machine translation, rewards are often sparse. This means that the agent receives a non-zero reward signal only after completing an entire sequence, like generating a full sentence. For all intermediate steps (e.g., generating individual words), the reward is zero ( for ), which can make learning challenging.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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