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@@ -11,7 +11,7 @@ Lyceum is a Reinforcement Learning (RL) playground designed for natural language
 
 ## Why Lyceum?
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-Modern NLP solutions like GPTs and BERTs have made great strides in language processing and generations, however they come with serious limitations. Even though a LLM can describe or make a game of chess, and even justify moved made, it is unable to *play* it. Why? Because there's no underlying mechanism for decision-making or reward-incentives during training. Transformers rely on static token distributions without real-time feedback, limiting their capacity to *actively* learn.
+Modern NLP solutions like GPTs and BERTs have made great strides in language processing and generation, however they come with serious limitations. Even though a LLM can describe or make a game of chess, and even justify moved made, it is unable to *play* it. Why? Because there's no underlying mechanism for decision-making or reward-incentives during training. Transformers rely on static token distributions without real-time feedback, limiting their capacity to *actively* learn.
 
 Lyceum tries to address this gap by shifting the focus to active learning through reinforcement. In Lyceum, the agent doesn't just passively learn to generate language; it learns through *interaction*.