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A central piece in enabling intelligent agentic behavior in foundation models is to make them capable of introspecting upon their behavior, reasoning, and correcting their mistakes as more computation or interaction is available.
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Jan Peters and Stefan Schaal · 2007
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Offline rl for natural language generation with implicit language q learning
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Solving math word problems with process-and outcome-based feedback
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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Language agent tree search unifies reasoning acting and planning in language models
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