**Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking** **Abstract:** This paper introduces Quiet-STaR, a method for language models to learn to generate rationales at each token to explain future text, improving their predictions. Building on the Self-Taught Reasoner (STaR), Quiet-STaR generalizes STaR by training language models to infer unstated rationales in arbitrary text, rather than just curated question-answering datasets. The approach addresses challenges such as computational cost, initial lack of internal thoughts, and the need to predict beyond individual tokens. Key contributions include a token-wise parallel sampling algorithm and an extended teacher-forcing technique. Experiments show that Quiet-STaR improves zero-shot reasoning abilities on datasets like GSM8K and CommonsenseQA, with improvements scaling with the number of internal thoughts. These results validate the effectiveness of teaching language models to reason more generally and scalably. **Introduction:** The paper discusses the implicit reasoning in text and the potential for language models to learn from this reasoning. It highlights the limitations of existing methods that rely on curated datasets or specific reasoning tasks. Quiet-STaR aims to address these issues by training models to generate and use internal thoughts, improving their ability to predict future text. The method involves generating rationales after each token, mixing predictions with and without thoughts, and optimizing rationale generation using REINFORCE. Experiments demonstrate significant improvements in zero-shot reasoning capabilities, with longer internal thoughts leading to better outcomes. The approach also shows promise in generating more structured and coherent chains of thought. **Related Work:** The paper reviews related work on reasoning in language models, including methods that exploit pre-trained models or generate chain-of-thought solutions. It also discusses the use of custom tokens and the challenges of training models to reason from diverse unstructured text data. **Conclusion:** Quiet-STaR represents a significant step towards language models that can learn to reason more generally and scalably. The results demonstrate the potential of this approach in improving downstream reasoning performance and generating meaningful rationales. Future work could explore ensemble thoughts and dynamic allocation of compute during generation.**Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking** **Abstract:** This paper introduces Quiet-STaR, a method for language models to learn to generate rationales at each token to explain future text, improving their predictions. Building on the Self-Taught Reasoner (STaR), Quiet-STaR generalizes STaR by training language models to infer unstated rationales in arbitrary text, rather than just curated question-answering datasets. The approach addresses challenges such as computational cost, initial lack of internal thoughts, and the need to predict beyond individual tokens. Key contributions include a token-wise parallel sampling algorithm and an extended teacher-forcing technique. Experiments show that Quiet-STaR improves zero-shot reasoning abilities on datasets like GSM8K and CommonsenseQA, with improvements scaling with the number of internal thoughts. These results validate the effectiveness of teaching language models to reason more generally and scalably. **Introduction:** The paper discusses the implicit reasoning in text and the potential for language models to learn from this reasoning. It highlights the limitations of existing methods that rely on curated datasets or specific reasoning tasks. Quiet-STaR aims to address these issues by training models to generate and use internal thoughts, improving their ability to predict future text. The method involves generating rationales after each token, mixing predictions with and without thoughts, and optimizing rationale generation using REINFORCE. Experiments demonstrate significant improvements in zero-shot reasoning capabilities, with longer internal thoughts leading to better outcomes. The approach also shows promise in generating more structured and coherent chains of thought. **Related Work:** The paper reviews related work on reasoning in language models, including methods that exploit pre-trained models or generate chain-of-thought solutions. It also discusses the use of custom tokens and the challenges of training models to reason from diverse unstructured text data. **Conclusion:** Quiet-STaR represents a significant step towards language models that can learn to reason more generally and scalably. The results demonstrate the potential of this approach in improving downstream reasoning performance and generating meaningful rationales. Future work could explore ensemble thoughts and dynamic allocation of compute during generation.