Corporate Sales Success Strategist

[rriiffaatt77]

These methods, such as MCTS, model the output as a sequence of nodes, which can be at the token or sentence level. for example: Token-level nodes: Each node corresponds to a Token in the generated sequence. Through MCTS, the model can explore different sequences of Tokens and ultimately generate a more coherent response. Sentence-level nodes: In complex reasoning tasks, each node can represent a complete sentence or step of reasoning, helping the model better cope with multi-step reasoning tasks. . Related works . Jason Wei's Chain of Thought Induces Reasoning in Large Language Models, also known as COT.



Main content: Through a series of brazil email list intermediate reasoning steps, the ability of large language models to perform complex reasoning can be significantly improved to fine-tune the model, which can naturally stimulate the reasoning ability of large language models. Emergence of chain-thinking ability: Chain-thinking reasoning ability is not possessed by all models, but gradually emerges as the scale of the model increases. For tasks that require multi-step reasoning, chain reasoning instructions can significantly improve model performance, especially on large language models. This method also provides new ideas for improving the interpretability and robustness of models. Through step-by-step reasoning, CoT requires the model to generate a series of intermediate reasoning steps before generating a final answer.



The process of generating this “chain of reasoning” helps to improve the reasoning ability of the model, especially in tasks such as mathematics and code generation. However, although CoT can generate intermediate steps, it does not teach the model how to think deeply about the connections between problems internally. Especially for tasks that are particularly complex and require multi-step reasoning planning, such reasonable intermediate CoT reasoning processes (Rationales) are more important. . Let’s check out the step-by-step by Ilia et al. Main content: Compares two methods for training large language models for complex reasoning: outcome supervision and process supervision, and makes the following main contributions: ) Process supervision is more efficient than outcome supervision Research shows that a reward model trained by process supervision is more reliable than outcome supervision and can solve 78.