研究人员推出了一种新颖的半监督思维链学习框架Semi-CoT，该框架利用未标记问题生成伪推理监督。该方法通过基于估计的答案级语义熵来选择可靠的推理链，从而改进了CoT的自训练方法。虽然实验在选择高精度伪CoT方面显示出潜力，但有效利用仍需要改进演示选择或学生训练策略。 AI
影响 这项研究可能通过利用未标记数据来提高LLM的推理能力，从而实现更有效的LLM训练。
排序理由 该集群包含两篇学术论文，讨论了LLM和MLLM中思维链推理的新方法和数据集。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2607.01511v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, most existing CoT methods use reasoning chains mainly as inference-time prompts, w…
arXiv:2511.17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs). Yet, its potential in multimodal large language models (MLLMs) remains largely untapped, hin…
Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, most existing CoT methods use reasoning chains mainly as inference-time prompts, while the generated reasoning traces are rarely r…