Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning
arXiv:2607.01511v1 Announce Type: cross 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, while the generated reasoning traces are rarely reused as semi-supervised learning signals. In this report, we define \textbf{Semi-supervised Chain-of-Thought Learning} and propose \textbf{Semi-CoT}, a simple framework that uses unlabeled questions to construct pseudo reasoning supervision. Semi-CoT samples multiple p
The rapid advancement and widespread adoption of large language models are driving a continuous search for more efficient and robust learning paradigms, especially under real-world constraints of limited labeled data.
This research introduces a novel method to enhance large language model reasoning capabilities with less human supervision, potentially accelerating AI development and deployment by reducing reliance on extensive, costly data annotation.
The ability to leverage semi-supervised learning for chain-of-thought reasoning shifts the bottleneck away from purely supervised methods, enabling more scalable and adaptive AI training processes.
- · AI developers and researchers
- · Companies with limited labeled data
- · Large Language Models (LLMs)
- · Purely supervised CoT methods
- · Manual data annotation services (long-term)
More sophisticated and efficient large language models with enhanced reasoning abilities will emerge faster.
The cost and time required to develop and deploy advanced AI systems will decrease, democratizing access to powerful AI capabilities.
This could accelerate the development of autonomous AI agents capable of complex problem-solving in various domains with minimal human input.
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Read at arXiv cs.LG