arXiv:2606.05315v1 Announce Type: new Abstract: Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empirically find that hidden-state reasoning trajectories exhibit low-rank structure. Motivated by this observation, we propose a low-rank distillation framework that transfers reasoning by aligning teacher and student trajectories in a shared low-rank tensor subspace using first- and second-order statistics. The resulting formulation captures the global structure of reasoning while supporting a compact
The paper addresses current challenges in optimizing implicit reasoning in large language models, a key pursuit in the rapid development cycle of AI capabilities.
This development could significantly enhance the efficiency and performance of AI models by improving internal reasoning mechanisms without explicit prompting, accelerating their practical application.
The proposed low-rank distillation framework offers a new technical approach to transfer and embed complex reasoning trajectories into more compact, performant AI models.
- · AI developers
- · Large Language Model providers
- · Cloud computing providers
- · AI research institutions
- · Inefficient conventional AI training methods
- · Organizations relying solely on explicit CoT prompting
Improved performance and efficiency of large language models for complex tasks requiring implicit reasoning.
Faster development and deployment of advanced AI applications across various industries due to more capable and compact models.
Reduced compute costs and energy consumption for running sophisticated AI, making advanced AI more accessible and sustainable.
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