Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2609.07406v1
• Date: Mon, 07 Sep 2026 12:18:51 GMT
• Title: Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning
• Abstract summary: Chain-of-thought (CoT) reasoning improves the reasoning ability of large language models by introducing intermediate computation.<n>However, explicit rationales increase decoding length, latency, and context cost.<n>Implicit CoT offers a more efficient alternative by moving intermediate reasoning into continuous latent states.
• Score: 3.216436532980238
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Chain-of-thought (CoT) reasoning improves the reasoning ability of large language models by introducing intermediate computation, but explicit rationales increase decoding length, latency, and context cost. Implicit CoT offers a more efficient alternative by moving intermediate reasoning into continuous latent states. However, latent reasoning can be unstable: successive latent states may become overly similar and collapse toward a shared dominant direction, reducing the diversity of the reasoning trajectory. In this work, we identify $\textit{latent rank collapse}$ and propose $\textbf{WIDER}$, a lightweight spectral regularizer for implicit CoT. During training, WIDER estimates the shared direction of each latent trajectory and penalizes projections onto this direction, encouraging latent states to span a broader representational subspace. The method is plug-and-play and leaves the backbone model, latent schedule, and inference-time decoding procedure unchanged. We further formulate this collapse as a geometric bottleneck in implicit reasoning, casting its mitigation as a training-time regularization problem rather than an inference-time decoding change. Extensive experiments show that WIDER improves matched implicit CoT baselines, while mechanistic analyses reveal higher effective rank, lower dominant-direction energy, and reduced redundancy among latent steps. These results highlight latent subspace utilization as an important factor for efficient continuous reasoning, providing a geometric perspective for analyzing and improving implicit CoT. Code is available at https://github.com/whitesweater/WIDER.

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