Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains

• URL: http://arxiv.org/abs/2605.25745v1
• Date: Mon, 25 May 2026 11:57:09 GMT
• Title: Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains
• Abstract summary: Explicit chain-of-thought (CoT) reasoning substantially improves the reasoning ability of large language models.<n>Existing latent reasoning methods treat reasoning as uniformly compressible, causing precision-critical intermediate steps to be overly compressed.<n>We propose Selective Latent Thinking (SLT), a framework that selectively compresses redundant reasoning spans into latent representations.
• Score: 9.979461886634022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explicit chain-of-thought (CoT) reasoning substantially improves the reasoning ability of large language models (LLMs), but incurs high inference cost due to lengthy autoregressive traces. Existing latent reasoning methods offer a promising alternative, yet they often treat reasoning as uniformly compressible, causing precision-critical intermediate steps to be overly compressed and thereby degrading reasoning accuracy. In this work, we propose Selective Latent Thinking (SLT), a framework that selectively compresses redundant reasoning spans into latent representations while preserving precision-critical spans as explicit CoT within the same reasoning trajectory. Specifically, SLT first uses a lightweight decoder to anticipate a short upcoming reasoning span, and then applies confidence-based gating to determine the longest span that can be reliably compressed. The accepted span is encoded into a compact latent representation to improve reasoning efficiency, while uncertain or precision-critical reasoning remains in explicit CoT form to preserve accuracy. To learn this selective compression policy, SLT adopts a three-stage training strategy that combines span-level latent compression, reliability-aware future reasoning prediction, and trajectory-level reinforcement learning to optimize the trade-off between answer correctness and reasoning cost. Extensive experiments across four mathematical reasoning benchmarks demonstrate that SLT achieves 22.7\% higher accuracy than latent reasoning baselines at comparable compression ratios, while reducing reasoning chain length by 58.4\% with only 2.8\% accuracy degradation compared to explicit CoT,Our code can be found in https://github.com/hunshi34/SLT.

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