Computer Science > Computation and Language
arXiv:2602.08220 (cs)
[Submitted on 9 Feb 2026 (v1), last revised 10 Mar 2026 (this version, v2)]
Title:Pretraining with Token-Level Adaptive Latent Chain-of-Thought
Authors:Boyi Zeng, Yiqin Hao, He Li, Shixiang Song, Feichen Song, Zitong Wang, Siyuan Huang, Yi Xu, ZiWei He, Xinbing Wang, Zhouhan Lin
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Abstract:Scaling large language models by increasing parameters and training data is increasingly constrained by limited high-quality corpora and rising communication costs. This work explores an alternative axis: increasing per-token computation without expanding parameters, by internalizing latent Chain-of-Thought (CoT) into pretraining. We propose Pretraining with Token-Level Adaptive Latent CoT (adaptive latent CoT), where the model generates a variable-length latent CoT trajectory before emitting each token -- allocating longer trajectories to difficult tokens and shorter (or even zero) trajectories to easy ones. Importantly, this behavior emerges naturally from one-stage pretraining on general text and reduces computation in both training and inference via token-wise adaptive halting. Experiments with Llama architectures show that adaptive latent CoT consistently improves language modeling perplexity and broad downstream accuracy, even with fewer training FLOPs than prior recurrent baselines.
| Comments: | |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2602.08220 [cs.CL] |
| (or arXiv:2602.08220v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.08220
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Submission history
From: Yiqin Hao [view email][v1] Mon, 9 Feb 2026 02:49:15 UTC (5,197 KB)
[v2] Tue, 10 Mar 2026 05:18:24 UTC (5,197 KB)
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View a PDF of the paper titled Pretraining with Token-Level Adaptive Latent Chain-of-Thought, by Boyi Zeng and 10 other authors
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