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This research introduces PLaT, a framework that separates latent planning from verbalization in Large Language Models to reduce computational costs and avoid premature token commitments. By allowing problem-dependent chain lengths and providing multiple textual readouts for verification, the method improves efficiency on mathematical reasoning tasks like GSM8K without sacrificing accuracy. The work advances the field of machine learning by optimizing how models perform complex reasoning within continuous hidden states before generating discrete text.
Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization
arXiv:2601.21358v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and early token commitments in discrete reasoning traces. Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning within continuous hidden states. However, many such methods optimize latent states end to end without a trained interface for intermediate textual readout, and several representative configurations use a pre-defined number of latent steps during inference. In this work, we introduce \textbf{PLaT} (\textbf{P}lanning with \textbf{La}tent \textbf{T}houghts), a framework that decouples latent planning from verbalization. The Planner deterministically evolves latent planning states, while an independent Decoder provides textual readouts when needed. Answer-aware textual stopping allows the latent rollout to use a problem-dependent number of groups rather than a pre-specified chain length. PLaT achieves competitive coverage at larger $k$ in several mathematical settings, with lower Pass@1: on Llama-1B GSM8K, it reaches 80.59\% Pass@128 versus CODI's 72.37\%. These results support PLaT as a candidate-generation interface supplying multiple textual readouts for downstream verification or reranking.
Abstract:Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and early token commitments in discrete reasoning traces. Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning within continuous hidden states. However, many such methods optimize latent states end to end without a trained interface for intermediate textual readout, and several representative configurations use a pre-defined number of latent steps during inference. In this work, we introduce \textbf{PLaT} (\textbf{P}lanning with \textbf{La}tent \textbf{T}houghts), a framework that decouples latent planning from verbalization. The Planner deterministically evolves latent planning states, while an independent Decoder provides textual readouts when needed. Answer-aware textual stopping allows the latent rollout to use a problem-dependent number of groups rather than a pre-specified chain length. PLaT achieves competitive coverage at larger $k$ in several mathematical settings, with lower Pass@1: on Llama-1B GSM8K, it reaches 80.59\% Pass@128 versus CODI's 72.37\%. These results support PLaT as a candidate-generation interface supplying multiple textual readouts for downstream verification or reranking.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2601.21358 [cs.AI]
(or arXiv:2601.21358v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2601.21358 Focus to learn more arXiv-issued DOI via DataCite
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