https://arxiv.org/abs/2412.06769 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2412.06769 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2412.06769 (cs) [Submitted on 9 Dec 2024] Title:Training Large Language Models to Reason in a Continuous Latent Space Authors:Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, Yuandong Tian View a PDF of the paper titled Training Large Language Models to Reason in a Continuous Latent Space, by Shibo Hao and 6 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are restricted to reason in the "language space", where they typically express the reasoning process with a chain-of-thought (CoT) to solve a complex reasoning problem. However, we argue that language space may not always be optimal for reasoning. For example, most word tokens are primarily for textual coherence and not essential for reasoning, while some critical tokens require complex planning and pose huge challenges to LLMs. To explore the potential of LLM reasoning in an unrestricted latent space instead of using natural language, we introduce a new paradigm Coconut (Chain of Continuous Thought). We utilize the last hidden state of the LLM as a representation of the reasoning state (termed "continuous thought"). Rather than decoding this into a word token, we feed it back to the LLM as the subsequent input embedding directly in the continuous space. Experiments show that Coconut can effectively augment the LLM on several reasoning tasks. This novel latent reasoning paradigm leads to emergent advanced reasoning patterns: the continuous thought can encode multiple alternative next reasoning steps, allowing the model to perform a breadth-first search (BFS) to solve the problem, rather than prematurely committing to a single deterministic path like CoT. Coconut outperforms CoT in certain logical reasoning tasks that require substantial backtracking during planning, with fewer thinking tokens during inference. These findings demonstrate the promise of latent reasoning and offer valuable insights for future research. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2412.06769 [cs.CL] (or arXiv:2412.06769v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2412.06769 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shibo Hao [view email] [v1] Mon, 9 Dec 2024 18:55:56 UTC (11,057 KB) Full-text links: Access Paper: View a PDF of the paper titled Training Large Language Models to Reason in a Continuous Latent Space, by Shibo Hao and 6 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.CL < prev | next > new | recent | 2024-12 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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