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SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs

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arxiv 2502.12134 v2 pith:JQX5JYAL submitted 2025-02-17 cs.CL

Yige Xu , Xu Guo , Zhiwei Zeng , Chunyan Miao This is my paper
classification cs.CL
keywords reasoningllmschain-of-thoughtcontinuous-spacefine-tuningrequiresoftsoftcot
verification ladder T0 review T1 audit T2 compute T3 formal
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Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks by generating intermediate reasoning steps. However, most existing approaches focus on hard token decoding, which constrains reasoning within the discrete vocabulary space and may not always be optimal. While recent efforts explore continuous-space reasoning, they often require full-model fine-tuning and suffer from catastrophic forgetting, limiting their applicability to state-of-the-art LLMs that already perform well in zero-shot settings with a proper instruction. To address this challenge, we propose a novel approach for continuous-space reasoning that does not require modifying the LLM. Specifically, we employ a lightweight fixed assistant model to speculatively generate instance-specific soft thought tokens as the initial chain of thoughts, which are then mapped into the LLM's representation space via a trainable projection module. Experimental results on five reasoning benchmarks demonstrate that our method enhances LLM reasoning performance through supervised, parameter-efficient fine-tuning. Source code is available at https://github.com/xuyige/SoftCoT.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning

    eess.AS 2026-03 unverdicted novelty 7.0 of 10

    FLAIR enables spoken dialogue AI to conduct continuous latent reasoning while perceiving speech through recursive latent embeddings and an ELBO-based finetuning objective.

  2. Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning

    cs.CL 2026-01 unverdicted novelty 7.0 of 10

    RoT renders CoT reasoning text as images and aligns them via VLM vision encoders to achieve 3-4x token compression and faster inference with competitive accuracy.

  3. Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression

    cs.LG 2026-02 unverdicted novelty 6.0 of 10

    Extra-CoT trains a semantic compressor on math CoT data, applies mixed-ratio SFT, and uses CHRPO reinforcement learning to achieve over 73% token reduction on MATH-500 with 0.6% accuracy gain on Qwen3-1.7B.

  4. LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning

    physics.chem-ph 2026-02 conditional novelty 6.0 of 10

    LatentChem reasons in continuous latent space for chemistry, achieving a 59.88% non-tie win rate over explicit CoT on ChemCoTBench with a 10.84x average reduction in reasoning overhead.

  5. Chain Of Thought Compression: A Theoretical Analysis

    cs.AI 2026-01 conditional novelty 6.0 of 10

    Compressing reasoning into latent states is theoretically hard because high-order interaction gradients decay, and latent-explicit alignment (ALiCoT) mitigates this.

  6. AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control

    cs.CL 2025-06 conditional novelty 6.0 of 10

    AALC, an accuracy-aware length reward, reduces reasoning-model response length by over 50% on math benchmarks while preserving or improving accuracy.

  7. Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment

    cs.AI 2026-07 conditional novelty 5.0 of 10

    PUMA detects when a reasoning model's entropy drop aligns with hidden-state momentum, truncates at that point, and reports improved accuracy-efficiency on 1.5B-32B reasoning models.

  8. Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs

    cs.AI 2025-07 conditional novelty 5.0 of 10

    The survey's L1/L2 taxonomy and benchmark show that current reasoning models waste compute on easy problems and underthink hard ones, motivating more adaptive inference.

  9. Toward Efficient Agents: Memory, Tool learning, and Planning

    cs.AI 2026-01 conditional novelty 3.0 of 10

    A survey that organizes efficiency techniques for LLM agents into memory, tool learning, and planning, and consolidates benchmarks and metrics for measuring cost-performance trade-offs.

  10. Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

    cs.AI 2025-07 conditional novelty 3.0 of 10

    A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.

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