mlsanityData to 7 Oct 2026

LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking

arXiv 2508.03440 ↗First posted 5 Aug 2025cs.CL cs.AIcited by 34

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Human cognition naturally engages with abstract and fluid concepts, whereas existing reasoning models often rely on generating discrete tokens, potentially constraining their expressive capabilities. Recent advancements aim to address this limitation by enabling large language models (LLMs) to generate soft, abstract tokens, thus facilitating reasoning within a continuous concept space. In this paper, we investigate the Soft Thinking capabilities of various LLMs through a systematic analysis of their internal behavior using a suite of probing techniques. Contrary to the prevailing belief that Soft Thinking supports parallel exploration of diverse reasoning paths, our findings reveal that LLMs behave as single-threaded reasoners--they predominantly rely on the token with the highest probability in the soft input to predict the next step. This behavior induces a greedy feedback loop that suppresses alternative reasoning paths and undermines the benefits of transmitting richer information via Soft Tokens. To address this Greedy Pitfall, we propose Stochastic Soft Thinking, which introduces stochasticity to break free from this Greedy Pitfall. Our experiments demonstrate that incorporating randomness--particularly with the Gumbel-Softmax trick--can alleviate the limitations of vanilla approaches and unleash the potential of Soft Thinking, resulting in superior performance across eight reasoning benchmarks. We further demonstrate that Stochastic Soft Thinking exhibits stronger exploration potential compared to conventional COT. Our findings deepen the understanding of continuous reasoning and establish the foundation for future work on improving Soft Thinking with Reinforcement Learning.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 24 of its 45 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

24 of 45 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Skywork Open Reasoner 1 Technical Report28 May

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  6. 2021Program Synthesis with Large Language Modelscited by 3,509
  7. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  8. 2024OpenAI o1 System Cardcited by 1,220
  9. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 948
  10. 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 946
  11. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 763
  12. 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approachcited by 372
11 more
  1. 2025REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalizationcited by 233
  2. 2024From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Stepcited by 225
  3. 2025CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillationcited by 216
  4. 2023Implicit Chain of Thought Reasoning via Knowledge Distillationcited by 165
  5. 2025Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Spacecited by 109
  6. 2025Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chainscited by 81
  7. 2025Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thoughtcited by 73
  8. 2025Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Trainingcited by 42
  9. 2025Continuous Chain of Thought Enables Parallel Exploration and Reasoningcited by 32
  10. 2024Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoningcited by 21
  11. 2025Text Generation Beyond Discrete Token Samplingcited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 26.

34 papers.

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  1. 2025LLM Latent Reasoning as Chain of Superpositioncited by 37
  2. 2025Soft Tokens, Hard Truthscited by 31
  3. 2025LaDiR: Latent Diffusion Enhances LLMs for Text Reasoningcited by 25
  4. 2025SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMscited by 20
  5. 2025SofT-GRPO: Surpassing Discrete-Token LLM Reinforcement Learning via Gumbel-Reparameterized Soft-Thinking Policy Optimizationcited by 18
  6. 2026Multiplex Thinking: Reasoning via Token-wise Branch-and-Mergecited by 16
  7. 2025Parallel Test-Time Scaling for Latent Reasoning Modelscited by 12
  8. 2026Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decodingcited by 11
  9. 2026Latent-GRPO: Group Relative Policy Optimization for Latent Reasoningcited by 10
  10. 2026Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structurecited by 9
  11. 2026LaDi-RL: Latent Diffusion Reasoning Prevents Entropy Collapse in Reinforcement Learningcited by 7
  12. 2026Quantifying the Necessity of Chain of Thought through Opaque Serial Depthcited by 7
22 more
  1. 2026ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spacescited by 6
  2. 2026SeLaR: Selective Latent Reasoning in Large Language Modelscited by 6
  3. 2026What Do Visual Tokens Really Encode? Uncovering Sparsity and Redundancy in Multimodal Large Language Modelscited by 5
  4. 2025JEPA-Reasoner: Decoupling Latent Reasoning from Token Generationcited by 4
  5. 2026LEPO: Latent Reasoning Policy Optimization for Large Language Modelscited by 4
  6. 2026Latent Reasoning with Normalizing Flowscited by 4
  7. 2026Restoring Exploration after Post-Training: Latent Exploration Decoding for Large Reasoning Modelscited by 3
  8. 2026Pair-In, Pair-Out: Latent Multi-Token Prediction for Efficient LLMscited by 2
  9. 2026Training Continuous Chain of Thought Models: A Tale of Two Regimescited by 2
  10. 2026BFS-PO: Best-First Search for Large Reasoning Modelscited by 1
  11. 2026CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoningcited by 1
  12. 2026Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routingcited by 1
  13. 2026Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectoriescited by 1
  14. 2025Multi-Path Collaborative Reasoning via Reinforcement Learningcited by 0
  15. 2026TARPO: Token-Wise Latent-Explicit Reasoning via Action-Routing Policy Optimizationcited by 0
  16. 2026SuperThoughts: Reasoning Tokens in Superpositioncited by 0
  17. 2026Soft Token Alignment for Cross-Lingual Reasoningcited by 0
  18. 2026A Model with No Head and Many Thoughtscited by 0
  19. 2026The Dynamics of Continuous Mixture Collapse in Language Modelscited by 0
  20. 2026Next Thoughts Are Distributions: Generative Autoregressive Reasoning in the Latent Spacecited by 0
  21. 2026Pretraining Latent Information Feedback Transformers with Teacher Supervisioncited by 0
  22. 2026Soft Spatial Reasoningcited by 0

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  1. 2025Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
  2. 2023Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners
  3. 2025Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning
  4. 2025Improving Latent Reasoning in LLMs via Soft Concept Mixing
  5. 2025From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models
  6. 2024A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences
  7. 2025State over Tokens: Characterizing the Role of Reasoning Tokens
  8. 2025Thinking in a Crowd: How Auxiliary Information Shapes LLM Reasoning