mlsanityData to 7 Oct 2026

The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models

arXiv 2503.02875 ↗First posted 4 Mar 2025cs.CLcited by 31

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce Unsupervised Prefix Fine-Tuning (UPFT), which leverages the observation of Prefix Self-Consistency -- the shared initial reasoning steps across diverse solution trajectories -- to enhance LLM reasoning efficiency. By training exclusively on the initial prefix substrings (as few as 8 tokens), UPFT removes the need for labeled data or exhaustive sampling. Experiments on reasoning benchmarks show that UPFT matches the performance of supervised methods such as Rejection Sampling Fine-Tuning, while reducing training time by 75% and sampling cost by 99%. Further analysis reveals that errors tend to appear in later stages of the reasoning process and that prefix-based training preserves the model's structural knowledge. This work demonstrates how minimal unsupervised fine-tuning can unlock substantial reasoning gains in LLMs, offering a scalable and resource-efficient alternative to conventional approaches.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 31 of its 45 references that are in the map. First posted on arXiv 4 Mar 2025.

Builds on

31 of 45 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025s1: Simple test-time scaling31 Jan
  2. 2024V-STaR: Training Verifiers for Self-Taught Reasoners9 Feb
  3. 2024ReFT: Reasoning with Reinforced Fine-Tuning17 Jan
  4. 2023Large Language Models Cannot Self-Correct Reasoning Yet3 Oct
  5. 2023Making Large Language Models Better Reasoners with Alignment5 Sep
  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Models17 May
  7. 2022STaR: Bootstrapping Reasoning With Reasoning28 Mar

Other references · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2024The Llama 3 Herd of Modelscited by 14,192
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  7. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  9. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  10. 2024Qwen2.5 Technical Reportcited by 4,713
  11. 2023Let's Verify Step by Stepcited by 3,598
  12. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
12 more
  1. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  2. 2024Self-Rewarding Language Modelscited by 654
  3. 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Modelscited by 552
  4. 2025LIMO: Less is More for Reasoningcited by 488
  5. 2023Reinforced Self-Training (ReST) for Language Modelingcited by 486
  6. 2023Scaling Relationship on Learning Mathematical Reasoning with Large Language Modelscited by 417
  7. 2023Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Modelscited by 325
  8. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  9. 2024Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Modelscited by 277
  10. 2024OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Datacited by 194
  11. 2023Learning From Mistakes Makes LLM Better Reasonercited by 97
  12. 2024LLMs Could Autonomously Learn Without External Supervisioncited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 25; 2026: 6.

31 papers.

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  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  2. 2025ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learningcited by 187
  3. 2025Jointly Reinforcing Diversity and Quality in Language Model Generationscited by 79
  4. 2025Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodalitycited by 49
  5. 2025Understanding Aha Moments: from External Observations to Internal Mechanismscited by 48
  6. 2025Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Timecited by 48
  7. 2026Fast and Effective On-policy Distillation from Reasoning Prefixescited by 43
  8. 2025Distilling LLM Agent into Small Models with Retrieval and Code Toolscited by 41
  9. 2025SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignmentcited by 24
  10. 2025Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilitiescited by 20
  11. 2025Learning from Peers in Reasoning Modelscited by 16
  12. 2025Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learningcited by 16
19 more
  1. 2025Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?cited by 9
  2. 2025LookAhead Tuning: Safer Language Models via Partial Answer Previewscited by 7
  3. 2025Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoningcited by 7
  4. 2025T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answeringcited by 6
  5. 2025Overthinking Reduction with Decoupled Rewards and Curriculum Data Schedulingcited by 4
  6. 2025Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoningcited by 3
  7. 2025LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?cited by 3
  8. 2026The Path of Least Resistance: Guiding LLM Reasoning Trajectories with Prefix Consensuscited by 3
  9. 2026Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separationcited by 3
  10. 2025EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensationcited by 2
  11. 2025The Valley of Code Reasoning: Scaling Knowledge Distillation of Large Language Modelscited by 2
  12. 2025Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithmscited by 1
  13. 2025MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Securitycited by 1
  14. 2025Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoningcited by 1
  15. 2026Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Trainingcited by 1
  16. 2025Decoding One Safety Trigger Token for Balancing Safety and Usability in Large Language Modelscited by 0
  17. 2025ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Datasetcited by 0
  18. 2026How Does Prefix Matter in Reasoning Model Tuning?cited by 0
  19. 2026Know When to Stop, Where to Restart: Accelerating Multi-Turn Agentic On-Policy Distillationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing Large Language Model Reasoning via Selective Critical Token Fine-Tuning
  2. 2025Think, Prune, Train, Improve: Scaling Reasoning without Scaling Models
  3. 2026Reasoning Quality Emerges Early: Data Curation for Reasoning Models
  4. 2025UFT: Unifying Supervised and Reinforcement Fine-Tuning
  5. 2025The Thinking Spectrum: An Empirical Study of Tunable Reasoning in LLMs through Model Merging
  6. 2026Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
  7. 2025Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study
  8. 2025HEFT: A Coarse-to-Fine Hierarchy for Enhancing the Efficiency and Accuracy of Language Model Reasoning