mlsanityData to 14 Oct 2026

SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking

arXiv 2604.07922 ↗First posted 9 Apr 2026cs.AI cs.CLcited by 2

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

Abstract (arXiv)

Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive "overthinking", generating unnecessarily long reasoning chains. While current solutions improve token efficiency, they often sacrifice fine-grained control or risk disrupting the logical integrity of the reasoning process. To address this, we introduce Stepwise Adaptive Thinking (SAT), a framework that performs step-level, difficulty-aware pruning while preserving the core reasoning structure. SAT formulates reasoning as a Finite-State Machine (FSM) with distinct thinking modes (Slow, Normal, Fast, Skip). It navigates these states dynamically using a lightweight Process Reward Model (PRM), compressing easy steps while preserving depth for hard ones. Experiments across 9 LRMs and 7 benchmarks show that SAT achieves up to 40% reduction in reasoning tokens while generally maintaining or improving accuracy.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 37 of its 52 references that are in the map. First posted on arXiv 9 Apr 2026.

Builds on

37 of 52 references.

Closest ancestors · 15

Same direction, newest first

  1. 2026Stop Unnecessary Reflection: Training LRMs for Efficient Reasoning with Adaptive Reflection and Length Coordinated Penalty12 Feb
  2. 2025DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference22 Oct
  3. 2025Enhancing Large Language Model Reasoning via Selective Critical Token Fine-Tuning13 Oct
  4. 2025Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression7 Aug
  5. 2025QFFT, Question-Free Fine-Tuning for Adaptive Reasoning15 Jun
  6. 2025Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency10 Jun
  7. 2025PATS: Process-Level Adaptive Thinking Mode Switching25 May
  8. 2025TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling22 May
  9. 2025ThinkSwitcher: When to Think Hard, When to Think Fast20 May
  10. 2025Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst20 May
  11. 2025Dynamic Early Exit in Reasoning Models22 Apr
  12. 2025A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond27 Mar
3 more
  1. 2025L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning6 Mar
  2. 2024C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness16 Dec
  3. 2024Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models1 Aug

Other references · 22

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  4. 2023Let's Verify Step by Stepcited by 3,789
  5. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
  6. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 928
  7. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  8. 2025Chain of Draft: Thinking Faster by Writing Lesscited by 297
  9. 2025Learning to Reason under Off-Policy Guidancecited by 281
  10. 2024Fast Best-of-N Decoding via Speculative Rejectioncited by 112
  11. 2023Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generationcited by 74
  12. 2024The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Modelscited by 63
10 more
  1. 2024Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thoughtcited by 56
  2. 2025Answer Convergence as a Signal for Early Stopping in Reasoningcited by 40
  3. 2024System-1.x: Learning to Balance Fast and Slow Planning with Language Modelscited by 35
  4. 2025Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMscited by 16
  5. 2025Generator-Assistant Stepwise Rollback Framework for Large Language Model Agentcited by 14
  6. 2025Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learningcited by 13
  7. 2025LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategycited by 10
  8. 2025Efficient Safety Alignment of Large Language Models via Preference Re-ranking and Representation-based Reward Modelingcited by 4
  9. 2026FaithRL: Learning to Reason Faithfully through Step-Level Faithfulness Maximizationcited by 4
  10. 2024DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Searchcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Mitigating Multimodal Hallucination via Phase-wise Self-rewardcited by 2
  2. 2026GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Modelscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens
  2. 2026CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models
  3. 2025Promoting Efficient Reasoning with Verifiable Stepwise Reward
  4. 2025Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models
  5. 2025THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models
  6. 2025When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning
  7. 2025Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation
  8. 2025On the Provable Performance Guarantee of Efficient Reasoning Models