# Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

arXiv: https://arxiv.org/abs/2505.16315 · HTML: https://mlsanity.com/paper/2505.16315/

## Key facts

- Cited by 21 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 18 of its 37 references that are in the map.
- First posted on arXiv 22 May 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [LIMO: Less is More for Reasoning](https://mlsanity.com/paper/2502.03387/) (2025)
- [L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning](https://mlsanity.com/paper/2503.04697/) (2025)
- [Demystifying Long Chain-of-Thought Reasoning in LLMs](https://mlsanity.com/paper/2502.03373/) (2025)
- [TokenSkip: Controllable Chain-of-Thought Compression in LLMs](https://mlsanity.com/paper/2502.12067/) (2025)
- [ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning](https://mlsanity.com/paper/2504.01296/) (2025)
- [CoT-Valve: Length-Compressible Chain-of-Thought Tuning](https://mlsanity.com/paper/2502.09601/) (2025)
- [DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models](https://mlsanity.com/paper/2503.04472/) (2025)
- [Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning](https://mlsanity.com/paper/2502.18080/) (2025)
- [Self-Training Elicits Concise Reasoning in Large Language Models](https://mlsanity.com/paper/2502.20122/) (2025)
- [Visual Agents as Fast and Slow Thinkers](https://mlsanity.com/paper/2408.08862/) (2024)
- [Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective](https://mlsanity.com/paper/2412.14135/) (2024)
- [System-1.x: Learning to Balance Fast and Slow Planning with Language Models](https://mlsanity.com/paper/2407.14414/) (2024)
- [Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking](https://mlsanity.com/paper/2501.01306/) (2025)
- [DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models](https://mlsanity.com/paper/2407.01009/) (2024)

## Built on by (most cited first, 21 of 21)

- [Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models](https://mlsanity.com/paper/2508.02120/) (2025)
- [VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice](https://mlsanity.com/paper/2601.05175/) (2026)
- [Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey](https://mlsanity.com/paper/2507.09662/) (2025)
- [OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation](https://mlsanity.com/paper/2506.02397/) (2025)
- [ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning](https://mlsanity.com/paper/2602.09953/) (2026)
- [Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models](https://mlsanity.com/paper/2601.11340/) (2026)
- [DeepCompress: A Dual Reward Strategy for Dynamically Exploring and Compressing Reasoning Chains](https://mlsanity.com/paper/2510.27419/) (2025)
- [Meta-R1: Empowering Large Reasoning Models with Metacognition](https://mlsanity.com/paper/2508.17291/) (2025)
- [ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure](https://mlsanity.com/paper/2602.01472/) (2026)
- [LACONIC: Length-Aware Constrained Reinforcement Learning for LLM](https://mlsanity.com/paper/2602.14468/) (2026)
- [Adaptive Dual Reasoner: Large Reasoning Models Can Think Efficiently by Hybrid Reasoning](https://mlsanity.com/paper/2510.10207/) (2025)
- [Can Confidence Estimates Decide When Chain-of-Thought Is Necessary for LLMs?](https://mlsanity.com/paper/2510.21007/) (2025)
- [BFS-PO: Best-First Search for Large Reasoning Models](https://mlsanity.com/paper/2602.14917/) (2026)
- [Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization](https://mlsanity.com/paper/2606.16111/) (2026)
- [A State-Transition Framework for Efficient LLM Reasoning](https://mlsanity.com/paper/2602.01198/) (2026)
- [HotelQuEST: Balancing Quality and Efficiency in Agentic Search](https://mlsanity.com/paper/2602.23949/) (2026)
- [EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization](https://mlsanity.com/paper/2607.19962/) (2026)
- [Strategic Reflectivism In Intelligent Systems](https://mlsanity.com/paper/2505.22987/) (2025)
- [Constraint-Rectified Training for Efficient Chain-of-Thought](https://mlsanity.com/paper/2602.12526/) (2026)
- [Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning](https://mlsanity.com/paper/2607.12395/) (2026)
- [AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking](https://mlsanity.com/paper/2608.26141/) (2026)

## Abstract (arXiv)

Large reasoning models (LRMs) have demonstrated strong performance on complex reasoning tasks, but often suffer from overthinking, generating redundant content regardless of task difficulty. Inspired by the dual process theory in cognitive science, we propose Adaptive Cognition Policy Optimization (ACPO), a reinforcement learning framework that enables LRMs to achieve efficient reasoning through adaptive cognitive allocation and dynamic system switch. ACPO incorporates two key components: (1) introducing system-aware reasoning tokens to explicitly represent the thinking modes thereby making the model's cognitive process transparent, and (2) integrating online difficulty estimation and token length budget to guide adaptive system switch and reasoning during reinforcement learning. To this end, we propose a two-stage training strategy. The first stage begins with supervised fine-tuning to cold start the model, enabling it to generate reasoning paths with explicit thinking modes. In the second stage, we apply ACPO to further enhance adaptive system switch for difficulty-aware reasoning. Experimental results demonstrate that ACPO effectively reduces redundant reasoning while adaptively adjusting cognitive allocation based on task complexity, achieving efficient hybrid reasoning.