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References (65)

[1]
Soft Tokens, Hard Truths
2025Natasha Butt, Ariel Kwiatkowski et al.
[2]
LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking
2025Chunhung Wu, Jinliang Lu et al.
[3]
Controlling Thinking Speed in Reasoning Models
2025Zhengkai Lin, Zhihang Fu et al.
[4]
TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
2025Zhenyu Hou, Ziniu Hu et al.
[5]
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
2025Shenzhi Wang, Le Yu et al.
[6]
A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
2025Xiaoang Xu, Shuo Wang et al.
[7]
MathArena: Evaluating LLMs on Uncontaminated Math Competitions
2025Mislav Balunovi'c, Jasper Dekoninck et al.
[8]
LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling
2025Yang Xiao, Jiashuo Wang et al.
[9]
VeriThinker: Learning to Verify Makes Reasoning Model Efficient
2025Zigeng Chen, Xinyin Ma et al.
[10]
First Finish Search: Efficient Test-Time Scaling in Large Language Models
2025Aradhye Agarwal, Ayan Sengupta et al.
[11]
Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning
2025Michael Hassid, Gabriele Synnaeve et al.
[12]
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
2025Xiaoxue Cheng, Junyi Li et al.
[13]
Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains
2025Wenhui Tan, Jiaze Li et al.
[14]
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy
2025Gengyang Li, Yifeng Gao et al.
[15]
Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space
2025Zhen Zhang, Xuehai He et al.
[16]
Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoning
2025Jiwon Song, Dongwon Jo et al.
[17]
Let LRMs Break Free from Overthinking via Self-Braking Tuning
2025Haoran Zhao, Yuchen Yan et al.
[18]
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning
2025Ziqing Qiao, Yongheng Deng et al.
[19]
ShorterBetter: Guiding Reasoning Models to Find Optimal Inference Length for Efficient Reasoning
2025Jingyang Yi, Jiazheng Wang
[20]
Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
2025Jinyan Su, Jennifer Healey et al.

Showing 20 of 65 references

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