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References (32)

[1]
Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?
2025Siddhant Bhambri, Upasana Biswas et al.
[2]
Thought Anchors: Which LLM Reasoning Steps Matter?
2025Paul C. Bogdan, Uzay Macar et al.
[3]
RL for Reasoning by Adaptively Revealing Rationales
2025Mohammad Hossein Amani, Aryo Lotfi et al.
[4]
TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
2025Zhenyu Hou, Ziniu Hu et al.
[5]
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
2025Parshin Shojaee, Iman Mirzadeh et al.
[6]
Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models
2025Yiran Guo, Lijie Xu et al.
[7]
Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens
2025Kaya Stechly, Karthik Valmeekam et al.
[8]
Reasoning Models Don't Always Say What They Think
2025Yanda Chen, Joe Benton et al.
[9]
Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
2025Subbarao Kambhampati, Kaya Stechly et al.
[10]
Do NOT Think That Much for 2+3=? On the Overthinking of Long Reasoning Models
2025Xingyu Chen, Jiahao Xu et al.
[11]
Forking Paths in Neural Text Generation
2024Eric J. Bigelow, Ari Holtzman et al.
[12]
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
2024Moshe Berchansky, Daniel Fleischer et al.
[13]
Are self-explanations from Large Language Models faithful?
2024Andreas Madsen, Sarath Chandar et al.
[14]
GPQA: A Graduate-Level Google-Proof Q&A Benchmark
2023David Rein, Betty Li Hou et al.
[15]
Efficient Memory Management for Large Language Model Serving with PagedAttention
2023Woosuk Kwon, Zhuohan Li et al.
[16]
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
2023Haipeng Luo, Qingfeng Sun et al.
[17]
Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions
2023Skyler Wu, Eric Meng Shen et al.
[18]
Self-Refine: Iterative Refinement with Self-Feedback
2023Aman Madaan, Niket Tandon et al.
[19]
Reflexion: language agents with verbal reinforcement learning
2023Noah Shinn, Federico Cassano et al.
[20]
Faithful Chain-of-Thought Reasoning
2023Qing Lyu, Shreya Havaldar et al.

Showing 20 of 32 references

Founder's Pitch

"In-depth analysis of Chain-of-Thought dynamics in LLM reasoning to better understand the mechanics behind successful problem-solving."

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