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References (42)

[1]
Do Depth-Grown Models Overcome the Curse of Depth? An In-Depth Analysis
2025Ferdinand Kapl, Emmanouil Angelis et al.
[2]
Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence
2025Sean McLeish, Ang Li et al.
[3]
Scaling Latent Reasoning via Looped Language Models
2025Ruiming Zhu, Zixuan Wang et al.
[4]
Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts
2025Yeskendir Koishekenov, Aldo Lipani et al.
[5]
Nemotron-CC-Math: A 133 Billion-Token-Scale High Quality Math Pretraining Dataset
2025Rabeeh Karimi Mahabadi, S. Satheesh et al.
[6]
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
2025Sangmin Bae, Yujin Kim et al.
[7]
Hierarchical Reasoning Model
2025Guan Wang, Jin Li et al.
[8]
Do Language Models Use Their Depth Efficiently?
2025R'obert Csord'as, Christopher D. Manning et al.
[9]
Reasoning with Latent Thoughts: On the Power of Looped Transformers
2025Nikunj Saunshi, Nishanth Dikkala et al.
[10]
The Curse of Depth in Large Language Models
2025Wenfang Sun, Xinyuan Song et al.
[11]
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
2025Jonas Geiping, Sean McLeish et al.
[12]
SmolLM2: When Smol Goes Big - Data-Centric Training of a Small Language Model
2025Loubna Ben Allal, Anton Lozhkov et al.
[13]
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
2025Fengli Xu, Qianyue Hao et al.
[14]
The 4th Dimension for Scaling Model Size
2025Ruike Zhu, Hanwen Zhang et al.
[15]
Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA
2024Sangmin Bae, Adam Fisch et al.
[16]
On the Inductive Bias of Stacking Towards Improving Reasoning
2024Nikunj Saunshi, Stefani Karp et al.
[17]
Looped Transformers for Length Generalization
2024Ying Fan, Yilun Du et al.
[18]
Does your data spark joy? Performance gains from domain upsampling at the end of training
2024Cody Blakeney, Mansheej Paul et al.
[19]
MoEUT: Mixture-of-Experts Universal Transformers
2024R'obert Csord'as, Kazuki Irie et al.
[20]
Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training
2024Wenyu Du, Tongxu Luo et al.

Showing 20 of 42 references

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"Develop a tool that enhances reasoning in LLMs through composable depth growth and looping techniques."

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