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cs.LG2025
Efficient Parallel Samplers for Recurrent-Depth Models and Their Connection to Diffusion Language Models
Jonas Geiping, Xinyu Yang, Guinan Su
Language models with recurrent depth, also referred to as universal or looped when considering transformers, are defined by the capacity to increase their computation through the r…
cs.LG2025
Sample Smart, Not Hard: Correctness-First Decoding for Better Reasoning in LLMs
Xueyan Li, Guinan Su, Mrinmaya Sachan +1
Large Language Models (LLMs) are increasingly applied to complex tasks that require extended reasoning. In such settings, models often benefit from diverse chains-of-thought to arr…
cs.LG2025
When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs
Keyu Wang, Tian Lyu, Guinan Su +4
Layer pruning has emerged as a widely adopted technique for improving the efficiency of large language models (LLMs). Although existing methods demonstrate strong performance reten…