5 citations · 14 across the 8 of their papers we have counts for
3 papers · 1 filter
M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models
Junxiong Wang, Wen-Ding Li, Daniele Paliotta +3
Effective reasoning is crucial to solving complex mathematical problems. Recent large language models (LLMs) have boosted performance by scaling test-time computation through long…
Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
Daniele Paliotta, Junxiong Wang, Matteo Pagliardini +6
Recent advancements have demonstrated that the performance of large language models (LLMs) can be significantly enhanced by scaling computational resources at test time. A common s…
Leveraging the true depth of LLMs
Ramón Calvo González, Daniele Paliotta, Matteo Pagliardini +2
The remarkable capabilities of Large Language Models (LLMs) are overshadowed by their immense computational cost. While recent work has shown that many LLM layers can be reordered…