2 papers
cs.CL2026
On the Optimal Reasoning Length for RL-Trained Language Models
Daisuke Nohara, Taishi Nakamura, Rio Yokota
Reinforcement learning substantially improves reasoning in large language models, but it also tends to lengthen chain-of-thought outputs and increase computational cost. Although l…
cs.LG2026
Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
Taishi Nakamura, Satoki Ishikawa, Masaki Kawamura +4
Empirical scaling laws have driven the evolution of large language models (LLMs), yet their coefficients shift whenever the model architecture or data pipeline changes. Mixture-of-…