3 papers
cs.LG2025
On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference
Yue Yu, Qiwei Di, Quanquan Gu +1
Test-time compute (TTC) has become an increasingly prominent paradigm for enhancing large language models (LLMs). Despite the empirical success of methods such as best-of- (BoN)…
cs.LG2025
Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation
Runze Zhao, Yue Yu, Ruhan Wang +2
Continuous-time reinforcement learning (CTRL) provides a natural framework for sequential decision-making in dynamic environments where interactions evolve continuously over time.…
cs.LG2025
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
Runze Zhao, Yue Yu, Adams Yiyue Zhu +2
Continuous-time reinforcement learning (CTRL) provides a principled framework for sequential decision-making in environments where interactions evolve continuously over time. Despi…