9 citations · 10 across the 7 of their papers we have counts for
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cs.LG2026
Ratio-Variance Regularized Policy Optimization
Yu Luo, Shuo Han, Yihan Hu +5
Standard on-policy reinforcement learning relies on heuristic clipping to enforce trust regions, but this mechanism imposes a severe cost by indiscriminately truncating high-return…
cs.LG2026
Ratio-Variance Regularized Policy Optimization for Efficient LLM Fine-tuning
Yu Luo, Shuo Han, Yihan Hu +2
On-policy reinforcement learning (RL), particularly Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), has become the dominant paradigm for fine-tuni…
cs.LG2024★ 1 cited
Reinforced In-Context Black-Box Optimization
Lei Song, Chenxiao Gao, Ke Xue +5
Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular co…