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cs.LG2025
OPPO: Accelerating PPO-based RLHF via Pipeline Overlap
Kaizhuo Yan, Yingjie Yu, Yifan Yu +2
Proximal Policy Optimization (PPO)-based reinforcement learning from human feedback (RLHF) is a widely adopted paradigm for aligning large language models (LLMs) with human prefere…
cs.AI2025
Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts
Haizhong Zheng, Yang Zhou, Brian R. Bartoldson +4
Reinforcement learning, such as PPO and GRPO, has powered recent breakthroughs in LLM reasoning. Scaling rollout to sample more prompts enables models to selectively use higher-qua…
cs.CL2025
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Haoyi Song, Ruihan Ji, Naichen Shi +2
Large language models (LLMs) have transformed natural language processing, but their reliable deployment requires effective uncertainty quantification (UQ). Existing UQ methods are…