335 citations · 1.7k across the 86 of their papers we have counts for
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cs.LG2026
RMB: Reward Model Boosting Mitigates Reward Hacking
Jiabin Fan, Dezhi Ye, Yongchang Hao +1
Reinforcement Learning from Human Feedback (RLHF) is a powerful technique for aligning large language models (LLMs) with human preference. However, it often suffers from the reward…
cs.LG2026
Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling
Yongchang Hao, Lili Mou
Speculative sampling (SpS) has been successful in accelerating the decoding throughput of auto-regressive large language models by leveraging smaller draft models. SpS strictly enf…
cs.LG2026
Do Neural Networks Lose Plasticity in a Gradually Changing World?
Tianhui Liu, Lili Mou
Continual learning has become a trending topic in machine learning. Recent studies have discovered an interesting phenomenon called loss of plasticity, referring to neural networks…