335 citations · 1.7k across the 86 of their papers we have counts for
90 papers
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…
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi +5
Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, multi-agent interactions common i…
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…
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…
IntentCoding: Amplifying User Intent in Code Generation
Zheng Fang, Yihong Dong, Lili Mou +3
Large Language Models (LLMs) have shown strong capabilities in code generation, but their adherence to fine-grained user intent with multiple constraints remains a significant chal…
Multi-Persona Thinking for Bias Mitigation in Large Language Models
Yuxing Chen, Guoqing Luo, Zijun Wu +1
Large Language Models (LLMs) exhibit social biases, which can lead to harmful stereotypes and unfair outcomes. We propose \textbf{Multi-Persona Thinking (MPT)}, a simple inference-…