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Found 5 papers, 3 papers with code
Safe RLHF: Safe Reinforcement Learning from Human Feedback
However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset
In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety alignment in large language models (LLMs).
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research
AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns.
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation
This paper presents a multi-agent reinforcement learning (MARL) scheme for proactive Multi-Camera Collaboration in 3D Human Pose Estimation in dynamic human crowds.
