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[LG] Self-Exploring Language Models: Active Preference Elicitation for Online Alignment https://t.co/Guz0zazLzy - Standard RLHF frameworks passively explore by sampling from the trained LLM, which can easily get stuck at local optima and overfit to current data. This paper… https://t.co/VVTuFGu2ij
Advancing Ethical AI: Preference Matching Reinforcement Learning from Human Feedback RLHF for Aligning LLMs with Human Preferences Quick read: https://t.co/wrlNk9zJGa Paper: https://t.co/8AQdVygp2q @weijie444
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning. https://t.co/6WJhcGzsUt
Tech giants like Google are introducing new approaches to reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human preferences. These methods aim to optimize rewards, mitigate tax, and enhance ethical AI by actively exploring preferences and matching them with LLM behavior.
