Add to ORKGReinforcement learning from human feedback (RLHF) is effective at aligning large language models (LLMs) to human preferences, but gathering high quality human preference labels is a key bottleneck. We conduct a head-to-head comparison of RLHF vs. RL from AI Feedback (RLAIF) - a technique where preferences are labeled by an off-the-shelf LLM in lieu of humans, and we find that they result in similar improvements. On the task of summarization, human evaluators prefer generations from both RLAIF and RLHF over a baseline supervised fine-tuned model in ~70% of cases. Furthermore, when asked to rate RLAIF vs. RLHF summaries, humans prefer both at equal rates. These results suggest that RLAIF can yield human-level performance, offering a potential...
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupe...
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalitie...
The current reward learning from human preferences could be used to resolve complex reinforcement le...
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align wi...
While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with gen...
With the development of large language models (LLMs), striking a balance between the performance and...
We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If...
Reinforcement learning from human feedback (RLHF) has dramatically improved the real-world performan...
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models...
To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to...
Reinforcement learning from human feedback (RLHF) is a recent technique to improve the quality of th...
Large Language Models (LLMs) have been a significant landmark of Artificial Intelligence (AI) advanc...
Deploying learning systems in the real-world requires aligning their objectives with those of the hu...
We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference...
The utility of reinforcement learning is limited by the alignment of reward functions with the inter...
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupe...
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalitie...
The current reward learning from human preferences could be used to resolve complex reinforcement le...
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align wi...
While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with gen...
With the development of large language models (LLMs), striking a balance between the performance and...
We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If...
Reinforcement learning from human feedback (RLHF) has dramatically improved the real-world performan...
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models...
To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to...
Reinforcement learning from human feedback (RLHF) is a recent technique to improve the quality of th...
Large Language Models (LLMs) have been a significant landmark of Artificial Intelligence (AI) advanc...
Deploying learning systems in the real-world requires aligning their objectives with those of the hu...
We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference...
The utility of reinforcement learning is limited by the alignment of reward functions with the inter...
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupe...
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalitie...
The current reward learning from human preferences could be used to resolve complex reinforcement le...