Prototypical Reward Network for Data-Efficient RLHF¶
Arxiv Link - 2024-06-06 15:23:30
Abstract¶
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback. By enabling stable and reliable structural learning from fewer samples, Proto-RM significantly enhances LLMs' adaptability and accuracy in interpreting human preferences. Extensive experiments on various datasets demonstrate that Proto-RM significantly improves the performance of reward models and LLMs in human feedback tasks, achieving comparable and usually better results than traditional methods, while requiring significantly less data. in data-limited scenarios. This research offers a promising direction for enhancing the efficiency of reward models and optimizing the fine-tuning of language models under restricted feedback conditions.
Socials¶
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| 🌟 Exciting News in AI and NLP! 🌟 The latest research introduces Proto-RM, a cutting-edge framework leveraging prototypical networks to enhance reward models for Large Language Models (LLMs) in Reinforcement Learning from Human Feedback (RLHF) tasks. Proto-RM enables stable and reliable structural learning from limited human feedback, significantly boosting LLMs' adaptability and accuracy in interpreting human preferences. 🔍 Research Results: Extensive experiments across various datasets demonstrate that Proto-RM outperforms traditional methods in human feedback tasks, achieving comparable or even superior results while requiring significantly less data in data-limited scenarios. 🚀 Dive into the details of this groundbreaking research at: http://arxiv.org/abs/2406.06606v1 #AI #NLP #LLMs #ProtoRM #HumanFeedback #ReinforcementLearning #Research #Innovation #TechBreakthrough |
🚀 Exciting new research alert! Proto-RM framework enhances reward models for Large Language Models under limited human feedback, boosting adaptability and accuracy. 🤖📈 Check out the results here: http://arxiv.org/abs/2406.06606v1 #AI #NLP #LLMs #ProtoRM #Research #TechInnovation |