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Open Problems and Fundamental Limitations of Reinforcement Learning . . . Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs) Despite this popularity, there has been relatively little public work systematizing its flaws In this paper, we (1) survey open problems and fundamental limitations of RLHF and
Open Problems and Fundamental Limitations of Reinforcement Learning . . . Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals there has been relatively little public work systematizing its flaws In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and
Open Problems and Fundamental Limitations of Reinforcement Learning . . . 3 Open Problems and Limitations of RLHF Figure1(bottom) illustrates the categories of challenges and questions we cover in this section We first divide challenges into three main types corresponding to the three steps of RLHF: collecting human feedback (Section3 1), training the reward model (Section3 2), and training the policy (Sec-tion3 3)
OpenProblemsandFundamentalLimitationsof . . . - arXiv. org In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and arXiv:2307 15217v2 [cs AI] 11 Sep 2023 1 Introduction human feedback, challenges with learning a good reward model, and
Improving Reinforcement Learning from Human Feedback with - arXiv. org Reinforcement Learning from Human Feedback (RLHF) is a widely adopted approach for aligning large language models with human values Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback ArXiv:2307 15217 [cs] Review, and Perspectives on Open Problems arXiv:2005 01643 [cs, stat] ArXiv: 2005 01643
The Alignment Ceiling: Objective Mismatch in Reinforcement Learning . . . \BBOQ \APACrefatitle Open problems and fundamental limitations of reinforcement learning from human feedback Open problems and fundamental limitations of reinforcement learning from human feedback \BBCQ \APACjournalVolNumPages arXiv preprint arXiv:2307 15217 \PrintBackRefs \CurrentBib; Christiano \BOthers
Understanding the Effects of RLHF on - arXiv. org Large language models (LLMs) fine-tuned with reinforcement learning from human feedback (RLHF) have been used in some of the most widely deployed AI models to date, such as OpenAI’s ChatGPT or Anthropic’s Claude Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback, 2023 Browser-assisted question
SuperHF: Supervised Iterative Learning from Human Feedback - arXiv. org a simple unified theoretical perspective that does not involve reinforcement learning and naturally justifies the KL penalty and iterative approach Our main contributions are as follows: 1 A simpler drop-in replacement for RLHF We propose Supervised Human Feedback (SuperHF), a simpler and more robust human preference learning method SuperHF
Using Human Feedback to Fine-tune Diffusion Models - arXiv. org Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models Tomasz Korbak, David Lindner, Pedro Freire, et al Open problems and fundamental limitations of reinforcement learning from human feedback arXiv preprint arXiv:2307 15217, 2023 Chi et al [2023]
Personalized Soups: Personalized Large - arXiv. org While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning diverse, individual perspectives Tomasz Korbak, David Lindner, Pedro Freire, et al Open problems and fundamental limitations of reinforcement learning from human feedback