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Objective Mismatch in Reinforcement Learning from Human Feedback: Acknowledgments, and References
This story was originally published on HackerNoon at: https://hackernoon.com/objective-mismatch-in-reinforcement-learning-from-human-feedback-acknowledgments-and-references.
This conclusion highlights the path toward enhanced accessibility and reliability for language models.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #reinforcement-learning, #rlhf, #llm-research, #llm-training, #llm-technology, #llm-optimization, #ai-model-training, #llm-development, and more.
This story was written by: @feedbackloop. Learn more about this writer by checking @feedbackloop's about page,
and for more stories, please visit hackernoon.com.
Discover the challenges of objective mismatch in RLHF for large language models, affecting the alignment between reward models and downstream performance. This paper explores the origins, manifestations, and potential solutions to address this issue, connecting insights from NLP and RL literature. Gain insights into fostering better RLHF practices for more effective and user-aligned language models.
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By HackerNoon
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This story was originally published on HackerNoon at: https://hackernoon.com/objective-mismatch-in-reinforcement-learning-from-human-feedback-acknowledgments-and-references.
This conclusion highlights the path toward enhanced accessibility and reliability for language models.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #reinforcement-learning, #rlhf, #llm-research, #llm-training, #llm-technology, #llm-optimization, #ai-model-training, #llm-development, and more.
This story was written by: @feedbackloop. Learn more about this writer by checking @feedbackloop's about page,
and for more stories, please visit hackernoon.com.
Discover the challenges of objective mismatch in RLHF for large language models, affecting the alignment between reward models and downstream performance. This paper explores the origins, manifestations, and potential solutions to address this issue, connecting insights from NLP and RL literature. Gain insights into fostering better RLHF practices for more effective and user-aligned language models.