Improve ROSA customer experience (and customer retention) by leveraging foundation models to do “gpt-chat” style search of Red Hat customer documentation assets.
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Collection of Comparison Data for Reward Model Training #58
The process of training a reward model for Reinforcement Learning from Human Feedback (RLHF) involves gathering comparison data to accurately assess the quality of prompt-response pairs. This comparison data aids in fine-tuning a Large Language Model (LLM) using Reinforcement Learning (RL) to generate responses that align with human preferences. This issue aims to guide the development of a systematic workflow for collecting comparison data using the Argilla platform.
Shreyanand
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3 months ago
The process of training a reward model for Reinforcement Learning from Human Feedback (RLHF) involves gathering comparison data to accurately assess the quality of prompt-response pairs. This comparison data aids in fine-tuning a Large Language Model (LLM) using Reinforcement Learning (RL) to generate responses that align with human preferences. This issue aims to guide the development of a systematic workflow for collecting comparison data using the Argilla platform.