RLHF Insights | Sumble

What is RLHF?

Reinforcement Learning from Human Feedback (RLHF) is a technique used to fine-tune language models to better align with human preferences. It involves training a reward model based on human feedback (e.g., rankings or ratings of different model outputs) and then using reinforcement learning to optimize the language model to maximize this reward.

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What other technologies are related to RLHF?

RLHF Complementary Technologies

SFT

Supervised Fine-tuning is a crucial initial step in the RLHF pipeline, providing the model with a foundational understanding of the desired task or behavior.

mentioned alongside RLHF in 25% (121) of relevant job posts

DPO

Direct Preference Optimization is an alternative to reinforcement learning, used to optimize the language model directly from preference data. While being an alternative to the RL part of RLHF, it is still a way to improve LLMs based on preferences.

What is RLHF?

What other technologies are related to RLHF?

RLHF Complementary Technologies

Which job functions mention RLHF?

Which organizations are mentioning RLHF?