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.

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.

PEFT

Parameter-Efficient Fine-Tuning methods are useful for adapting large language models in RLHF, reducing computational costs.

Number of organizations that mention technology

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Which job functions commonly mention RLHF?

AI Engineer

0.2% of all AI Engineer jobs mention RLHF

View 153 jobs on Sumble

Research Scientist

0.2% of all Research Scientist jobs mention RLHF

View 77 jobs on Sumble

Machine Learning

0.1% of all Machine Learning jobs mention RLHF

View 114 jobs on Sumble

Researcher

111 Researcher jobs mention RLHF

View 111 jobs on Sumble

Data Scientist

53 Data Scientist jobs mention RLHF

View 53 jobs on Sumble

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Which organizations are mentioning RLHF?

TikTok

Information

ByteDance

Scientific and Technical Services

Amazon

Retail Trade

Apple

Scientific and Technical Services

Krutrim

Scientific and Technical Services

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**RLHF**

What is RLHF?

What other technologies are related to RLHF?

RLHF Complementary Technologies

Which job functions commonly mention RLHF?

Which organizations are mentioning RLHF?

Find the right accounts, contact, message, and time to sell

RLHF