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RLHF
Machine learning technique that leverages human feedback to optimize and improve the performance of machine learning models. This approach involves human evaluators providing feedback on the model's outputs, which is then used to guide the learning process, aligning the model's behavior with human values and preferences. RLHF is particularly useful in scenarios where it is challenging to define a clear reward function, and human judgment is essential for evaluating the quality of the model's performance.
RLHF was discovered on January 31st 2024 and it currently has a search volume of 33.1K with a growth of +155%.
Growth
- Exploding
- Regular
- Peaked
Speed
- Exponential
- Constant
- Stationary
Seasonality
- High
- Medium
- Low
Volatility
- High
- Average
- Low
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