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Found 9 papers, 7 papers with code
Scaling Laws for Autoregressive Generative Modeling
The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
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Measuring the Algorithmic Efficiency of Neural Networks
Three factors drive the advance of AI: algorithmic innovation, data, and the amount of compute available for training.
Scaling Laws for Neural Language Models
We study empirical scaling laws for language model performance on the cross-entropy loss.
Fine-Tuning Language Models from Human Preferences
Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.
Unrestricted Adversarial Examples
We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool.
Is Generator Conditioning Causally Related to GAN Performance?
Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs).
Adversarial Patch
We present a method to create universal, robust, targeted adversarial image patches in the real world.
Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems.
