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Gathering human feedback
RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.
Global Internet Forum to Counter Terrorism to Hold First Meeting in San Francisco - meta.com
Global Internet Forum to Counter Terrorism to Hold First Meeting in San Francisco meta.com
Tesla Q1 2026 Financial Results and Q&A Webcast - Tesla
Tesla Q1 2026 Financial Results and Q&A Webcast Tesla
Better exploration with parameter noise
We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
Better exploration with parameter noise
We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement learning algorithm at OpenAI because of its ease of use and good performance.
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement learning algorithm at OpenAI because of its ease of use and good performance.
Robust adversarial inputs
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.
Robust adversarial inputs
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.
Khaleej Times - Dubai News, UAE News, Gulf, News, Latest news, Arab news, Gulf News, Dubai Labour News - Khaleej Times
Khaleej Times - Dubai News, UAE News, Gulf, News, Latest news, Arab news, Gulf News, Dubai Labour News Khaleej Times
Prime Members Enjoyed Biggest Global Shopping Event in Amazon History - About Amazon
Prime Members Enjoyed Biggest Global Shopping Event in Amazon History About Amazon
Investing in Menlo Park and the Community - meta.com
Investing in Menlo Park and the Community meta.com
Amazon’s efforts to address homelessness expand to Washington, D.C. - About Amazon
Amazon’s efforts to address homelessness expand to Washington, D.C. About Amazon
Hard Questions: Who Should Decide What Is Hate Speech in an Online Global Community? - meta.com
Hard Questions: Who Should Decide What Is Hate Speech in an Online Global Community? meta.com
Our First Communities Summit and New Tools For Group Admins - meta.com
Our First Communities Summit and New Tools For Group Admins meta.com
Giving People More Control Over Their Facebook Profile Picture - meta.com
Giving People More Control Over Their Facebook Profile Picture meta.com
The world’s most luxurious dog manors - Emirates 24|7
The world’s most luxurious dog manors Emirates 24|7
Introducing Hard Questions - meta.com
Introducing Hard Questions meta.com
Learning from human preferences
One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.
Learning from human preferences
One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.
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