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For further details check the project's GitHub repository or the Hugging Face dataset cards (taskmaster-1, taskmaster-2, taskmaster-3). Dialog/Instruction prompted 2019 [339] Byrne and Krishnamoorthi et al. DrRepair A labeled dataset for program repair. Pre-processed data Check format details in the project's worksheet. Dialog/Instruction prompted
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large. The predecessor to PPO, Trust Region Policy Optimization (TRPO), was published in 2015.
The Hugging Face Hub is a platform (centralized web service) for hosting: [19] Git-based code repositories, including discussions and pull requests for projects. models, also with Git-based version control; datasets, mainly in text, images, and audio;
Deep reinforcement learning has also been applied to many domains beyond games. In robotics, it has been used to let robots perform simple household tasks [18] and solve a Rubik's cube with a robot hand. [19] [20] Deep RL has also found sustainability applications, used to reduce energy consumption at data centers. [21]
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
The key is to understand language generation as if it is a game to be learned by RL. In RL, a policy is a function that maps a game state to a game action. In RLHF, the "game" is the game of replying to prompts. A prompt is a game state, and a response is a game action. This is a fairly trivial kind of game, since every game lasts for exactly ...
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Model-free RL algorithms can start from a blank policy candidate and achieve superhuman performance in many complex tasks, including Atari games, StarCraft and Go.Deep neural networks are responsible for recent artificial intelligence breakthroughs, and they can be combined with RL to create superhuman agents such as Google DeepMind's AlphaGo.