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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;
Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g. every pixel rendered to the screen in a video game) and decide what actions to perform to optimize an objective (e.g ...
A key breakthrough was LSTM (1995), [note 1] a RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient learning of long-sequence modelling. One key innovation was the use of an attention mechanism which used neurons that multiply the outputs of other neurons, so-called multiplicative units. [13]
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal.
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 ...
The decoder sends in a query, and obtains a reply in the form of a weighted sum of the values, where the weight is proportional to how closely the query resembles each key. The decoder first processes the "<start>" input partially, to obtain an intermediate vector h 0 d {\displaystyle h_{0}^{d}} , the 0th hidden vector of decoder.
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