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DPO eliminates the need for a separate reward model or reinforcement learning loop, treating alignment as a supervised learning problem over preference data. This is simpler to implement and train than RLHF and has been shown to produce comparable and sometimes superior results. [45]
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.
A major technical contribution is the departure from the exclusive use of Proximal Policy Optimization (PPO) for RLHF – a new technique based on Rejection sampling was used, followed by PPO. Multi-turn consistency in dialogs was targeted for improvement, to make sure that "system messages" (initial instructions, such as "speak in French" and ...
AI alignment involves ensuring that an AI system's objectives match those of its designers or users, or match widely shared values, objective ethical standards, or the intentions its designers would have if they were more informed and enlightened. [40] AI alignment is an open problem for modern AI systems [41] [42] and is a research field ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
A comprehensive comparison of comparative RNA structure prediction approaches: Yes: No: No: data [180] BRalibase II A benchmark of multiple sequence alignment programs upon structural RNAs: No: Yes: No: data [181] BRalibase 2.1 A benchmark of multiple sequence alignment programs upon structural RNAs: No: Yes: No: data Archived 2010-05-25 at the ...
Constructive alignment is the underpinning concept behind the current requirements for programme specification, declarations of learning outcomes (LOs) and assessment criteria, and the use of criterion based assessment. There are two basic concepts behind constructive alignment: Learners construct meaning from what they do to learn.
[29] [13] The weighting is proposed to accelerate the convergence of dynamic programming and correct for effects arising from alignment lengths. In a benchmarking study, TM-align has been reported to improve in both speed and accuracy over DALI and CE. [29] Other promising methods of structural alignment are local structural alignment methods.