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Found 5 papers, 1 papers with code
Averaging log-likelihoods in direct alignment
To reconcile these approaches, we introduce a principled approach for making direct alignment length-invariant.
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
Reinforcement Learning (RL) has been used to finetune Large Language Models (LLMs) using a reward model trained from preference data, to better align with human judgment.
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models.
Inference Suboptimality in Variational Autoencoders
Furthermore, we show that the parameters used to increase the expressiveness of the approximation play a role in generalizing inference rather than simply improving the complexity of the approximation.
Reinterpreting Importance-Weighted Autoencoders
The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound.
