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Found 9 papers, 1 papers with code
Policy Optimization via Stochastic Recursive Gradient Algorithm
In this paper, we propose the StochAstic Recursive grAdient Policy Optimization (SARAPO) algorithm which is a novel variance reduction method on Trust Region Policy Optimization (TRPO).
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes.
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tuning Large Language Models (LLMs).
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
In this paper, we delve into the prospect of growing a strong LLM out of a weak one without the need for acquiring additional human-annotated data.
Fast Sampling via De-randomization for Discrete Diffusion Models
Despite its success in continuous spaces, discrete diffusion models, which apply to domains such as texts and natural languages, remain under-studied and often suffer from slow generation speed.
Stochastic Recursive Momentum for Policy Gradient Methods
In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced policy gradient in an exponential moving average fashion.
Stochastic Modified Equations for Continuous Limit of Stochastic ADMM
In this work, we put different variants of stochastic ADMM into a unified form, which includes standard, linearized and gradient-based ADMM with relaxation, and study their dynamics via a continuous-time model approach.
Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization
Such a complexity is known to be the best one among IFO complexity results for non-convex stochastic compositional optimization, and is believed to be optimal.
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent
Stochastic compositional optimization arises in many important machine learning tasks such as reinforcement learning and portfolio management.
