GORMPO uses generative models for density-based regularization in model-based offline RL, outperforming baselines by 17% on a medical dataset while providing theoretical guarantees under mild assumptions.
Diffusion policies as an expressive policy class for offline reinforcement learning
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
SOM is an actor-critic algorithm that constructs the target velocity field for one-step MeanFlow policies directly from the Q-function via score estimation and probability flow ODE, achieving claimed SOTA on locomotion tasks with reduced training and inference time.
PROCO generates synthetic unsafe samples via model-based rollouts and LLM-grounded costs to enable safer policy learning from offline datasets containing few or no violations.
TRFP combines rectified flow models with truncation to support multimodal policies in MaxEnt RL while allowing fast one-step sampling and stable training.
citing papers explorer
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Generative OOD-regularized Model-based Policy Optimization
GORMPO uses generative models for density-based regularization in model-based offline RL, outperforming baselines by 17% on a medical dataset while providing theoretical guarantees under mild assumptions.
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Score-Based One-step MeanFlow Policy Optimization
SOM is an actor-critic algorithm that constructs the target velocity field for one-step MeanFlow policies directly from the Q-function via score estimation and probability flow ODE, achieving claimed SOTA on locomotion tasks with reduced training and inference time.
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Model-Based Proactive Cost Generation for Learning Safe Policies Offline with Limited Violation Data
PROCO generates synthetic unsafe samples via model-based rollouts and LLM-grounded costs to enable safer policy learning from offline datasets containing few or no violations.
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Truncated Rectified Flow Policy for Reinforcement Learning with One-Step Sampling
TRFP combines rectified flow models with truncation to support multimodal policies in MaxEnt RL while allowing fast one-step sampling and stable training.