Reinforcement Learning via Advantage Guidance

arXiv:2610.19783 · cs.LG · Submitted 2026-10-14 · Read on arXiv

cs.LG

Submitted: 2026-10-14

Updated: 2026-10-19

Code: https://github.com/zhouzypaul/advantage-guidance

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• $\pi^{*}_{0.6}$: a VLA That Learns From Experience
• Training Diffusion Models with Reinforcement Learning
• Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
• One-Step Flow Policy Mirror Descent
• Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
• RvS: What is Essential for Offline RL via Supervised Learning?
• Advantage-Guided Diffusion for Model-Based Reinforcement Learning
• Diffusion Guidance Is a Controllable Policy Improvement Operator
• Extreme Q-Learning: MaxEnt RL without Entropy
• IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
• DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
• Classifier-Free Diffusion Guidance
• Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning
• Offline Reinforcement Learning with Implicit Q-Learning
• Reward-Conditioned Policies
• When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
• Reinforcement Learning with Action Chunking
• Flow Matching for Generative Modeling
• Flow-Based Policy for Online Reinforcement Learning
• AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

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