PrefPI: Preference-Guided Steering into Out-of-Distribution Behaviors

arXiv:2609.40165 · cs.RO, cs.AI, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.RO, cs.AI, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Steering — 40× in this paper · explained in 2 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Reward Function — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Direct Preference Optimization — 4× in this paper
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Iterative Filtered Behavior Cloning — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes

Sources

• Diffusion Guidance Is a Controllable Policy Improvement Operator
• Classifier-Free Diffusion Guidance
• FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization
• GRAPE: Generalizing Robot Policy via Preference Alignment

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