Improving Zero-Shot Offline RL via Behavioral Task Sampling

📰 ArXiv cs.AI

Improve zero-shot offline RL with behavioral task sampling to learn agents that optimize unseen reward functions

advanced Published 29 Apr 2026

Action Steps

Sample task vectors using behavioral task sampling to capture the structure of the task space
Train task-conditioned policies using the sampled task vectors
Evaluate the performance of the policies on unseen reward functions
Compare the results with random task vector sampling to demonstrate the improvement
Apply behavioral task sampling to other offline RL algorithms to further improve their performance

Who Needs to Know This

Researchers and engineers working on reinforcement learning can benefit from this approach to improve the efficiency of their models

Key Insight

💡 Behavioral task sampling can improve the efficiency of zero-shot offline RL by capturing the structure of the task space

Key Takeaways

Improve zero-shot offline RL with behavioral task sampling to learn agents that optimize unseen reward functions

Full Article

Title: Improving Zero-Shot Offline RL via Behavioral Task Sampling

Abstract:
arXiv:2604.25496v1 Announce Type: new Abstract: Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this problem trains task-conditioned policies by sampling task vectors that define linear reward functions over learned state representations. In most existing algorithms, these task vectors are randomly sampled, implicitly assuming this adequately captures the structure of the

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