Scaling Offline RL via Efficient and Expressive Shortcut Models (2505.22866v1)

Abstract: Diffusion and flow models have emerged as powerful generative approaches capable of modeling diverse and multimodal behavior. However, applying these models to offline reinforcement learning (RL) remains challenging due to the iterative nature of their noise sampling processes, making policy optimization difficult. In this paper, we introduce Scalable Offline Reinforcement Learning (SORL), a new offline RL algorithm that leverages shortcut models - a novel class of generative models - to scale both training and inference. SORL's policy can capture complex data distributions and can be trained simply and efficiently in a one-stage training procedure. At test time, SORL introduces both sequential and parallel inference scaling by using the learned Q-function as a verifier. We demonstrate that SORL achieves strong performance across a range of offline RL tasks and exhibits positive scaling behavior with increased test-time compute. We release the code at nico-espinosadice.github.io/projects/sorl.

Overview of "Scaling Offline RL via Efficient and Expressive Shortcut Models"

The paper "Scaling Offline RL via Efficient and Expressive Shortcut Models" proposes a novel approach to addressing critical challenges in scaling offline reinforcement learning (RL). Traditional RL techniques face hurdles in efficiently training models that are expressive enough to handle complex, multimodal data distributions while ensuring rapid and precise inference. This paper introduces a new algorithm, termed Scalable Offline Reinforcement Learning (SORL), which leverages shortcut models to optimize both training and inference in offline RL scenarios.

Key Contributions

1. Shortcut Models for Efficient Policy Optimization: The paper introduces shortcut models—a generative modeling technique that enhances inference efficiency by predicting the directions in which larger steps can be taken, as opposed to traditional flow and diffusion models that rely on small, incremental steps. These shortcut models are conditioned not only on timesteps but also on step sizes, enriching expressivity during policy optimization.
2. Unified Training Framework: SORL utilizes a single-stage training framework that incorporates self-consistency. This allows the policy to be optimally trained across varying inference-time compute budgets. The self-consistency property enables the model to adapt to different denoising steps, balancing efficiency and expressivity.
3. Theoretical Regularization to Behavior Policy: The authors provide a theoretical analysis demonstrating that SORL regularizes the learned policy to the behavior policy derived from the offline data. This is achieved through a Wasserstein behavioral regularization approach, ensuring the policy remains close to the behavior of the offline data.
4. Empirical Evaluation: Extensive empirical evaluations underscore the effectiveness of SORL compared to baseline methods across diverse offline RL tasks. The algorithm demonstrates strong performance, highlighting its ability to scale effectively with increased compute during test time.

Strong Numerical Results

The paper reports that SORL achieves superior performance on several benchmark tasks, outperforming ten baseline methods in environments such as large-scale navigation tasks and complex robot manipulation scenarios. Notable improvements were seen in average success rates, with SORL attaining near-optimal scores in environments like antmaze and antsoccer, indicating its robustness across various settings.

Implications and Future Directions

• Practical Impact: The development of SORL presents substantial practical implications for offline RL in applications requiring high precision, such as autonomous driving and surgical robots. Its ability to efficiently scale inference-time computation opens new avenues for deploying RL models in real-world scenarios that demand both rapid decision-making and adaptive response.
• Theoretical Advancements: The theoretical insights into Wasserstein regularization highlight new possibilities for guaranteeing policy stability and fidelity to real-world data, advancing the understanding of model consistency in generative RL applications.
• Future Developments in AI: The notion of shortcut models offers promising directions for future research in AI, particularly in developing more sophisticated generative models that can bridge the gap between efficient training and robust inference.

In conclusion, the paper successfully addresses inherent limitations in offline RL scalability, providing both theoretical contributions and empirical evidence to support the efficacy of SORL. By integrating shortcut models, the approach offers a compelling solution for leveraging offline datasets to train high-performance RL models, paving the way for more adaptive and scalable AI systems.