arXiv:2605.27079v1 Announce Type: new Abstract: Off-policy reinforcement learning of pretrained flow policies remains challenging due to the instability of optimization arising from the multi-step sampling process. Recently, Q-learning with Adjoint Matching (QAM) addressed this issue by reformulating into a memoryless stochastic optimal control (SOC) problem with a learned critic. However, QAM inherits a fundamental fragility of critic-guided improvement: small critic errors are amplified when critics are ill-conditioned, often leading to model collapse. This paper introduces Trust Region Q-Ad
The continuous development in reinforcement learning, particularly addressing stability in off-policy methods, is crucial for advancing AI capabilities and is a current focus for research.
Improved stability in off-policy reinforcement learning can accelerate the development of more robust AI agents for complex real-world applications, especially those requiring pre-trained policies.
The introduction of Trust Region Q Adjoint Matching offers a more stable optimization technique, mitigating issues of model collapse and enabling more reliable learning from pre-trained policies.
- · AI researchers
- · Reinforcement learning applications
- · Robotics
- · Autonomous systems
- · Less stable RL algorithms
- · Optimization methods prone to model collapse
More stable and efficient training of complex AI models becomes possible.
Faster deployment of advanced AI agents in high-stakes environments due to increased reliability.
Accelerated innovation in areas like robotics and agentic systems, potentially leading to more sophisticated autonomous behaviors.
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Read at arXiv cs.LG