arXiv:2509.19305v2 Announce Type: replace Abstract: Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. However, existing approaches primarily focus on time-domain features while overlooking frequency-domain features, leading to frequency shift and degraded performance according to our observation. In this paper, we investigate the RL problem from a new perspective of the frequency domain. We first observe that time-domain-only approaches inadvertently introduce shifts in the low-frequency components of the fr
The paper identifies and proposes a solution to a limitation in current diffusion models for reinforcement learning, indicating an active research front in improving AI's learning capabilities.
This research represents a step towards more robust and efficient reinforcement learning models, which are critical for advanced AI systems and agentic development.
The focus extends beyond time-domain features to incorporate frequency-domain analysis in diffusion models for RL, potentially leading to more stable and performant AI agents.
- · AI researchers
- · Reinforcement learning developers
- · Robotics
- · Current time-domain-only diffusion model approaches
Improved performance and stability in AI systems utilizing diffusion models for reinforcement learning.
Accelerated development of more complex and reliable AI agents capable of operating in dynamic environments.
Increased adoption of reinforcement learning in real-world applications as model weaknesses are addressed.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG