arXiv:2606.24962v1 Announce Type: new Abstract: Recent progress in large-scale sequence modeling has shown that a single model can learn useful representations across highly diverse data distributions. Inspired by these advances, we investigate whether a unified transformer policy can be trained across large collections of heterogeneous reinforcement learning environments. We introduce LDM-v0, a Large Decision Model trained offline on trajectories collected from thousands of environments spanning multiple domains and modalities. LDM-v0 is a multi-task, multi-modal transformer policy conditione
The proliferation of large language models and vast datasets enables the application of similar architectural principles to reinforcement learning, pushing towards generalized AI agents.
This work indicates a significant step towards unified AI systems capable of performing diverse tasks across multiple domains, converging current AI research strands.
AI development shifts from specialized models to generalist architectures, potentially accelerating the creation of highly adaptable and autonomous agents.
- · AI research institutions
- · Robotics
- · Software automation
- · Cloud computing providers
- · Specialized AI platform developers
- · Companies relying on niche AI solutions
- · Low-skill manual labor
The ability to train a single transformer policy across diverse RL environments streamlines AI development and resource allocation.
Reduced need for task-specific AI models could consolidate AI development platforms and foster broader adoption of unified AI systems.
Generalized decision models could enable highly versatile and autonomous agents that reshape industries by automating complex, multi-faceted tasks.
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Read at arXiv cs.LG