Hybrid Sequence Modeling and Reinforced Verification for Controllable Target-Conditioned Decision Making
arXiv:2508.16420v3 Announce Type: replace Abstract: Target-conditioned sequence models provide a simple interface for controllable offline decision making, but the requested target return can be an unreliable control signal, especially when the target return lies in underrepresented regions of the dataset. This paper proposes Doctor, a hybrid sequence modeling and reinforced verification framework for controllable target-conditioned offline decision making. Doctor trains a shared masked trajectory Transformer with two complementary objectives: masked trajectory reconstruction for candidate gen
This paper addresses a fundamental challenge in applying target-conditioned sequence models effectively in real-world decision-making scenarios where data reliability can be an issue.
Improved reliability and controllability in AI decision-making systems will accelerate their adoption across various critical applications, making them more robust and trustworthy.
The ability to ensure target conditions are met even with unreliable data input enhances the practical utility and safety of AI agents in complex environments.
- · AI developers
- · Robotics
- · Autonomous systems
- · Logistics and supply chain
- · Manual decision-making processes
- · Inefficient AI control mechanisms
More robust and deployable AI agents capable of operating within specified constraints despite data imperfections.
Increased trust in AI-driven autonomous systems, leading to wider adoption in safety-critical sectors.
The acceleration of AI automation in workflows previously deemed too complex or risky for agentic systems.
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