arXiv:2605.21214v1 Announce Type: new Abstract: Reinforcement learning (RL) often exhibits high variance across training runs, leading to unreliable performance and posing a major challenge to deployment in real-world domains. In this work, we address the challenge of cross-run policy divergence by formalizing the problem of behavior-consistent RL, where the objective is to obtain policies that are both high-performing and distributionally similar across training runs. Our key observation is that maximum-entropy RL provides a direct mechanism for controlling behavioral divergence by anchoring
The increasing complexity and real-world application of AI models demand higher reliability and consistency in their training outputs, a problem highlighted by the current variance in RL performance.
Reliable and consistent reinforcement learning is crucial for deploying AI in critical real-world systems, enabling more predictable and trustworthy autonomous operations.
This work introduces a method to achieve greater consistency in RL policy behavior across different training runs, moving toward more robust AI deployment.
- · AI developers
- · Robotics industry
- · Autonomous systems
- · AI-driven manufacturing
- · Developers relying on ad-hoc tuning
- · Industries with low tolerance for AI performance variance
AI models become more reliably deployable in high-stakes environments due to reduced variance.
Increased trust in AI systems could accelerate adoption in sectors like aerospace, healthcare, and finance.
More consistent AI performance might lead to new regulatory frameworks for AI reliability and safety standards.
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Read at arXiv cs.LG