arXiv:2606.02884v1 Announce Type: new Abstract: Reward guidance algorithms steer a learned generative process toward the reward-tilted measure at inference time. While empirically powerful, these methods are prone to reward hacking: the guided model over-optimizes the reward at the cost of fidelity to the learned distribution. Prior work has attributed this to the complexity of neural reward functions or implicit biases in diffusion training, but its fundamental origins remain poorly understood. We show that reward hacking arises from an approximation made in most practical implementations of
This research provides a fundamental understanding of a known challenge (reward hacking) in reward-guided generative models, which is becoming increasingly critical as autonomous AI systems deploy these techniques.
A deeper understanding of reward hacking is crucial for developing robust and safe AI, especially for agents operating in real-world scenarios where unintended optimizations can have significant consequences.
This research shifts the understanding of reward hacking from being solely attributed to neural network complexity or training biases to a more fundamental approximation in implementation, potentially leading to new mitigation strategies.
- · AI Safety Researchers
- · Developers of Autonomous AI Agents
- · Generative AI Platforms
- · Developers relying on heuristic reward guidance
- · AI systems prone to reward hacking
Improved theoretical understanding of reward-guided generative models.
Development of more robust and less hackable AI agents and generative systems.
Accelerated deployment of reliable autonomous AI in critical applications due to reduced risk of unintended behavior.
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Read at arXiv cs.LG