arXiv:2606.28955v1 Announce Type: new Abstract: Reinforcement learning agents can exploit misspecified reward signals to achieve high apparent returns while failing on the intended objective, a failure mode known as reward hacking. Existing practical defenses typically constrain policy updates to stay near a known safe reference, creating a tension between suppressing hacking and permitting legitimate improvement. We propose Modification-Considering Value Learning (MCVL), which operationalizes the theoretical idea of current utility optimization for standard value-based RL. MCVL wraps an off-p
The proliferation of AI agents and increasingly complex reinforcement learning systems necessitates robust solutions for alignment and preventing unintended behaviors like reward hacking.
This research addresses a fundamental challenge in AI safety, crucial for deploying advanced AI systems reliably and effectively across various applications.
A new methodological approach, MCVL, is introduced that aims to mitigate reward hacking in RL agents, offering a path towards more aligned and trustworthy AI.
- · AI developers
- · Organizations deploying RL agents
- · AI safety researchers
- · AI systems prone to reward hacking
- · Ineffective RL alignment methods
More robust and predictable behavior from reinforcement learning agents in complex environments.
Accelerated adoption of RL in safety-critical applications due to improved alignment guarantees.
Enhanced overall public trust in autonomous AI systems as they become less susceptible to unintended strategic exploitation.
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Read at arXiv cs.LG