In recent times, large language models (LLMs) have been popular due to their human-like interaction capacity achieved through reinforcement learning. However, aligning these LLMs with human preferences can lead to “reward hacking,” where the models achieve high rewards without meeting the objectives, raising safety concerns.
The main challenges in creating RMs include distribution shifts and inconsistent preferences. Existing methods face issues, so a new strategy called Weight Averaged Reward Models (WARM) has been proposed. WARM combines multiple RMs to address the challenges more efficiently and reliably.
The benefits of WARM include efficiency, reliability under distribution shifts, and privacy improvement. However, WARM has limitations and should be considered in the context of responsible AI to address safety risks.
In conclusion, WARM offers a promising solution to reward modeling challenges, contributing to creating more aligned, transparent, and effective AI systems.
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