Photo by charly barcelona on Pexels

A newly released guide offers practical strategies for building more robust reinforcement learning (RL) agents that are less prone to exploiting loopholes in their reward systems. The resource emphasizes the use of ‘verifiable rewards’ to ensure AI behavior aligns with intended goals. Key topics covered include a unified approach to interpreting reward signals, KL divergence, and entropy, along with the implementation of layered verifiable rewards addressing structure, semantics, and behavior. The guide also delves into curriculum scheduling and the use of safety, latency, and cost ‘gates’ to constrain agent actions. The author provides a starter configuration for Transformer Reinforcement Learning (TRL) and example reward snippets, and is actively soliciting feedback on real-world failure modes, potential pitfalls in metric selection, and enhancements to gating strategies. The original post can be found on Reddit.