RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards
Quick summary
arXiv:2608.24275v2 Announce Type: replace Abstract: Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware safeguards mainly rely on prompting or supervised fine-tuning, limiting their ability to adapt to unseen trajectories and changing policy contexts. We propose RePolicy, an agent safeguard that learns safety-policy invocation through reinforcement learning. Given an agent trajectory and a dynamic policy library, RePolicy invokes the applicable policy and uses its content to produce a policy-ground
Key takeaways
- arXiv:2608.24275v2 Announce Type: replace Abstract: Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies.
- Existing policy-aware safeguards mainly rely on prompting or supervised fine-tuning, limiting their ability to adapt to unseen trajectories and changing policy contexts.
- We propose RePolicy, an agent safeguard that learns safety-policy invocation through reinforcement learning.
Why it matters
“RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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