EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms

In three linesEvalStop detects and stops RLHF jobs that overoptimize the reward model at the expense of real-world metrics. On 80% RLHF workloads (64 GPUs), the system achieves 98% precision and cuts wasted compute by 22% while improving JCT by 9% over SRTF-Est.

Summary generated by Claude — human-verified