AgenticRL-Bench: Evaluating AI Agents on Production-Grade Reinforcement Learning Tasks

The Environment Lab at Eigent AI has introduced AgenticRL-Bench, a benchmark featuring 22 tasks rebuilt from real-world reinforcement learning incidents on production-grade agentic systems. In initial evaluations across six agents, models successfully passed no more than 14.0% of verifiable tasks, while open-ended task testing demonstrated that agents could independently improve real policies or training steps using specific rollout and monitoring methods. Open article →