Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?

We introduce Agent2 RL-Bench, a compact diagnostic benchmark for evaluating agentic RL post-training, which tests whether LLM agents can autonomously design, implement, debug, and execute post-training pipelines that improve foundation models. RL post-training increasingly drives model alignment and specialization, yet existing benchmarks are largely static, rewarding supervised fine-tuning or script generation without assessing an agent's ability to close an interactive RL loop. Agent2 RL-Bench

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ethics.ai (12 April 2026), “Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?,” evidence record 6006, https://ethics.ai/record/6006 (originally published by arXiv).

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