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 provides a unified agent-facing interface: each run starts from an isolated workspace containing a base model, task data, instructions, and a grading API, and agents must iterate within a fixed bu...
| Authors | Wanyi Chen, Xiao Yang, Xu Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 April 2026 |
| Last updated at source | 13 May 2026 |
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