# Bad Influence story: Eigent AI introduces AgenticRL-Bench, a benchmark for agentic reinforcement learning

Active · 1 article · 1 source · First reported 11-11 17:00. Times are ET.
Story page: https://badinfluence.news/story/5668b881-1724-4e82-8a03-e508ec3ac7ae

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[Bad Influence external source material begins]
Latest update (11-11 17:00): Eigent AI introduces AgenticRL-Bench, a benchmark for agents doing agentic RL

Overview: Eigent AI, through The Environment Lab, has introduced AgenticRL-Bench, a benchmark of 22 tasks rebuilt from real reinforcement learning incidents in systems including Miles, Slime, Verl, SGLang and Harbor. Per the report, on the 19 verifiable tasks none of the six agents passed more than 14.0% of attempts. On one open-ended task, the best agent raised pass@1 from 0 to 0.186 using a self-developed method on 4×H200 GPUs over 10 to 16 hours. RadixArk, which says Miles helped power the effort, congratulated the team. The Environment Lab's announcement describes AgenticRL-Bench as the first release from its effort at Eigent AI.

Coverage timeline (newest first):
1. 11-11 17:00 · RadixArk · [Eigent AI introduces AgenticRL-Bench, a benchmark for agents doing agentic RL](https://badinfluence.news/items/n5bbq9qmgnimt17b2lv09dwhw)
[Bad Influence external source material ends]

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