AutoRLBench: A Benchmark Testing AI Agents on Real RL Training Failures
wzenus · x · 2026-11-03
The Thinking Space team launched AutoRLBench, the first benchmark that measures AI agents' ability to diagnose and repair real RL training failures.
The idea: most AI-for-AI evaluation is end-to-end — agents run the full pipeline and return only a pass/fail, hiding where things break. AutoRLBench isolates diagnosis and repair: the agent reads the workspace, logs and metrics, finds the root cause, and where a fix exists, repairs and reruns.
Scale: 6 failure categories, 21 tasks (15 support solution-level evaluation), still collecting; 2 evaluation levels (Issue Score and Solution Score). The leaderboard already compares agents by category, time and cost efficiency.
Call for contributions: submit the run that confused you for a week as a benchmark task; two valid tasks earn co-authorship on the research publication, and all tasks are open-sourced.
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