OpenBMB introduces AutoRLBench to test whether LLM agents can debug failed RL runs

AIOpenBMB introduces AutoRLBench, a benchmark that tests 10 frontier agents on real failed reinforcement learning runs with original code, configs, logs, and metrics. The best model reaches about 40% accuracy, performing better on explicit config bugs than implicit data bugs, and stronger agents diagnose faster using fewer tokens.

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The post shows how the benchmark tests agents on real failed RL runs, separating explicit config bugs from harder implicit data bugs.

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🔧 Can an LLM agent debug a broken RL training run?

Introducing AutoRLBench — a unified benchmark putting 10 frontier agents to the test on real-world, failed RL runs.

Full code, configs, logs, and metrics are kept intact. No synthetic bugs. The goal? Pinpoint the exact root cause behind deceptively "healthy-looking" reward curves.

📊 Key findings:
- Even the best frontier model barely reaches ~40%, with a massive capability gap across tiers.
- Models perform best on explicit Config bugs, but struggle severely on implicit Data bugs.
- Stronger agents diagnose faster and consume fewer tokens than weaker ones.

In RL, the hardest failure to catch is the one that still looks like learning.

📄 Website: https://thinking-space.github.io/AutoRLBench/
💻 Code: https://github.com/Thinking-Space/AutoRLBench

#RL #LLM #Agent #Benchmark #PostTraining #RSI

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OpenBMB · x.comPublished Nov 9, 8:30 AM ET · added here 4:12 PM ET

Original title: 🔧 Can an LLM agent debug a broken RL training run?

OpenBMB introduces AutoRLBench to test whether LLM agents can debug failed RL runs

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