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arXiv Machine Learning By Daman Arora, Xuecheng Liu, Himanshu Gaurav Singh, Fahim Tajwar, Abitha Thankaraj, Ruslan Salakhutdinov, Haiwen Feng, Yuda Song, Andrea Zanette

Training Language Models to Fail Gracefully

Read the original on arXiv Machine Learning →

The paper proposes a method called GracefulRL to train language models to fail gracefully. By reshaping the reinforcement learning optimization landscape, the models learn to preserve successful behavior while executing a safe fallback policy when actions are unreliable, all from outcome-level reward signals without extra annotations or inference-time checks. Experiments across various tasks and scales show that models maintain task-solving capability while selectively falling back when success is unlikely.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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