Training Language Models to Fail Gracefully
Reinforcement learning makes language models better at solving tasks, but the same mechanism also rewards guessing.
ProofPaper ↗
Key points
- Therefore, we propose training models to fail gracefully: to preserve successful behavior whenever possible while learning to execute a safe fallback policy when acting would be unreliable.
- However, naively using policy gradients with intermediate reward for fallback behavior is brittle due to unfavorable optimization dynamics.
- We address this challenge with GracefulRL, which reshapes the optimization landscape to promote graceful failure.
- More broadly, our results show that reinforcement learning can not only incentivize models to succeed, but also to reason about the limits of their capabilities and change course when they determine that success is out of reach.
Sources (1)
- [1]Training Language Models to Fail GracefullyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 04:03 PM
Reinforcement learning makes language models better at solving tasks, but the same mechanism also rewards guessing.
Therefore, we propose training models to fail gracefully: to preserve successful behavior whenever possible while learning to execute a safe fallback policy when acting would be unreliable.
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