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This work proposes training models to fail gracefully: to preserve successful behavior whenever possible while learning to execute a safe fallback policy when acting would be unreliable, and shows that reinforcement learning can not only incentivize models to succeed, but also to reason about the limits of their capabi...
Meta-reasoning, an inference-time harness that makes agentic meta-reasoning an explicit and structured reasoning process, keeps improving over the tested budget ranges where direct control plateaus, though its overhead can hurt at small budgets.
A language-model agent is jointly defined by its model and its harness, the executable program that organizes model calls, tool use, and information flow. Because different tasks call for different ways of organizing these operations, the harness needs to be adapted using feedback from the task at hand. We introduce ha...
Tail-Likelihood Reinforcement Learning (TailRL), which maximizes the log-probability of exceeding a randomly chosen reward threshold, which gives more weight to rare, high-reward rollouts and can be interpreted as a mixture of Best-of-k gradients.
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