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Frontier models can be used sparingly for long-horizon tasks by a weak agent that learns to predict when it needs help, then keeps the strong model on a leash.
Post-training LLMs for agentic tasks can drastically reduce solution coverage, a "Sharpening Tax" that a novel Bayesian sampler can mitigate.
Progress advantage reveals a powerful, annotation-free scoring mechanism that outperforms traditional reward models in LLM agentic settings.
Discovering when a robot's about to fail just got easier: Hide-and-Seek pinpoints failure signals in VLA trajectories using only coarse, trajectory-level labels, ditching the need for expensive step-by-step annotations.