Daniel Kang
@ddkang
Distinguished AI research scientist at Bridgewater AIA labs, asst. professor at UIUC CS. Formerly in the Stanford DAWN lab and the Berkeley Sky Lab.
Joined November 2010
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New research from Bridgewater AIA Labs, UIUC, and MIT: we prove what we believe to be the first non-vacuous generalization bounds for reasoning LLMs on real-world problems.
RLVR powers frontier reasoning capabilities yet its generalization to unseen data has remained an open theoretical question and deployment blocker for practitioners. Our generalization bounds for RLVR deliver provable high-probability lower bounds of the accuracy for billion-parameter RLVR models on unseen data, which can provide guidance on safely deploying RLVR.
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