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OnGameLearn not only navigates the complexities of strategic interactions but also adapts to evolving contextual signals, achieving superior performance in competitive pricing scenarios.
Achieving SFT-level performance with less than 7% of the computation, Weightless Fine-Tuning revolutionizes how we personalize LLMs without costly weight updates.
Neighbor fusion at test time can refine molecular property predictions by leveraging the most relevant training data, achieving substantial accuracy gains without retraining.
Training data diversity is the secret sauce that boosts agentic model performance, with OpenThoughts-Agent achieving a notable accuracy leap over existing benchmarks.
Delegating prediction tasks in human-AI teams may preserve calibration but imposes a daunting challenge on the rejector model to accurately assess expertise.
