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Language agents can achieve superior sample efficiency and performance by jointly learning to act and forecast multi-step action sequences within a single policy backbone.
GPT-4.1 can predict future research ideas with higher accuracy than its competitors, revealing the nuanced interplay between model architecture and forecasting ability.
Tail-focused evaluation reveals that traditional methods can misrepresent job runtime predictions, with UserReq outperforming other models in critical scenarios.