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On-policy RL for machine learning engineering agents is now practical, thanks to a synthetic sandbox that slashes execution time by 13x while boosting performance by up to 67%.
LLMs still struggle to accurately infer user interests from interaction histories, especially when dealing with diverse engagement signals – a critical gap for effective personalization.
Achieve significant reasoning gains in frozen LLMs (+22.4%) without retraining by adaptively routing reward model guidance at the token level during inference.
