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Short-context models can achieve superior reasoning performance by leveraging long-context teacher models through innovative token alignment and training strategies.
Action-Grounded Representation Alignment (AGRA) transforms how robots interpret visual data, enabling them to focus on crucial interaction regions and significantly enhancing manipulation performance.
Even the best LLMs struggle with Olympiad-level combinatorics, achieving only 65.4% on a benchmark designed to expose their reasoning limitations.
