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TASM achieves high performance in multi-modal tasks while compressing memory by dynamically adapting to task-specific needs without sacrificing semantic integrity.
Forget sifting through mountains of MCTS trajectories – contrasting successful and failed reasoning paths distills 10x more effective training data for reasoning models.
