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WIDE achieves a remarkable 55.1% performance boost at 50% sparsity, revolutionizing how LLMs can efficiently allocate computation at the token level.
MADA-RL boosts compact model reasoning accuracy by 2% with 16 times fewer trainable parameters, redefining how critics learn from generators.
