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DatasetProcessing ModelDistillation
+on+the+synthetic+dataset+to+align+with+various+expert+training+trajectories.+However%252C+traditional+long-range+matching+methods+possess+an+overfitting-like+problem%252C+the+fixed+step+size+NS+forces+synthetic+dataset+to+distortedly+conform+seen+expert+training+trajectories%252C+resulting+in+a+loss+of+generality-especially+to+those+from+unencountered+architecture.+We+refer+to+this+as+the+Accumulated+Mismatching+Problem+(AMP)%252C+and+propose+a+new+approach%252C+Automatic+Training+Trajectories+(ATT)%252C+which+dynamically+and+adaptively+adjusts+trajectory+length+NS+to+address+the+AMP.+Our+method+outperforms+existing+methods+particularly+in+tests+involving+cross-architectures.+Moreover%252C+owing+to+its+adaptive+nature%252C+it+exhibits+enhanced+stability+in+the+face+of+parameter+variations.&cdnurl=https%3a%2f%2fbing.ai.mrmusic.cloudns.ch/th%2fth%2fid%2fR.367d13ec7cbbcb9651dbb95750f4e87f%3frik%3dX7CNpHZsiYxlzw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Dataset+Processing+Model+Distillation&FORM=IRPRST&ck=808C7F7D656A58CD1343A10551368F4C&selectedIndex=26&itb=0)
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