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+training+data.+Yet%252C+the+widespread+use+of+popular+models+means+that+the+ecosystem+of+online+data+and+text+will+co-evolve+to+progressively+contain+increased+amounts+of+synthesized+data.+In+this+paper+we+ask%3a+How+will+the+scaling+laws+change+in+the+inevitable+regime+where+synthetic+data+makes+its+way+into+the+training+corpus%253F+Will+future+models%252C+still+improve%252C+or+be+doomed+to+degenerate+up+to+total+(model)+collapse%253F+We+develop+a+theoretical+framework+of+model+collapse+through+the+lens+of+scaling+laws.+We+discover+a+wide+range+of+decay+phenomena%252C+analyzing+loss+of+scaling%252C+shifted+scaling+with+number+of+generations%252C+the+%27%27un-learning+of+skills%252C+and+grokking+when+mixing+human+and+synthesized+data.+Our+theory+is+validated+by+large-scale+experiments+with+a+transformer+on+an+arithmetic+task+and+text+generation+using+the+large+language+model+Llama2.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.14f44a7be00594693b25df36e58957c3%3frik%3dc1yle9hFnL77xg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Tails+Leach+Machine+Learning+Model&FORM=IRPRST&ck=AD640EBB4E6A2C3E494EF38541A837D7&selectedIndex=14&itb=0)
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