Schulman says user data gains in math are unlikely; disclosure norms needed
As a follow-up, it's exceedingly unlikely that training on user data contributes much to frontier model gains in areas like math -- those...
AISummary
John Schulman argues that training on user data contributes little to frontier math gains, which come mainly from scaling pretraining and RLVR. He says user data is more likely used to find failure modes that hired annotators struggle to recreate. He calls for stronger norms on disclosing how companies train on user data, including the methods and capabilities targeted.
Source: John Schulman · x.com