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However, the performance of such models is often limited by the\nquantity and diversity of high-quality human data. In this paper, we explore\nwhether we can go beyond human data on tasks where we have access to scalar\nfeedback, for example, on math problems where one can verify correctness. To do\nso, we investigate a simple self-training method based on\nexpectation-maximization, which we call ReST^{EM}, where we (1) generate\nsamples from the model and filter them using binary feedback, (2) fine-tune the\nmodel on these samples, and (3) repeat this process a few times. Testing on\nadvanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find\nthat ReST^{EM} scales favorably with model size and significantly surpasses\nfine-tuning only on human data. Overall, our findings suggest self-training\nwith feedback can substantially reduce dependence on human-generated data.","upvotes":29,"discussionId":"6577f1e4ee33d547aebf89c0","ai_summary":"Self-training with scalar feedback using expectation-maximization improves language model performance on tasks with verifiable answers, reducing reliance on human data.","ai_keywords":["self-training","expectation-maximization","scalar feedback","MATH reasoning","APPS coding benchmarks","fine-tuning"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63f974b0aadb0790bb60cd65","avatarUrl":"/avatars/5ed67e7def00b7d1117a00048d0a6e0f.svg","isPro":false,"fullname":"James Liu","user":"jamesliu23","type":"user"},{"_id":"631e40a4b07bce5f2a7faa38","avatarUrl":"/avatars/999e8f6a820d7145b60a99eb16bd7f35.svg","isPro":false,"fullname":"Ogbogu Kalu","user":"ogkalu","type":"user"},{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"},{"_id":"63284f86cbc744f197050300","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63284f86cbc744f197050300/cGbUDe5fn-8A8Jcmz5lre.png","isPro":false,"fullname":"Hoptimizer","user":"bunnycore","type":"user"},{"_id":"612246596d9ce900691744d2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/612246596d9ce900691744d2/9DlHVQDqblKz7QPTA6nDa.jpeg","isPro":false,"fullname":"Edoardo Federici","user":"efederici","type":"user"},{"_id":"6311bca0ae8896941da24e66","avatarUrl":"/avatars/48de64894fc3c9397e26e4d6da3ff537.svg","isPro":false,"fullname":"Fynn Kröger","user":"fynnkroeger","type":"user"},{"_id":"60c8d264224e250fb0178f77","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60c8d264224e250fb0178f77/i8fbkBVcoFeJRmkQ9kYAE.png","isPro":true,"fullname":"Adam Lee","user":"Abecid","type":"user"},{"_id":"65554e5b9518733aca07b9e3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65554e5b9518733aca07b9e3/kkvzuINCuGEuw3VNsfBr7.jpeg","isPro":false,"fullname":"Datorien Anderson","user":"niltheory","type":"user"},{"_id":"630412d57373aacccd88af95","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1670594087059-630412d57373aacccd88af95.jpeg","isPro":true,"fullname":"Yasunori Ozaki","user":"alfredplpl","type":"user"},{"_id":"63c3850f6e6561b339e68f24","avatarUrl":"/avatars/23c06fb21107267a717a833d1f5e2b8e.svg","isPro":false,"fullname":"nyoro","user":"nyoronyoro","type":"user"},{"_id":"6362e3412a84d82a8c92be83","avatarUrl":"/avatars/d26aa485d24d4c915c61029361f401be.svg","isPro":false,"fullname":"Error PDF","user":"Error999","type":"user"},{"_id":"6343f83791049e1bce85373e","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1665398834110-noauth.png","isPro":false,"fullname":"Zhang ning","user":"pe65374","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":2}">