Jàngat bu am doole ci feedback nit
RLHF mooy pexem biy soppi xeetu làkk bu ñor mu nekk assistant bu am njariñ, diko tàggat ci tànneefi doomi aadama yi.
Résumé
It matters because it aligns model behavior with what people actually want, not just what is statistically likely.
Plongeur bu xóot
Royuwaayu làkk buñ tàggat bu njëkk dafay wax luy waaja am ci mbind mu wóor, waaye lu wóor wuute na ak jàppale, njub, wala wóor. RLHF dafay saafara jafe-jafe yii. Bi njëkk mooy, fine-tuning biñ yor dafay jàngal model bi topp tegtal yi ci jëfandikoo misaali tontu yu nit ñi bind. Bi ci topp, nit ñi dañuy méngale ñaari tontu ci benn laaj ba noppi tànn bi gëna baax; méngale yooyu dañuy tàggat xeetu neexal bu wuute buy joxe poñ ci tontu bu nekk. Fi may jeexalee mooy xeetu làkk wi dañu ko gëna suqali ak jàng buy dooleel ngir mëna joxe tontu ci xeetu neexal bi gëna rëy. Penalty daf koy tere dem fu sori lool ci modelu original bi suko defee mu mëna wax bu baax te baña jëfandikoo quirks modelu neexal bi. RLHF nekkoon na lu am solo ci defar xeetu assistant yu ñuy jëfandikoo AIU_PROTECTED_7__
Gis-gis xarala
Royuwaayu neexal bi dañu koy faral di tàggat ci ñaari tànneef yu am ñàkkaale bu nuru Bradley-Terry, jàng jox tontu bi nit ñi taamu poñ scalar bu gëna rëy. Ginaaw loolu ñu yeesal politik bi ak PPO (Proximal Policy Optimization), luy yokk neexal bi ci noonu la KL-divergence penalty ci xeetu royuwaay bi di tere gëna xéewale ak 'pirateri neexal'. Ndax PPO dafa jafe, pexe yu bees yu melni DPO (Direct Preference Optimization) dañuy jël xeetu neexal bu leer bi ak loop buy dooleel, di gëna xéewale politik bi ci ñaari tànneef.
njeextalu pexe
Njëgg ak budget
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
dogal yu gëna leer
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Xool kalite
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Ëlëg jàng bu am doole ci feedback nit
RLHF ñu ngi koy yombal ba noppi ñu def ko ci otomatik. DPO ak pexe yu jëm ci tànneef yu direct ñu ngi wecci gasoduc PPO bu diis bi ci ekip yu bari, te RLAIF dafay jëfandikoo feedback bi IA defar (ni ci IA konstitusioneel) ngir wàññi njëgu etiketu. Gëstu mingi xeex pirateri neexal, tënk annotatër, ak jafe-jafe àtte tontu yu gudd wala yu xarañ, ak pexe yu melni saytu liggéey ak waxtaan. Xaarandil alignment ngir boole feedback nit ak IA, siñaal neexal yu gëna riis yu weesu benn baraam, ak saytu buy gëna yokk ci kiy joxe tànneef yi ak ban valeur lañuy kode.
Doxal ci àdduna dëgg
Tuning assistant chat suko defee mu baña nangu ay laaj yu mëna lore, ba noppi di joxe tontu yu am njariñ, yu jaar yoon, du ay mbind yu leer rek.
Ranking ñaari resumé ci tànneefi nit ñi ngir tàggat ab model buy bind resumé yu nit ñi gis ni am njariñ.
Wàññi li toxic wala génne lu jaarul yoon ci joxe tontu yu nit ñi jàpp ni dañu rafet te amul benn sikk.
Jëfandikoo DPO ci kaw benn done bu tontu yiñ taamu ak yiñ bañ ngir méngale ab xeetu open-source te doo def benn loop PPO bu mat.
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Benchmark ci biir sargal ak done yu dëggu.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
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Gis bi ci topp
Jàng buy dooleel
Laaj yi ñuy faral di laaj
What is Reinforcement Learning From Human Feedback?
RLHF mooy pexem biy soppi xeetu làkk bu ñor mu nekk assistant bu am njariñ, diko tàggat ci tànneefi doomi aadama yi. Dafa am solo ndax dafay méngale xeetu doxalin ak li nit ñi bëgg, te baña yam ci li ñu mëna am ci xayma.
Lan mooy jubluwaay bi gëna mag ci RLHF ci misaalu làkk?
RLHF dafay teg xeetu tontu yi nit ñi taamu, muy gëna am njariñ, gëna dëggu, gëna wóor moo gën ñu gëm rek.
Lan la xeetu neexal bi ci RLHF jàng def?
Royuwaayu neexal bi dañu ko tàggat ci méngale tànneefi nit ñi ngir am poñ ci tontu yi, di joxe siñaal bi politik bi di gëna xéewale.
Lan moo waral ñuy teg KL-divergence penalite ci xeetu njëkk bi ñu jëfandikoo ci jéego RL bi?
Penalty KL dafay tëye politik biñ gëna defar mu jege modelu royuwaay bi, di moytu pirate neexal ak ñàkka mëna wax bu baax.
naka lañuy faral di seddalee done yiñ taamu ngir xeetu neexal bi?
Annotateur yi dañuy tànn tontu biñ taamu ci méngale ñaari yoon, loolu mooy tàggat xeetu neexal bi jaaraleko ci ñàkkaale bu nuroo ak Bradley-Terry.
Ban njariñ la DPO (Optimisation préférence directe) di joxe ci pipeline RLHF bu yàgg bi?
DPO mingi jëlee mébet bi ci tànneef yi, moytu xeetu neexal bu wuute ak jéego jàng buy dooleel.