Overview
Muenzaniso wemubairo ndeye neural network yakadzidziswa kufanotaura kuti yakanaka sei mhinduro yeAI, ichiita seyakazvimiririra yekumira-mukati yekutonga kwevanhu. Ndiyo injini yezvibodzwa inoita kuti kudzidza kwekusimbisa kubva kumhinduro dzevanhu kugoneke pamwero.
Reward Modelling chikamu chemutauro-AI stack inoshandiswa kuverenga, kugadzira, kuronga, uye kushandura zvinyorwa uye kutaura pamwero.
Deep Dive
Reward modelling inogadzirisa dambudziko rinoshanda: vanhu havagone kuyera yega yega yemamirioni ezvibodzwa zvinogadzirwa nemuenzaniso panguva yekudzidziswa. Pane kudaro, vanyoreri vanofananidza seti diki yemhinduro, kazhinji vachitora kuti ndeipi yemhinduro mbiri kune imwecheteyo kukurumidza iri nani. Modhi yemubairo inozodzidziswa pakuenzanisa uku kuti ibudise imwe scalar mamakisi kune chero yekukurumidza-mhinduro peya. Iyo yakajairwa chinangwa chekudzidzisa ndeyeBradley-Terry modhi, inoshandura zvido zvevaviri kuita mukana wekuti imwe mhinduro inokunda imwe. Kana yangodzidziswa, iyi modhi yemubairo inogona kuyera zvakachipa zvisingaperi zvitsva, ichipa chiratidzo chekuti algorithms sePPO inoshandisa kuvandudza modhi yemutauro. Mamodheru emubairo anoshandiswa zvakare panguva yekufungidzira kune yakanakisa-ye-N sampling, uko vazhinji vanokwikwidza vanogadzirwa uye yepamusoro-yepamusoro-zvibodzwa inodzoswa.
Technical Insight
Muenzaniso wemubairo unowanzo kuve mugadziriso wemutauro wekutanga nemusoro wechiratidzo-yekufanotaura unotsiviwa nemutsara mutsetse mumwe chete unoburitsa scalar imwe. Kudzidzira kunowedzera irogi-mukana wekuti mhinduro yakasarudzwa inokwira kupfuura iyo yakarambwa: kurasikirwa = -log(sigmoid(r_chosen - r_rejected)). Musiyano wehukama chete ndiwo une basa, saka chiyero chakakwana chinopokana. Hunhu hunotsamira pane label kuenderana uye kufukidzwa kwakafara kwemaitiro ekupindura.
Mastering Reward Modelling
Muenzaniso wemubairo ndeye neural network yakadzidziswa kufanotaura kuti yakanaka sei mhinduro yeAI, ichiita seyakazvimiririra yekumira-mukati yekutonga kwevanhu. Ndiyo injini yezvibodzwa inoita kuti kudzidza kwekusimbisa kubva kumhinduro dzevanhu kugoneke pamwero. Reward Modelling chikamu chemutauro-AI stack inoshandiswa kuverenga, kugadzira, kuronga, uye kushandura zvinyorwa uye kutaura pamwero. Kuti uvake kunzwisisa kwakadzama, bata Reward Modelling semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodiwa, kujekesa fungidziro, uye patsanura izvo zvinogona kuitwa nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Reward Modelling dhizaini zvinokurudzira, kudzoreredza, uye kuongorora zvishwe seimwe yakabatanidzwa yekutaurirana system. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Mutauro workflows inogona kufamba nekukurumidza pasina kupira kuenderana. Panguva imwecheteyo, chokwadi cheHallucified chinogona kupinda chinyararire mishumo, kuyerera kwetsigiro, kana kutsvagisa zvinobuda. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Mutauro workflows inogona kufamba nekukurumidza pasina kupira kuenderana.
Mutauro workflows inogona kufamba nekukurumidza pasina kupira kuenderana. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Inopamhidzira kupinda mumitauro yese nemataera ekutaurirana.
Inopamhidzira kupinda mumitauro yese nemataera ekutaurirana. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Zvikwata zvinogona kupedza nguva yakawanda pakutonga uku otomatiki ichibata kudzokorora.
Zvikwata zvinogona kupedza nguva yakawanda pakutonga uku otomatiki ichibata kudzokorora. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
Kusimbisa RLHF yevabatsiri vakaita se ChatGPT uye Claude nekumaka mhinduro dzevamiriri panguva yePPO yekudzidziswa
Yakanakisa-ye-N sampling, uko modhi inogadzira mhinduro dzakawanda uye modhi yemubairo inosarudza yakanakira mushandisi
Math uye coding 'verifiers' kana process mibairo modhi inotora matanho epakati ekufunga kugadzirisa kugadzirisa matambudziko.
Kuisa chinzvimbo uye kusefa yekugadzira yekudzidzisa data, ichichengeta chete epamusoro-zvibodzwa zvizvarwa zvekuwedzera-tuning
Maitiro Ekuita
Reward Modelling mukuita
Kupa simba RLHF yevabatsiri vakaita se ChatGPT uye Claude nekumaka mhinduro dzevamiriri panguva yePPO yekudzidziswa.
Kupa simba RLHF yevabatsiri vakaita se ChatGPT uye Claude nekupa mhinduro dzevamiriri panguva yePPO yekudzidzisa Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura hunhu hwemberi, chengetedza nzira yekukwira kwevanhu yekesi dzemupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Reward Modelling mukuita
Yakanakisa-ye-N sampling, uko modhi inogadzira mhinduro dzakawanda uye modhi yemubairo inosarudza yakanakira mushandisi.
Yakanakisa-ye-N sampling, uko modhi inogadzira mhinduro dzakawanda uye modhi yemubairo inosarudza yakanakira mushandisi Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Reward Modelling mukuita
Math uye coding 'verifiers' kana process mibairo modhi inotora matanho epakati ekufunga kugadzirisa kugadzirisa matambudziko.
Math uye coding 'verifiers' kana maitiro emubairo mamodheru anotora matanho epakati ekugadzirisa matambudziko Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Reward Modelling mukuita
Kuisa chinzvimbo uye kusefa yekugadzira yekudzidzisa data, ichichengeta chete epamusoro-zvibodzwa zvizvarwa zvekuwedzera-tuning.
Kuisa zvinzvimbo uye kusefa data rekudzidzira rekugadzira, kuchengetedza chete zvizvarwa zvine zvibodzwa zvekuwedzera-tuning Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
Chokwadi chehuroyi chinogona kupinda chinyararire mishumo, kuyerera kwetsigiro, kana tsvakiridzo.
Kunzwa nekukasira kunogona kugadzira mhedzisiro isingaenderane pane zvikumbiro zvakafanana.
Sensitive text data inogona kuburitswa kana zvidhiraivho zvisina kusimba.
Implementation Roadmap
Tsanangura chimiro chekubuda, toni, uye mhando zviyero usati waburitsa.
Tsanangura chimiro chekubuda, toni, uye mhando zviyero usati waburitsa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Mhinduro dzepasi neakavimbika masosi pese pazvine basa.
Mhinduro dzepasi neakavimbika masosi pese pazvine basa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Chengetedza ongororo yekuongorora yemunhu kune yakakwira-stake zvinobuda.
Chengetedza ongororo yekuongorora yemunhu kune yakakwira-stake zvinobuda. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Tevera maitiro ekutadza uye dzidzisazve kukurudzira kana mafambiro ebasa nguva nenguva.
Tevera maitiro ekutadza uye dzidzisazve kukurudzira kana mafambiro ebasa nguva nenguva. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.