Overview
Proximal Policy Optimization (PPO) ndiyo yekusimbisa kudzidza algorithm inonyanya kuenderana nemhando yemitauro yekumisikidza kubva mumhinduro dzevanhu. Inovandudza mutemo mukungwarira, nhanho diki kudzivirira kusagadzikana kunotambudza naive policy gradient nzira.
Proximal Policy Optimization chikamu chemutauro-AI stack inoshandiswa kuverenga, kugadzira, kuronga, uye kushandura zvinyorwa uye kutaura pamwero.
Deep Dive
PPO yakaunzwa ne OpenAI muna 2017 uye yakave nhanho yekuseri kweRLHF kumasisitimu akaita seInstructGPT uye ChatGPT. Dambudziko guru mupolicy-gradient RL nderekuti imwe chete yakawandisa yekuvandudza inogona kudonha kuita. PPO inogadzirisa izvi ne 'yakachekwa surrogate chinangwa': inoyera kuti yakawanda sei (kana kushoma) chiitiko chave kupesana negwara rekare, inowanza iyo reshiyo nemukana (zvanga zviri nani sei pane zvaitarisirwa), uye inocheka reshiyo kune diki renji senge 0.8 kusvika 1.2. Izvi zvinovhara kuti gwara rinogona kufamba kusvika papi pakuvandudza, kuchengetedza kudzidza kwakagadzikana uchiri kubvumira kuvandudzwa kwakasimba. Mumutauro-muenzanisiro RLHF, 'chiito' chiri kugadzira chiratidzo kana mhinduro, mubairo unobva pamuenzaniso wemubairo, uye KL-divergence chirango chinochengeta modhi yacho kuti isaswedere kure nemaitiro ayo epakutanga.
Technical Insight
PPO inokwidziridza chinangwa chakachekwa: min(ratio * mukana, clip(ratio, 1-eps, 1+eps) * mukana), uko reshiyo ndiyo nyowani-pamusoro-yekare chiitiko. Zvakanakira zvinowanzofungidzirwa neGeneralized Advantage Estimation uye kukosha kwakadzidzwa (critic) network. MuRLHF, mubairo wakakwana unosanganisa mubairo-modhiyo chibodzwa nechero-tokeni yeKL chirango chinopesana nereferensi mutemo, kuenzanisa mubairo wekuwana uchipokana nekugara padyo neiyo yekutanga modhi.
Mastering Proximal Policy Optimization
Proximal Policy Optimization (PPO) ndiyo yekusimbisa kudzidza algorithm inonyanya kuenderana nemhando yemitauro yekumisikidza kubva mumhinduro dzevanhu. Inovandudza mutemo mukungwarira, nhanho diki kudzivirira kusagadzikana kunotambudza naive policy gradient nzira. Proximal Policy Optimization chikamu chemutauro-AI stack inoshandiswa kuverenga, kugadzira, kuronga, uye kushandura zvinyorwa uye kutaura pamwero. Kuti uvake kunzwisisa kwakadzama, tora Proximal Policy Optimization semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodiwa, kujekesa fungidziro, uye patsanura izvo zvingaitwe nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Proximal Policy Optimization 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
Kunyatsogadzirisa InstructionGPT uye ChatGPT kutevera mirairo uye zvido zvevanhu kuburikidza neRLHF
Kudzidzira kutamba-kutamba uye marobhoti anodzora maajenti, PPO's yepakutanga domain pamberi pemhando dzemitauro
Kuderedza huturu kana kuvandudza kubatsira nekuwedzera mubairo-modhi reki pasi peKL constrict.
Kunatsiridza maturusi-kushandisa kana akawanda-nhanho mumiriri maitiro apo modhi inopihwa mubairo wekupedza mabasa nemazvo
Maitiro Ekuita
Proximal Policy Optimization mukuita
Fine-tuning InstructionGPT uye ChatGPT kutevera mirairo uye zvido zvevanhu kuburikidza neRLHF.
Fine-tuning InstructGPT uye ChatGPT kutevera mirairo uye zvido zvevanhu kuburikidza neRLHF Teams kazhinji vanowana mibairo iri nani kana vachinge vatsanangura hunhu hwepamberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Proximal Policy Optimization mukuita
Kudzidzira kutamba-kutamba uye marobhoti anodzora maajenti, PPO's yepakutanga domain pamberi pemhando dzemitauro.
Kudzidzira mutambo-kutamba uye marobhoti ekudzora maajenti, PPO's yepakutanga dura pamberi pemitauro modhi Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Proximal Policy Optimization mukuita
Kuderedza huturu kana kuvandudza kubatsira nekuwedzera mubairo-modhi reki pasi pekumanikidza kweKL.
Kuderedza huturu kana kuvandudza kubatsira nekuwedzera mubairo-modhi yezvibodzwa pasi peKL constraint Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Proximal Policy Optimization mukuita
Kunatsiridza maturusi-kushandisa kana akawanda-nhanho mumiriri maitiro apo modhi inopihwa mubairo wekupedza mabasa nemazvo.
Kunatsiridza maturusi-kushandisa kana akawanda-nhanho maitiro emumiriri apo modhi inopihwa mubairo wekupedza mabasa nemazvo Zvikwata zvinowanzowana mhedzisiro iri nani kana vachinge vatsanangura hunhu hwepamberi, 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.