Ci bi mu melGis bi ci topp
Optimisation ci politik yu jege
IA làkk
GUIDE teknik
Groupe bi gëna xéewale ci wàllu politik
Groupe Relative Policy Optimization (GRPO) xeetu jàng-jàng buy dooleel la ngir gëna suqali xeetu làkk yi, di àtte tontu bu nekk ci tontub mbokk yu bari ci benn laaj, di dindi reso valeur bu tàqaloo bi PPO di jëfandikoo.
Déglul tegtal biiIA-nettali · ~2 min
Ci xët wii2 simili jàng
Résumé
Mu siiw ni xeetu tàggat yaram bu mag bi ci ginaaw xeetu xalaat yu DeepSeek.
Plongeur bu xóot
GRPO is a variant of policy-gradient reinforcement learning designed to make RL fine-tuning of large language models cheaper and more stable. Many PPO implementations train a value model to estimate returns; its size and cost depend on the implementation. GRPO removes that critic entirely. For each prompt it samples a group of completions (say 8-64), scores them all with a reward signal, and then computes each completion's advantage by standardizing its reward against the group's mean and standard deviation. Above-average answers are reinforced and below-average ones suppressed. A KL-divergence term keeps the model close to a reference policy. Introduced by DeepSeek, it powered DeepSeekMath and the DeepSeek-R1 reasoning models.
Doxal ci àdduna dëgg
Taggat DeepSeek-R1 ak DeepSeekMath ngir génne xalaat yu guddu ci jëfandikoo sàrt yu lalu ci njub ci neexal ci jafe-jafe math
Royuwaayi defar kode yu baax, fu bépp saafara buñ jël ñu jël poñ ci ndax jàll na test yuñ def, ba noppi ñu normalise kuréel gi ngir tànn ki jël raw-gàddu gi
Pipeline RLHF yu ubbeeku (lu melni, ci bibliotek TRL ak verl) di jëfandikoo GRPO ngir méngale xeetu waxtaan yi te doo fay beneen reso bu am valeur
Yokkatal topp tegtal yi wala jeffin ju wóor ci sampling tontu yu bari ci laaj bu nekk ak neexal ñooñu xeetu neexal bi gëna rëy ci seeni moroom
Ëlëgu Groupe Relatif Politik Optimisation
GRPO dafa gaaw nekk rëset buñ miin ngir tàggat xeetu xalaat yu ubbeeku yi, ba noppi laboratoire yi dañuy baamtu ci barab yu néew doole yi. Gëstukat yi ñu ngi jàngat saafara yi ngir guddaay ak jafe-jafe (lu melni Dr. GRPO), niveau token moo gën normalisation niveau sequence, ak dindi wala soppali terme KL. Xaarandi lëkkaloo bu gëna dëgër ak neexal yuñ mëna saytu (math, kode, jëfandikoo jumtukaay), gëna mëna jëfandikoo siñaal yu néew, ak ibrid yuy boole grupu baseline ak ŋàññikat yu woyof ngir liggéey yu bari, yu bari.
Teg ko ci jëf
Lu tax mu am solo
- 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.
Moytul
- 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.
Jéego yi ci topp
- 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
Bulletin bu amul fayda
Jàngal xibaar AI bu bes
Ñatti jaar-jaari IA yuñ firndeel bis bu nekk ci ayu-bis bi, ñu bind leen ci Àngle bu leer. Gratis ba fàww, amul yëgle.
Benn email bis bu nekk ci ayu-bis bi. Benn klik nga dindi abonemaa bi. Dunu musa jaay wala séddoo sa adres.
Nattal sa bopp
Take the Group Relative Policy Optimization quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
Jàppale njàngum IA te doo fay. AI Understanding mbootaay la buy def te Yàlla tax — amul yëgle, amul paywall, musul am. Defal ab sarax
Laaj yi ñuy faral di laaj
Luy njariñu politik relatif ci kuréel gi?
Groupe Relative Policy Optimization (GRPO) xeetu jàng-jàng buy dooleel la ngir gëna suqali xeetu làkk yi, di àtte tontu bu nekk ci tontub mbokk yu bari ci benn laaj, di dindi reso valeur bu tàqaloo bi PPO di jëfandikoo. Mu siiw ni xeetu tàggat yaram bu mag bi ci ginaaw xeetu xalaat yu DeepSeek.
Which separately learned estimator does the original GRPO approach avoid training?
GRPO uses a group-relative baseline rather than a separately learned value estimator. The size and resource savings depend on the PPO implementation used for comparison.
naka la GRPO di xaymaa njariñu benn matal buñ jox?
Njariñu bépp matt mooy (neexal - moyenne groupe) / groupe standard deviation, kon kuréel gi tontu ci benn laaj bi dafay nekk liiñ bu njëkk bi.
Ban xeetu xam-xam moo tax GRPO siiw?
DeepSeek moo dugal GRPO ba noppi siiwal ko, rawatina ci DeepSeekMath ak muy motëru RL bi ci ginaaw xeetu xalaat DeepSeek-R1.
Ban cër la terme KL-divergence def ci GRPO?
Penalty KL ci kaw benn royuwaay (dafay faral di nekk pre-RL) model dafay yamale tàggat yaram suko defee politik bi gëna mëna dem te baña daanu wala dem ci génnug degenerate.
Lan moo waral GRPO di gëna am solo ngir tàggat ay xeetu xalaat ci math wala kode?
Sampling pexe yu bari ak poñ leen ci njubte otomatik saytu ñaar ñu sell ak njariñu GRPO's group-normalised, soxlawul ŋàññi.
Weyal di jàng
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