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Réjection de échantillonnage de rejection
Rejection-sampling fine-tuning generates candidate responses, filters them with a reward model or verifier, and uses selected examples for supervised training.
Déglul tegtal biiIA-nettali · ~2 min
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Résumé
It can be simpler than policy-gradient optimization, but quality depends on the candidate pool and scorer.
Plongeur bu xóot
A rejection-sampling fine-tuning loop prompts a policy to generate multiple candidates, scores them with a reward model or task-specific verifier, and retains examples meeting a selection rule. The model is then fine-tuned with supervised learning on selected responses. In verifiable tasks, tests or exact-answer checks can provide a reward; open-ended tasks often rely on learned preference scores. Llama 2-Chat’s paper describes rejection sampling as one stage of its alignment pipeline. RFT can transfer best-of-N behavior into a model, but cannot select answers absent from the candidate pool and may overfit or exploit an imperfect scorer. It is not equivalent to every RLHF recipe and does not guarantee higher quality or diversity.
Doxal ci àdduna dëgg
Liggéeyukaay xeetu Llama ci sampling tontu yu bari ci laaj bu nekk, tëye poñ yi gëna rëy ci xeetu neexal, ba noppi SFT ci ñooñu
Yokkateg ab solver math ci defar ay solution yu bari te bàyyi ci tontu bu jaar yoon, buñu mëna saytu
Defar kodu fu ñuy denc kàndidaa yi sudee jàll nañu examen yi, ñu jëfandikoo leen ni done tàggat
Tabax ay done yuñ defaree ay tegtal ci segg tontu yu gëna baax yu model bi defar boppam ngir tàggat yaram bi ci topp
Ëlëgu Rejection Sampling Fine-Tuning
RFT lu am solo la ci tàggat-yaram bu bees bi, ñu koy faral di jëfandikoo balaa wala ci wetu pexe RL yu melni PPO ak DPO. Appel bi dafay màgg ndax inference yu yomb ak verifier otomatik yu dëgër: ginaaw bi model yi gënee xarañ ci defar seen bopp ak saytu seen bopp, sampling rejection buy baamtu dafay jàppale done synthetik ak bouclage yuy gëna suqali seen bopp. Xaarandil lëkkaloo bu gëna dëgër ak xeetu xalaat yuy defar ay xalaat yuñ mëna saytu, ak jàngat buy wéy ci ni ñuy moytu piratage neexal ak daanu bu wuute suñuy tàggat lu bari ci génnug xeetu boppam.
Teg ko ci jëf
Lu tax mu am solo
- Gaawaay ak yaatuwaay. Liggéeyukaay yi ci làkk yi mën nañu gëna gaaw te duñu yàq deggoo gi.
- Dugg ak yegg. Dafay yaatal jëfandikoo gi ci làkk yi ak ci anam yi ñuy jokkoo.
- dogal yu gëna leer. Ekip yi mën nañu gëna yàgg ci àtte ci jamono ji otomatisation di liggéey ci baamtu.
Moytul
- Lépp lu jaarul yoon mën na dugg ci rapoor yi, jàppale ci liggéey bi, wala ci njariñu gëstu bi.
- Sensibilite bu gaaw mën na jur njariñ yu wuute ci laajte yu noonu mel.
- Done yu am solo mën nañu feeñ sudee seytu jëfandikoo gi néew doole.
Jéego yi ci topp
- Mandargal formaa génne gi, melokaan bi, ak standard kalite yi laata ngay dugal ko.
- Tontu yu am solo ak balluwaay yu wóor saa yu dëggu bi di am solo.
- Fexeel am barabu xool nit ñi ngir am njariñ yu am solo.
- Toppal anami gacce yi ak di faral di tàggataat ay laaj wala def-liggéey.
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Nattal sa bopp
Take the Rejection Sampling Fine-Tuning quiz
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What is Rejection Sampling Fine-Tuning?
Rejection-sampling fine-tuning generates candidate responses, filters them with a reward model or verifier, and uses selected examples for supervised training. It can be simpler than policy-gradient optimization, but quality depends on the candidate pool and scorer.
Ban bouclage moo gëna mëna fësal ni Rejection Sampling Fine-Tuning di gëna baaxal benn model?
RFT dafay jël misaali tontu yu bari, segg ci ñi gëna am poñ, ba noppi defar model bi ci ñi jël raw-gàddu gi.
Ci wàll yiñ mëna saytu lu melni math wala kode, lan moo mëna nekk neexal bi?
Ngir liggéey yiñ mëna saytu, RFT mën na jëfandikoo njub wala jàll ci test yu benn, moytu xeetu neexal buñ jàng.
Which model family does the cited paper describe using rejection sampling in its alignment pipeline?
The Llama 2 paper describes rejection sampling as one stage of the Llama 2-Chat alignment pipeline.
Why might a team choose rejection-sampling fine-tuning over a policy-gradient approach for some tasks?
RFT uses generated and filtered examples for supervised updates, though generation, scoring, and data quality still incur costs.
Taggat ci misaali neexal bu gëna mag, lan lay def?
Suñu jàngee ci tontu yiñ segg ci kaw, xeetu model bi dafay dugal ci boppam jeffin ju gëna baax ci benn jikko.
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