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Kuramba 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.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It can be simpler than policy-gradient optimization, but quality depends on the candidate pool and scorer.
Kudzika Kwakadzika
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.
Kuitwa muNyika yeChokwadi
Kuenzanisa Llama-maitiro emhando nekuenzanisa mhinduro dzakawanda pakukurumidza, kuchengeta iwo epamusoro-mubairo-modhi yezvibodzwa, ipapo SFT pane izvo.
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Kuvaka madhaseti ekuraira ekugadzira nekusefa ega mhinduro dzakanakisa dzega dzega redzidzo rinotevera.
Ramangwana Rekurambwa Sampling Fine-Tuning
RFT iri pakati pemazuva ano post-kudzidziswa, inowanzo shandiswa pamberi kana padivi peRL nzira sePPO neDPO. Kukwezva kwayo kunokura nekuchipa kwekufungidzira uye yakasimba otomatiki verifiers: sezvo modhi dzinova nani pakuzvigadzira wega uye kuzviongorora, iterated kuramba sampling inotsigira synthetic-data uye kuzvinatsiridza zvishwe. Tarisira kubatanidzwa kwakasimba nemamodeli ekufunga anoburitsa cheni dzepfungwa dzinogoneka, uye kudzidza kunoenderera kwenzira yekudzivirira kubira mubairo nekupunzika kwakasiyana-siyana paunenge uchidzidzira kakawanda pane zvakabuda zvemuenzaniso wega.
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Take the Rejection Sampling Fine-Tuning quiz
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Mibvunzo inowanzo bvunzwa
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.
Which loop best describes how Rejection Sampling Fine-Tuning improves a model?
RFT sampuli mhinduro dzakawanda, mafirita kune iwo epamusoro-zvibodzwa, uye inonatsa-tuna modhi kune avo vakundi.
Munzvimbo dzinotariswa semasvomhu kana kodhi, chii chinogona kushanda semubairo?
Kune mabasa anotariswa, RFT inogona kushandisa kunyatsorongeka kana kupasa bvunzo dzeyuniti, kudzivirira yakadzidza mubairo modhi.
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.
Kudzidzira pane yakanyanya-mubayiro samples zvinobudirira kunoita chii?
Nekudzidza kubva kumhinduro dzakapepetwa dzepamusoro, modhi inoisa mukati memhando yepamusoro hunhu muchizvarwa chimwe.
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