pith:7LPQM4CB
RLHF Workflow: From Reward Modeling to Online RLHF
Online iterative RLHF using proxy preference models from open-source datasets reaches state-of-the-art results on LLM chatbot benchmarks.
arxiv:2405.07863 v3 · 2024-05-13 · cs.LG · cs.AI · cs.CL · stat.ML
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Claims
We have shown that supervised fine-tuning (SFT) and iterative RLHF can obtain state-of-the-art performance with fully open-source datasets.
The proxy preference model built from open-source datasets approximates real human feedback closely enough that online RLHF updates remain beneficial rather than harmful.
The paper supplies a complete open-source recipe for online iterative RLHF that uses proxy preference models and reaches competitive performance on AlpacaEval-2, Arena-Hard, and MT-Bench.
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| First computed | 2026-05-17T23:38:13.576059Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/7LPQM4CBKI6IJLFXS4SPBULVWL \
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Canonical record JSON
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