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Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow research paper by NVIDIA, 2026

NVIDIA · Jan 20, 2026 · Training and scaling · 11 citations · 26 upvotes · unverified

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What it shows

Quantized reinforcement learning training using FP8 precision faces stability issues due to numerical mismatches between training and inference phases, but a unified FP8 framework achieves significant speedups with stable convergence.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Haocheng Xi, Charlie Ruan, Peiyuan Liao and 7 more
arXiv
2601.14243 · PDF
Venue
arXiv.org
Citations
11, 4 influential · Semantic Scholar
Upvotes
26 · Hugging Face
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 4Sep 25, 2026
Citationsfirst count: 11Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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