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TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning research paper by Meta, 2025

Meta · Sep 30, 2025 · Foundation models · 17 citations · 56 upvotes · unverified

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What it shows

TruthRL, a reinforcement learning framework, enhances the truthfulness of large language models by balancing accuracy and abstention, significantly reducing hallucinations and improving performance across benchmarks.

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

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About this paper
Authors
Zhepei Wei, Xiao Yang, Kai Sun and 12 more
arXiv
2509.25760 · PDF
Venue
arXiv.org
Citations
17, 2 influential · Semantic Scholar
Upvotes
56 · Hugging Face
Lab
Meta · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 2Sep 25, 2026
Citationsfirst count: 17Sep 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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