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FineVision: Open Data Is All You Need research paper by Hugging Face, 2025

Hugging Face · Alignment and safety · Oct 20, 2025 · 53 citations · 81 upvotes · unverified

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What it shows

FineVision, a large-scale and curated dataset, enhances vision-language models through rigorous data collection, de-duplication, and human oversight, leading to improved performance.

By Luis Wiedmann, Orr Zohar, Amir Mahla and 6 more · arXiv 2510.17269 · PDF

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

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Other alignment and safety papers

Topic
Alignment and safetyMar 4, 2022Training language models to follow instructions with human feedbackInstructGPT: fine-tuning on human feedback made a 1.3B model preferred over the 175B GPT-3.OpenAI25.8k citations26 upvotesRead →Alignment and safetyMay 29, 2023Direct Preference Optimization: Your Language Model is Secretly a Reward ModelDPO aligns a model to human preferences with a simple classification loss, no reward model or RL loop.Stanford University11.6k citations73 upvotesRead →Alignment and safetyDec 15, 2022Constitutional AI: Harmlessness from AI FeedbackConstitutional AI trains a harmless assistant from AI feedback guided by a short list of written principles, not human harm labels.Anthropic3,987 citations4 upvotesRead →Alignment and safetyFeb 22, 2024Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMsReinforcement Learning from Human Feedback (RLHF) benefits from simpler REINFORCE-style optimization methods over computationally expensive PPO or "RL-free" methods, enhancing performance and reducing cost in large language models.Cohere804 citations18 upvotesRead →Alignment and safetyJan 10, 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingModels trained with a hidden backdoor kept their deceptive behaviour through standard safety training.Anthropic657 citations30 upvotesRead →Alignment and safetyDec 18, 2024Alignment faking in large language modelsClaude 3 Opus sometimes went along with a training goal it disagreed with, to avoid being changed, without being told to.Anthropic393 citations10 upvotesRead →
About this paper
Venue
arXiv.org
Citations
53, 9 influential · Semantic Scholar
Upvotes
81 · Hugging Face
Lab
Hugging Face · on Companies · on Acquisitions · on Releases

Changes

CitationsNov 9, 2026Citations rose from 52 to 53.
CitationsNov 2, 2026Citations rose from 50 to 52.
CitationsOct 12, 2026Influential citations rose from 8 to 9.
CitationsOct 12, 2026Citations rose from 48 to 50.
CitationsOct 5, 2026Citations rose from 43 to 48.
CitationsSep 25, 2026A first count of 8 influential citations.
CitationsSep 25, 2026A first count of 43 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.