少点错误 02月28日
OpenAI releases ChatGPT 4.5
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OpenAI发布了GPT-4.5的研究预览版,这是他们迄今为止最大、知识最渊博的模型。GPT-4.5建立在GPT-4o的基础上,进一步扩展了预训练规模,旨在比以往任何OAI模型在标准问题上更准确,并且幻觉率更低。该模型采用了新的监督技术,并结合了传统的监督微调(SFT)和人类反馈强化学习(RLHF)方法。早期测试表明,与GPT-4.5的互动感觉更自然,其更广泛的知识库、与用户意图的更强对齐以及改进的情感智能使其非常适合写作、编程和解决实际问题等任务。

🧠 GPT-4.5是OpenAI发布的最新研究预览模型,被定位为比以往模型更通用,且知识更渊博,是扩展非监督学习范式的重要一步。

🤝 该模型采用了新的对齐技术,能够利用较小模型的数据训练更大更强大的模型,从而提升了GPT-4.5的可操纵性、对细微差别的理解以及自然对话的能力。

🎨 GPT-4.5在情感理解和创造力方面表现出色,擅长在用户进行情感查询时提供建议、缓解挫折或倾听,并在创意写作和设计方面为用户提供帮助。

🛡️ 在安全性方面,GPT-4.5主要限制在拒绝提供医疗或法律建议,并且安全性评估与之前的模型相比没有显著变化。

Published on February 27, 2025 9:40 PM GMT

This is not o3; it is what they'd internally called Orion, a larger non-reasoning model.

They say this is their last fully non-reasoning model, but that research on both types will continue.

It's currently limited to Pro users, but the model hasn't yet shown up on the chooser, despite them announcing it. They say it will be shared with Plus users next week.

It claims to be more accurate at standard questions and with a lower hallucination rate than any previous OAI model (and presumably any others).

Here's the start of the system card:

OpenAI GPT-4.5 System Card

OpenAI
February 27, 2025

1 Introduction

We’re releasing a research preview of OpenAI GPT-4.5, our largest and most knowledgeable model yet. Building on GPT-4o, GPT-4.5 scales pre-training further and is designed to be more general-purpose than our powerful STEM-focused reasoning models. We trained it using new supervision techniques combined with traditional methods like supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), similar to those used for GPT-4o. We conducted extensive safety evaluations prior to deployment and did not find any significant increase in safety risk compared to existing models.

Early testing shows that interacting with GPT-4.5 feels more natural. Its broader knowledge base, stronger alignment with user intent, and improved emotional intelligence make it well-suited for tasks like writing, programming, and solving practical problems—with fewer hallucinations. We’re sharing GPT-4.5 as a research preview to better understand its strengths and limitations. We’re still exploring its capabilities and are eager to see how people use it in ways we might not have expected.

This system card outlines how we built and trained GPT-4.5, evaluated its capabilities, and strengthened safety, following OpenAI’s safety process and Preparedness Framework.

2 Model data and training

Pushing the frontier of unsupervised learning

We advance AI capabilities by scaling two paradigms: unsupervised learning and chain-of-thought reasoning. Scaling chain-of-thought reasoning teaches models to think before they respond, allowing them to tackle complex STEM or logic problems. In contrast, scaling unsupervised learning increases world model accuracy, decreases hallucination rates, and improves associative thinking. GPT-4.5 is our next step in scaling the unsupervised learning paradigm.

New alignment techniques lead to better human collaboration

As we scale our models, and they solve broader, more complex problems, it becomes increasingly important to teach them a greater understanding of human needs and intent. For GPT-4.5, we developed new, scalable alignment techniques that enable training larger and more powerful models with data derived from smaller models. These techniques allowed us to improve GPT-4.5’s steerability, understanding of nuance, and natural conversation.

Internal testers report GPT-4.5 is warm, intuitive, and natural. When tasked with emotionally charged queries, it knows when to offer advice, diffuse frustration, or simply listen to the user. GPT-4.5 also shows stronger aesthetic intuition and creativity. It excels at helping users with their creative writing and design.

GPT-4.5 was pre-trained and post-trained on diverse datasets, including a mix of publicly available data, proprietary data from data partnerships, and custom datasets developed in-house, which collectively contribute to the model’s robust conversational capabilities and world knowledge.

Safety is limited to refusals, notably including refusals for medical or legal advice. Have they deliberately restricted those abilities to avoid lawsuits or to limit public perceptions of expertise being overtaken rapidly by AI?

They report no real change from previous safety evaluations, which seems reasonable as far as it goes. We're not to the really scary models yet, although it will be interesting to see if this produces better tool-use and the type of recursive self-checking that's crucial for powering competent agents. 



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