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General-purpose AI models source answers from the web, a weak fit for chip design, where accuracy has to be 100%. Synopsys’s Thomas Andersen tells Brendan Burke and Matthew Kimball how pre- and post-training with expert knowledge closes the gap. $SNPS

23,136 görüntüleme • 8 gün önce •via X (Twitter)

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New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

Andrew Ng

126,309 görüntüleme • 1 yıl önce

Why General AI Fails Tax Professionals — and What Makes TaxGPT State-of-the-Art Tax LLM Every tax professional using general-purpose AI tools like ChatGPT or Claude for research is one hallucinated source away from costly damage to their clients and professional career. So we ran a head-to-head benchmark: TaxGPT vs. OpenAI, Anthropic, and Gemini. Our methodology: 1️⃣ Quantitative testing on CPA + EA tax-specific exam questions 2️⃣ Qualitative testing complex, real-world scenarios across trust & estate, direct and indirect taxes, payroll, multi-state nexus, advisory, and compliance Results (Accuracy): TaxGPT: 96% Gemini 2.5 Pro: 89% Claude Sonnet 4: 87% GPT-4o: 80% GPT5: 92% But here’s the real story 👇 The Source Quality Gap General LLMs are great test takers. But tax research isn’t a trivia game — it’s a liability-driven profession where every position must be defensible. When we examined the sources behind their answers, the gap became a canyon: 🔍 OpenAI included citations only 9% of the time. 🔍 91% of answers had no verifiable source trail. 🔍 When citations did appear, they averaged 3.78 sources — many from Wikipedia, CNBC, AP News, Time, NerdWallet, etc. TaxGPT averages 14 authoritative sources per answer, all from primary/secondary tax law — IRC, Treasury Regs, court cases, IRS rulings, and our proprietary tax knowledge library. More details in our official blog post in the following post.

Kash from TaxGPT.com

59,282 görüntüleme • 11 ay önce