Trends In AI Training Techniques For Limited Data

AI field notes: It is looking likely that we are in the middle of a huge shift in AI capability. Let's take a closer look at S1, the "$6 thinking model". Traditional AI models rely on massive datasets and compute-intensive fine-tuning. But a new model from Stanford, the University of Washington, the Allen Institute for AI, and Contextual AI (Seattle, represent!) shows that increasing compute at test time—without modifying the model’s parameters—can drive significant performance gains. The model uses "test-time scaling", a technique for improving the performance of language models by increasing computational effort during inference rather than just training. S1 introduces "budget forcing", which strategically controls the model’s reasoning duration during test time. If the model stops too soon, they append “Wait” to encourage deeper reasoning. If the model takes too long, they force it to provide an answer. This results in significant performance gains without additional model retraining. 🎁 Their model, s1-32B, trained with just 1,000 reasoning samples, achieves competitive results, surpassing OpenAI's o1-preview on challenging reasoning tasks. By comparison, other approaches rely on massive datasets—DeepSeek-r1, for example, was trained on 800K+ samples. 🏋️♀️ s1-32B only uses supervised fine-tuning (SFT) with simple next-token prediction, while o1 and R1 use RL-based methods requiring extensive fine-tuning. This simplicity makes the S1 approach much more accessible and replicable. 📊 By extending the model’s reasoning process through budget forcing, s1-32B improves from 50% → 57% accuracy on AIME24, demonstrating extrapolation beyond its normal limits. 💵 Oh, and the model was fine-tuned in only 26 minutes on 16 H100 GPUs, showcasing remarkable efficiency. That's about 6 bucks worth. 💰 That said, while S1 is efficient to train, inference remains compute-heavy—so operating costs are still a factor. These results challenge fundamental assumptions about AI model development and deployment in a profound way. I'm not prone to hyperbole, but we may be witnessing one of the most profound shifts in machine learning in years—where efficiency, capability, and competition are being rewritten in real time. I'm here for it.

Prediction: In the next phase of AI, some gains will come from *scaling up* dataset & LLM size- and many will now come from *scaling down*. Bigger dataset/model != better anymore. Scaling up: In some frontier areas like multimodal where we're likely far from data scale saturation (e.g. image, video, time series, motion/control, etc) we'll see continued zero-to-one step changes from scaling up data/LLM sizes. This will be especially powerful where there are existing synthetic data generators to leverage (e.g. video game, gameplay, logic engines). Scaling down: In areas like text/chat/etc, scaling to better, bigger jack-of-all-trades generalists will have diminishing returns, due to exhaustion of both resources (hitting limit of data on the internet) and patience (business/developers want reliable results on actual use cases, not better brainteaser memorizers...). Here, base LLMs will continue to commoditize, and the game will be about training/tuning on small, carefully curated use case/domain-specific datasets for high performance (accuracy & cost) on specific tasks. We've already seen amazing results from the latter Snorkel AI! Intuition: - If you are training a toddler to read/talk, volume of raw data matters. - If you are training a new grad employee- you want a carefully curated curriculum for the task they are actually supposed to learn to do. You don't care about how many internet brainteasers they've memorized... you want them to perform on a specific set of tasks with high accuracy and speed. This is a big shift in how we think about LLM "scaling"- and it's all about how you curate & develop the data!

This AI breakthrough could change everything about model training A new paper from the Writer research team introduces "Reflect, Retry, Reward" - and it's a game-changer for how we train language models. Why this matters: 🎯 The Problem: Traditional AI training requires massive datasets and often fails when models encounter new, challenging tasks they weren't specifically trained for. 💡 The Solution: Instead of training models on specific tasks, this approach teaches them to get better at self-reflection - essentially learning how to learn from their mistakes. The Results Are Stunning: ✅ 34.7% improvement in math equation writing ✅ 18.1% improvement in function calling ✅ Smaller 7B models outperforming 70B+ models after training ✅ No catastrophic forgetting of existing skills Why This Is Revolutionary: 1️⃣ Resource Efficiency: You don't need perfect training data or massive compute - just a way to verify if the answer is right or wrong 2️⃣ Generalization: Models learn transferable reflection skills, not just task-specific patterns 3️⃣ Real-World Ready: Works for any task where you can verify correctness (code execution, API calls, math problems) 4️⃣ David vs. Goliath: A fine-tuned 7B model can beat a vanilla 72B model - democratizing AI capabilities The implications are huge. Instead of needing specialized models for every task, we could have smaller, more adaptable models that improve themselves through experience. This feels like a fundamental shift from "training models to know answers" to "training models to think better." Link: https://lnkd.in/eThqfcw2

As artificial intelligence continues its meteoric rise, we often hear about breakthroughs and new capabilities. But what if the next big challenge isn’t just technical, but about something more fundamental — running out of data? A recent report highlights a looming bottleneck: by 2028, AI developers may exhaust the stock of public online text available for training large language models (LLMs). The rapid growth in model size and complexity is outpacing the slow expansion of usable Internet content, and tightening restrictions on data usage are only compounding the problem. What does this mean for the future of AI? Good piece in Nature outlining some of the key advances in the field. 1️⃣ Shift to Specialized Models: The era of “bigger is better” may give way to smaller, more focused models, tailored to specific tasks. 2️⃣ Synthetic Data: Companies like OpenAI are already leveraging AI-generated content to train AI — a fascinating, but potentially risky, feedback loop. 3️⃣ Exploring New Data Types: From sensory inputs to domain-specific datasets (like healthcare or environmental data), innovation in what counts as “data” is accelerating. 4️⃣ Rethinking Training Strategies: Re-reading existing data, enhancing reinforcement learning, and prioritizing efficiency over scale are paving the way for smarter models that think more deeply. This challenge isn’t just technical; it’s ethical, legal, and creative. Lawsuits from content creators highlight the delicate balance between innovation and intellectual property rights. Meanwhile, researchers are pushing the boundaries of what’s possible with less. Link to piece here: https://lnkd.in/gvRvxJZq