What if everything you thought you knew about training large language models turned out to be… not quite right? 🤯
In this episode, we dive deep into a topic that could completely change the way we think about LLM training. We’re talking about batch size — yes, it sounds dry and technical, but new research shows that tiny batches, even as small as one, don’t just work — they can actually bring major advantages.
🔍 In this episode you’ll learn:
Why the dogma of “huge batches for stability” came about in the first place.
How LLM training is fundamentally different from classical optimization — and why “smaller” can actually beat “bigger.”
The secret setting researchers had overlooked for years: scaling Adam’s β2 with a constant “token half-life.”
Why plain old SGD is suddenly back in the game — and how it can make large-scale training more accessible.
Why gradient accumulation may actually hurt memory efficiency instead of helping, and what to do instead.
💡 Why it matters for you:
If you’re working with LLMs — whether it’s research, fine-tuning, or just making the most out of limited GPUs — this episode can save you weeks of trial and error, countless headaches, and lots of resources. Small batches are not a compromise; they’re a path to robustness, efficiency, and democratized access to cutting-edge AI.
❓Question for you: which other “sacred cows” of machine learning deserve a second look?
Share your thoughts — your insight might spark the next breakthrough.
👉 Subscribe now so you don’t miss future episodes. Next time, we’ll explore how different optimization strategies impact scaling and inference speed.
Key Takeaways:
Small batches (even size 1) can be stable and efficient.
The secret is scaling Adam’s β2 correctly using token half-life.
SGD and Adafactor with small batches unlock new memory and efficiency gains.
Gradient accumulation often backfires in this setup.
This shift makes LLM training more accessible beyond supercomputers.
SEO Tags:
Niche: #LLMtraining, #batchsize, #AdamOptimization, #SGD
Popular: #ArtificialIntelligence, #MachineLearning, #NeuralNetworks, #GPT, #DeepLearning
Long-tail: #SmallBatchLLMTraining, #EfficientLanguageModelTraining, #OptimizerScaling
Trending: #AIresearch, #GenerativeAI, #openAI
Read more: https://arxiv.org/abs/2507.07101
