@neural_avb: If you think about it, LLM training in 2026 is really a 3-step loop : - train it on some data - dogfood it/run categori…
Summary
The tweet outlines a 3-step loop for LLM training in 2026: train on data, run evals, and add synthetic data for underperforming tasks. It emphasizes the accessibility of legal distillation via open source models and cheap APIs, noting that training on reasoning traces alone can achieve high scores.
View Cached Full Text
Cached at: 06/09/26, 01:36 AM
If you think about it, LLM training in 2026 is really a 3-step loop :
- train it on some data
- dogfood it/run categorized evals
- add new synthetic data in whatever task type it’s underperforming
Open source LMs and their API pricing is so lenient that legal distillation dataset creation is very accessible to everyone rn.
You just need to perfect your decision making skills for Step 2 and Step 3. That’s half the game rn. Just training on millions of reasoning traces will straight-up get you reward scores upto 60% on many RLVR tasks. You’d already have 2024 model purely through distillation, even before you do RL - really think about that.
With a 300$ budget it can get you to a pretty good 7B param model in a month, and then you can share it with your girlfriend.
Start SFT maxxing today
Similar Articles
Developing open source LLM from ground up from pretrain - rlhf(PPO/GRPO)
A developer shares progress on training a 7B parameter open source LLM from scratch using a DeepSeek architecture optimized for low VRAM, with the goal of democratizing AI development and eventually surpassing large proprietary models.
RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training
Harvard researchers challenge the standard LLM training pipeline by showing RL can be effectively applied during pre-training rather than only after SFT, finding that data composition matters more than model scale, and proposing parallel averaging of RL and SFT objectives that outperforms sequential approaches while preserving general capabilities.
Learning to reason with LLMs
OpenAI publishes an article exploring reasoning techniques with LLMs through cipher-decoding examples, demonstrating step-by-step problem-solving approaches and pattern recognition in language models.
What happened to the issue of companies running out of training data for LLMs?
The article revisits the earlier concern that human-generated training data for LLMs would run out, questioning whether the issue has been resolved or remains a problem given the continued improvement of AI models.
Inference Engines for LLMs & Local AI Hardware (2026 Edition)
This article provides a comprehensive guide to LLM inference engines for local AI hardware in 2026, explaining how to choose based on hardware strategy, workload, and serving model, and covering engines like llama.cpp, MLX, ExLlamaV2/3, vLLM, SGLang, TensorRT-LLM, and NVIDIA Dynamo.