@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.
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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
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