Ask what should change when the equation is manipulated, then let the visualization test that expectation.
Foundation Lab
Instruction Tuning
Unlocks instruction-following—base models don't understand "summarize"
#84InstructScaling & Alignment
key equation
\mathcal{L} = -\sum_t \log p(y_t | \text{instr}, y_{<t})Selected Foundation Object
Keep the equation fixed; move through the evidence.
Concept 84 of 100InstructScaling & Alignment / Phase 7: Alignment & RLHF
Current question
Base models predict text; instruct models follow commands
\mathcal{L} = -\sum_t \log p(y_t | \text{instr}, y_{<t})Use the runnable panel, the key equation, and canonical papers as separate forms of evidence for the same object.
The useful learning product is the reusable mechanism you can carry into another model, paper, or engineering tradeoff.
Use prerequisites, dependents, and semantic links to repair the next gap without leaving the object behind.
Why It Matters for Modern Models
- Unlocks instruction-following—base models don't understand "summarize"
- FLAN showed dramatic zero-shot improvements
- First step before RLHF: instruct → RM → PPO
What Tutorials Skip
What is still poorly explained in textbooks and papers:
- Base models predict text; instruct models follow commands
- Diversity matters: more task types = better generalization
- CoT in the mix teaches reasoning as an instruction
Interactive Visualization
Core Math (Optional Deep Dive)
If you want intuition first, start with the key equation and the visualization. Come back here for the full walkthrough.
Key Equation
Fine-tune on (instruction, response) pairs:
Multi-task format:
Instruction-tuned models generalize to new tasks (zero-shot).