# How the fire sets are chosen

No LLM is involved in any of this.

| file | what it does |
|---|---|
| `choose_fire_set.py` | **the set logic** — which tokens go in each program's fire set |
| `build_corpus.py` | streams 300k OpenWebText documents, drops anything matching the test split |
| `count_fires.py` | one pass over 36,786,566 positions; per (feature, token) firing counts |
| `fit_programs.py` | joins counts to corpus occurrences, calls the selection, writes the programs |

## The set logic in three steps

1. **Count.** For each token: how often it occurs in the train corpus, and how
   often the feature fires on it. `' George'` occurs 3,249 times and fires
   3,249 times; `' Osama'` occurs 227 times and fires 214.
2. **Score.** Each token's own F1 as a one-token rule, where its misses are the
   times it appears without firing. This is why step 1 needs the occurrence
   count and not just the firing count — a token that fires often but appears
   far more often is a liability.
3. **Grow greedily.** Start empty; repeatedly add whichever remaining token
   most increases the F1 of the *whole set*; stop when nothing improves it.
   For L6 F8045 that runs `' George'` 0.877 → `'George'` 0.927 → `' Osama'`
   0.954 → `'ORGE'` 0.958 → stop.

Step 3 is not "take the tokens in firing order" — at each step every remaining
token is re-tested, and the winner is usually *not* the next most frequent one.
Across layer-6 features with ≥300 firings, greedy order differs from
firing-count order **98.7%** of the time: a token firing 531 times out of 2,227
occurrences loses to one firing 142 times out of 145.

All selection happens on train. The test split is touched once, to score the
finished program.