---
license: mit
task_categories: [feature-extraction]
tags: [interpretability, sparse-autoencoder, gpt2, program-synthesis]
---

# Programs for GPT-2 SAE features

A readable Python program for each of **301,090 SAE features** of GPT-2 small —
all 13 `gpt2-small-res-jb` checkpoints (`blocks.0-11.hook_resid_pre` plus
`blocks.11.hook_resid_post`), 24,576 features each.

Each program is an ordinary function that takes the tokens seen so far and
returns whether that feature fires:

```python
def prog_L06F08045(input_tokens):
    """3 token families, 4 surface forms (train F1 of the uncapped optimum: 0.958)."""
    fire = {
        ' George', 'George',   # george  (3,592 firings)
        ' Osama',              # osama   (214 firings)
        'ORGE',                # orge    (28 firings)
    }
    return ('george-family', 1) if input_tokens[-1] in fire else ('no match', 0)
```

Fitted on **289,658 OpenWebText sequences** (36,786,566 read positions), scored
on a structurally disjoint 10,000-sequence test split. No LLM was used to write
these — the token sets are chosen by greedy marginal F1.

## Layout

| path | what |
|---|---|
| `programs/layer{00..12}/` | the programs, **100 per file**, in thousand-blocks |
| `scores/layer{00..12}.csv` | per-feature test F1, precision, recall, token count |
| `synthesis_attempt_1/` | 387 LLM-written layer-6 programs — an earlier attempt, kept for comparison |
| `activation_value_program/` | one program that predicts the activation **value**, not just whether it fires |

## Results

Median test F1 by layer, and the single best program in each:

| | L0 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | L8 | L9 | L10 | L11 | L12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| median | .98 | .86 | .80 | .73 | .65 | .55 | .41 | .30 | .23 | .19 | .17 | .14 | .13 |
| best | 1.00 | 1.00 | 1.00 | 1.00 | .98 | .99 | .97 | .96 | .95 | .95 | .94 | .93 | .90 |

The median falls steeply with depth while the best barely moves. Depth doesn't
cap what a token-only rule can reach — it changes how many features are
reachable by one.

## Caveats

- `scores/` reports test F1; the docstring inside each program reports the
  *train* optimum. They are different numbers.
- Layers 0–3 have fewer than 24,576 programs because some features fire only at
  position 0 (attention-sink features) and are excluded from fitting.