Work performed as a part of Neel Nanda's MATS 6.0 (Summer 2024) training program.

TLDR

This is an interim report on reverse-engineering Othello-GPT, an 8-layer transformer trained to take sequences of Othello moves and predict legal moves. We find evidence that Othello-GPT learns to compute the board state using many independent decision rules that are localized to small parts of the board. Though we cannot rule out that it also learns a single succinct algorithm in addition to these rules, our best guess is that Othello-GPT's learned algorithm is just a bag of independent heuristics.

Board state reconstruction

1. Direct attribution to linear probes indicate that the internal board representation is frequently up- and down-weighted during a forward pass.
2. Case study of a decision rule:
   1. MLP Neuron L1N421 represents the decision rule: If the move A4 was [...]

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Outline:

(00:19) TLDR

(02:18) Review of Othello-GPT

(04:02) Project goal

(04:33) Results on box #1: Board reconstruction

(04:39) A circuit for how the model computes if a cell is blank or not blank

(05:59) An example of a logical rule for how the model computes if a cell is “mine” or “yours”

(07:32) Intra-layer phenomenology

(08:49) Results on box #2: Valid move prediction

(08:55) Direct logit attribution (Logit Lens)

(10:28) Board Pattern Neurons

(13:57) Clock Neurons

(15:14) Suppression behavior

(16:22) Future Work

(18:11) Acknowledgements

The original text contained 10 footnotes which were omitted from this narration.

The original text contained 2 images which were described by AI.

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First published:

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Narrated by TYPE III AUDIO.