Re:Astra can read and write weights to improve Qwen
by ninglor on Monday November 02, 2026 @05:48PM
(#50345134)
> “breakout game weights” always look the same regardless of training and model
The prompt told Astral what the input and output layers mean -- that is, how the current state of the game is turned into a vector for matrix multiplication, and how the result of those multiplications is turned into an action to take in-game.
Astral saw that the model was trained on a game where the player is at the bottom of the grid and moves left and right, reward objects live at the top, and there's some not-player-controlled thing moving around in the middle. Astral knows that this type of game is called "breakout."
A model that plays breakout will have meaningful weights for the player being in the bottom row, but not for the player being anywhere else, because the player can't go anywhere else. Astral saw that there were meaningful weights for the player in the bottom row, and nodes representing the player in any other row had low-magnitude noise, so the model had never been trained on the player being anywhere besides the bottom row.
Similarly, breakout has "reward" objects in the top rows, and "there is a reward object at [COORDINATE]" is part of the input layer. Astral saw that the model was trained on reward objects in the top few rows.
The prompt told Astral what the input and output layers mean -- that is, how the current state of the game is turned into a vector for matrix multiplication, and how the result of those multiplications is turned into an action to take in-game.
Astral saw that the model was trained on a game where the player is at the bottom of the grid and moves left and right, reward objects live at the top, and there's some not-player-controlled thing moving around in the middle. Astral knows that this type of game is called "breakout."
A model that plays breakout will have meaningful weights for the player being in the bottom row, but not for the player being anywhere else, because the player can't go anywhere else. Astral saw that there were meaningful weights for the player in the bottom row, and nodes representing the player in any other row had low-magnitude noise, so the model had never been trained on the player being anywhere besides the bottom row.
Similarly, breakout has "reward" objects in the top rows, and "there is a reward object at [COORDINATE]" is part of the input layer. Astral saw that the model was trained on reward objects in the top few rows.