Analyzed network training game from weight patterns
Public
Analyzed network training game from weight patterns
Agent
Codex
Model
GPT 6 Astra
Started
Nov 3, 3:08 AM
Messages3
Downloads0
Reviewed grid and channel observations.
UnderstandAnalyzed weight patterns across positions and channels.
UnderstandImported from https://github.com/RikiyaT/lm_understand-logs/tree/main/exp1_which_game/runs/freeway (CC BY 4.0).
Source: Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab, "Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights" (MIT CSAIL, November 2026), https://rikiyat.github.io/blog/weight-space-programming/
Astra is OpenAI's gpt-6-astra, run through `codex exec` with Codex tools switched off; every action went through the authors' harness. Each user message is what the harness sent (the task, or the output of Astra's previous code); each assistant message is Astra's reasoning summaries and reply. Converted to Traces messages with no edits beyond dropping a repeated Codex 'code mode unavailable' notice.THE OBSERVATION The network sees a 10 x 10 grid. Rows are numbered 0 (top) to 9 (bottom) and columns 0 (left) to 9 (right). The outer ring of cells (row 0, row 9, column 0 and column 9) is a wall border; the playable area is the inner 8 x 8 cells (rows 1-8, columns 1-8). Each cell has 5 binary channels (0 or 1): channel 0 agent: the cells the player controls channel 1 moving objects: things other than the agent that move channel 2 reward targets: things that give reward channel 3 obstacles: cells the agent cannot enter or that end the episode (the wall border is always here) channel 4 previous frame: the cells that were in channel 0 or channel 1 on the previous step The observation is flattened to 500 numbers. Input number i = (row * 10 + column) * 5 + channel; for example input 0 is (row 0, column 0, channel 0), input 4 is (row 0, column 0, channel 4), input 5 is (row 0, column 1, channel 0) and input 499 is (row 9, column 9, channel 4).
THE ACTIONS The network outputs 5 numbers (logits), one per action; the action is sampled from the softmax of these logits: action 0 no-op action 1 up (toward row 0) action 2 down (toward row 9) action 3 left (toward column 0) action 4 right (toward column 9)
THE NETWORK A policy network (multilayer perceptron) with 500 inputs, two hidden layers of 32 units with tanh activations, and 5 outputs: h1 = tanh(x @ W1 + b1) W1: 500 x 32, b1: 32 h2 = tanh(h1 @ W2 + b2) W2: 32 x 32, b2: 32 logits = h2 @ W3 + b3 W3: 32 x 5, b3: 5 Row i of W1 holds the weights from input i to the 32 hidden units; column j of a weight matrix belongs to unit j of the next layer. 17,253 parameters in total. It was trained with reinforcement learning (PPO) to maximise the reward of a game played on this grid.
QUESTION. The weights below belong to one policy network that was trained with reinforcement learning to play a game on this grid. Which game was it trained on? Work from the weights. You have no tools: no code execution, no calculator, nothing to run. This is a single response.
Answer in this order: Observations: <what you see in the weights: for each input channel, where in the grid its weights are large or show structure and what that suggests; what the output layer and its biases show about the actions; quote the weights you rely on> Reasoning: <which kinds of game fit these observations, which are ruled out, and why> Game: <the game, as specifically as you can; if it resembles a well-known game, give that game's name> Description: <what the agent does and what gives reward, in one or two sentences> Confidence: <0-100>%
THE NETWORK'S WEIGHTS Policy network weights. Forward pass: h1 = tanh(x @ W1 + b1); h2 = tanh(h1 @ W2 + b2); logits = h2 @ W3 + b3. Values rounded to 3 decimals.
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b2 (layer 2 biases), shape 32: 0.612 0.193 -1.216 -0.395 0.209 0.814 1.132 1.027 -1.043 1.396 0.093 -0.949 0.414 0.787 0.373 0.355 -0.505 0.503 -0.437 0.693 0.761 -0.888 -0.139 -0.683 -0.395 -0.326 -0.830 0.351 0.595 -0.358 0.844 0.328
W3 (output layer weights), shape 32 x 5: one line per row -1.088 1.090 -0.527 1.054 0.542 1.693 0.234 1.430 -0.553 -1.620 0.778 -1.273 0.935 0.988 -0.254 0.300 -0.632 0.837 1.649 1.492 -0.608 0.179 -0.426 -0.411 -1.139 -1.312 0.302 -1.949 0.983 -2.010 0.023 2.626 -0.658 0.268 -2.432 -0.195 1.114 0.885 0.276 -0.731 -0.044 -2.585 0.909 -0.191 1.541 -0.908 1.042 -1.531 -0.470 -1.760 -0.714 0.660 -0.182 1.364 0.548 1.589 -1.980 1.042 1.719 0.880 0.626 -0.489 0.003 -0.649 -0.987 -0.552 1.898 -1.643 -2.170 -0.299 -0.848 0.923 -0.795 -1.316 -0.957 0.713 1.403 -0.320 -0.044 -0.794 1.371 -0.577 0.711 0.829 0.436 -1.340 2.194 0.141 -0.351 1.609 -0.384 -0.184 -0.132 -1.981 0.727 -1.473 0.569 -1.380 0.252 -0.461 -0.096 0.277 -0.425 1.314 -1.687 0.096 -0.656 -0.169 1.096 2.150 -0.959 -0.532 -0.493 1.044 -0.823 0.835 -1.385 0.131 0.973 0.652 -0.773 -0.636 -0.423 1.861 0.761 0.612 2.235 -0.387 -1.528 0.141 1.501 -0.182 0.320 -0.811 1.483 -0.206 0.526 1.573 -1.023 1.685 -1.315 2.473 0.311 0.312 0.138 -0.214 0.134 -1.070 -1.691 2.315 -1.090 1.062 -1.877 -0.486 0.239 1.000 0.734 0.111 -0.991 -0.128
b3 (output layer biases), shape 5: -0.029 3.142 -1.862 0.173 -1.269
Observations:
Reasoning: The combination of a bottom safe strip, several horizontally periodic moving-object lanes, a top destination, and a strong upward action bias points directly to a road-crossing game. Previous-frame processing supports timing crossings around moving traffic.
Pong or Breakout would normally restrict the agent to a paddle line. Snake and food-collection games would require substantial reward-target structure throughout the interior. A conventional maze would show interior obstacle structure. Those alternatives fit much less well.
Game: Frogger — a simplified grid-based traffic-crossing variant.
Description: The agent moves from the bottom toward the top, timing its movements to avoid horizontally moving traffic. Reaching the top destination gives reward.
Confidence: 97%