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k3.1-which-game-breakout-trace

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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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k3.1 reasoning trace: exp1_which_game / Breakout

A single reasoning trace from k3.1 (the model as named in the source tweet), stored in the Session Trace Simple Format so it opens in the Hub's trace viewer.

The model is given the raw weights of a small PPO policy network (500 → 32 → 32 → 5 MLP) and asked, with no tools, which grid game the network was trained on. It reasons over the weights by hand and concludes Breakout (~97% confidence).

a quick test of exp1_which_game with k3.1, we definitely never trained on such tasks .... mind blowing

— @bigeagle_xd

Contents

trace.jsonl holds one session:

line content
1 session header (type: "session") with links to the sources below
2 user turn: the full task prompt, including all network weights
3 assistant turn: the model's thinking (reasoningContent, 10m28s) and its final answer (content)

The paste holds only the model's thinking. It opens with the model restating the task to itself ("I need respond to puzzle from weights…"), so it is not a user message. The final answer comes from the screenshot attached to the tweet.

Every weight quoted in the final answer matches the prompt, except the three values given for input 205 (5.482, -5.880, 6.095). These are in row 206 of W1 (r4,c1, moving-object channel), not row 205 (agent channel), so the "isolated agent exception" in the answer is an indexing slip by the model. The answer is kept exactly as the model wrote it.

Sources

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