GPT-6 Astra Scores 517.4 in Breakout by Writing Network Weights
Photo: TokenPost
MIT researchers used GPT-6 Astra to fill in a neural network’s weights for MinAtar’s simplified Breakout, producing a score of 517.4. That was about 20 times the score of a model with the same architecture trained for 500 million Proximal Policy Optimization (PPO) steps. For the Breakout test, Astra received the game’s rules and source code, plus a network with all its weights set to zero. It had no training data and could not play the game. Astra computed where the ball would land and encoded that prediction and paddle-control logic in the network’s weights.
Source: TokenPost
