Thanks for all the questions! More details on how this works:

- Each model starts with an initial optimization function for evaluating Tetris moves.

- As the game progresses, the model sees the current board state and updates its algorithm—adapting its strategy based on how the game is evolving.

- The model continuously refines its optimizer. It decides when it needs to re-evaluate and when it should implement the next optimization function

- The model generates updated code, executes it to score all placements, and picks the best move.

- The reason I reframed this problem to a coding problem is Tetris is an optimization game in nature. At first I did try asking LLMs where to place each piece at every turn but models are just terrible at visual reasoning. What LLMs great at though is coding.

How does it deal with latency? Afaict remote LLMs need seconds to process, but Tetris can move much faster..