| --- |
| license: apache-2.0 |
| base_model: convaiinnovations/laya |
| tags: |
| - "2048" |
| - game |
| - imitation-learning |
| - dagger |
| - laya |
| language: |
| - en |
| --- |
| |
| # laya-2048 |
|
|
| [convaiinnovations/laya](https://huggingface.co/convaiinnovations/laya) fine-tuned to play the game **2048**. |
| It reads the board as text and picks one of four moves (up / down / left / right) in a single forward pass. |
|
|
| - Code, training scripts and the browser player: **https://github.com/ctfpanda17/laya-2048** |
| - Speedrun: reaches the 2048 tile in **~11 seconds** (~950 moves, ~12 ms per move on an RTX 4090 with CUDA graphs) |
|
|
| ## Results |
|
|
| 64 self-played games in a simulator (same random seeds for every row): |
|
|
| | Model | Avg. score | Reached 2048 | Reached 4096 | |
| |---|---|---|---| |
| | Original laya (zero-shot) | ~1,000 | 0/64 | 0/64 | |
| | After imitation learning | 15,000 | 17/64 | 1/64 | |
| | After 5 DAgger rounds (**this model**) | ~45,000–48,000 | 51–55/64 | 33/64 | |
|
|
| The expectimax teacher it learned from averages ~108,000 points, so there is still a gap. |
|
|
| ## How it was trained |
|
|
| 1. **Imitation learning**: an expectimax search played 200 games; laya was fine-tuned (cross-entropy on the |
| four option markers) to pick the same move on ~90k boards. |
| 2. **DAgger, 5 rounds**: laya plays 64 games by itself, a stronger expectimax teacher labels every board laya |
| visited, the data is aggregated (~590k boards in total) and laya is fine-tuned again. |
| Agreement with the teacher went from 61% to 85%. |
|
|
| Full ModernBERT-large backbone fine-tuned (421M parameters), AdamW, lr 1e-5–2e-5, bf16 autocast, RTX 4090. |
| Weights are stored in float16 (~840 MB); `laya.load` upcasts them to float32. |
|
|
| ## Usage |
|
|
| The board **must** be formatted exactly like this, and the question must be the same, or the model will not |
| recognise the task. |
|
|
| ```python |
| import laya |
| from huggingface_hub import snapshot_download |
| |
| # local_dir avoids the Hugging Face cache symlinks, which fail on Windows without developer mode |
| path = snapshot_download("linpanda/laya-2048", local_dir="laya-2048") |
| agent = laya.load(path, device="cuda") # or "cpu" |
| |
| QUESTIONS = { |
| "move": { |
| "type": "choice", |
| "instructions": ( |
| "You are playing 2048. Tiles with equal numbers merge when they slide into each other. " |
| "Keep the largest tile in a corner and keep empty cells. Which move is best?" |
| ), |
| "criteria": { |
| "up": "slide all tiles up", |
| "down": "slide all tiles down", |
| "left": "slide all tiles left", |
| "right": "slide all tiles right", |
| }, |
| } |
| } |
| |
| def laya_state(grid): |
| """grid: 4x4 list of ints, 0 = empty cell.""" |
| board = "\n".join(f"row{i + 1}: " + " ".join(str(v) if v else "." for v in row) |
| for i, row in enumerate(grid)) |
| return {"game": "2048 puzzle, 4x4 board, '.' is empty", "board": board} |
| |
| grid = [[1024, 512, 256, 64], |
| [32, 64, 128, 32], |
| [16, 32, 16, 8], |
| [4, 8, 2, 0]] |
| probs = agent.predict(laya_state(grid), QUESTIONS)["answers"]["move"]["probabilities"] |
| print(probs) # pick the most probable move that actually changes the board |
| ``` |
|
|
| The model sometimes ranks a move that cannot change the board first, so in practice choose the most probable |
| **legal** move. The GitHub repo has a browser player (`play_2048.py`) and the speedrun script (`speedrun.py`). |
|
|
| ## Limitations |
|
|
| - Trained only on the standard 4x4 board with the text format above. |
| - Plays clearly weaker than its expectimax teacher; it imitates rather than searches. |
| - Not eligible for speedrun.com's 2048 leaderboards, which are for human players only. |
|
|
| ## License |
|
|
| Apache-2.0, the same as the base model [convaiinnovations/laya](https://huggingface.co/convaiinnovations/laya). |
|
|
