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@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+games.jsonl filter=lfs diff=lfs merge=lfs -text
+train.jsonl filter=lfs diff=lfs merge=lfs -text
+val.jsonl filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

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+The MIT License (MIT)
+
+Copyright (c) 2014 Gabriele Cirulli
+Copyright (c) 2026 Yeth (yeth.dev)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+1. The above copyright notice and this permission notice shall be included in
+   all copies or substantial portions of the Software.
+
+2. Credit must be given to Yeth as the original
+   author of the AI model, training code, and dataset in any derivative work,
+   publication, or distribution that uses them.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

README.md

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+---
+license: mit
+task_categories:
+  - reinforcement-learning
+tags:
+  - "2048"
+  - game-ai
+  - supervised-learning
+  - imitation-learning
+size_categories:
+  - 10K<n<100K
+---
+
+# 2048 Expert Gameplay Dataset
+
+State-action pairs from an expert N-Tuple Network agent playing 2048.
+Can be used for imitation learning / supervised training of 2048 agents.
+
+## Stats
+
+- Source games: 10,000
+- Games after filtering: 9,000
+- Total moves: 54,010,983
+- Average score: 143,847
+- Win rate (>= 2048): 100% (losing games removed)
+- Score floor: 62,152 (bottom 10% removed)
+
+## Files
+
+- `train.jsonl` - 8,100 games (48,592,790 moves) for training
+- `val.jsonl` - 900 games (5,418,193 moves) for validation
+- `games.jsonl` - original unfiltered dataset (10,000 games)
+
+## Filtering
+
+The raw dataset was filtered to improve supervised learning quality:
+1. Removed games that did not reach the 2048 tile (losing games with desperate end-moves)
+2. Removed bottom 10% by score (bad-luck games with messy board patterns)
+3. Split 90/10 into train/validation (every 10th kept game to validation)
+
+## Format
+
+JSONL format, one game per line. Each game object:
+
+```json
+{
+  "game_id": 0,
+  "score": 142056,
+  "max_tile": 8192,
+  "num_moves": 2341,
+  "moves": [
+    {
+      "board": [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],
+      "action": 0,
+      "action_name": "up",
+      "reward": 0,
+      "move_number": 0
+    }
+  ]
+}
+```
+
+- `board`: 16 tile values in row-major order (0=empty, 2/4/8/...)
+- `action`: 0=up, 1=right, 2=down, 3=left
+- `reward`: points from merges on this move

games.jsonl

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+oid sha256:526ddf3239bd747a591f9e66d26d1acc68435751bf9434efdb9622ac35fe3028
+size 6562770344

train.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:008274131d4e0a786504070ca06543c128e75b207eac4b9971fb802f9d3477c6
+size 5712671092

val.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d822a524da71be6d4e35678f3e642df68d86466db26c0877e1645d2edb49caf9
+size 637045167