Research results
From a decision to a finished task
A correct-looking move is not enough. The agent needs to reach the goal.
Generated task: ten 4 × 4 mazes, reused for exploration. Layouts and protocol · Maze generator.
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The small network reads walls, player and goal, then returns one of four moves. It does not write directions in text.
| Method | Goals reached |
|---|---|
| 256 training states | 0 / 10 |
| 256 states + forbid wall moves | 2 / 10 |
| 4,992 training states | 5 / 10 |
| 4,992 states + forbid wall moves | 7 / 10 |
| Shortest-path search | 10 / 10 |
More examples helped. Forbidding moves through walls helped further, reaching 7/10 goals. The remaining failures were legal moves repeated in loops.
This is a small, separate network, not a Qwen early-exit result. No Qwen layer-skipping or timing saving is claimed here.
Training and the original failed run
The network maps 48 grid values through two 128-unit hidden layers to four action scores. A search algorithm supplies training labels. More training states come from the same 32 training layouts; the ten evaluation layouts are different but previously inspected.
The optional legal-move rule sees walls and boundaries, not the correct path. Search remains the stronger baseline on these simple fully visible mazes.
The original Qwen policies both reached 0/10 goals. The existing browser replay shows those original failures, not the later 7/10 policy.
Replay the original Qwen failures · Later training results and trajectories · Follow-up episode records