CommunityJev Guides & Articles
Teaching a decision model to cook
Rodney L. trains OpenJev to play cooperative Overcooked. Two copies of one policy serve six soups in 512 ticks, and every action is a single native button.
- Format
- Article
- Published by
- Community
- Author
- Rodney L.
- Last verified
- Sep 2026
Works forJev for Realtime control
Used inJev for Gaming
Highlights
- 26 September 2026. Two chefs share weights and choose independently. Checkpoint 220 serves six soups.
- Each decision is one of six buttons: stay, up, down, left, right, or interact. There is no planner or pathfinder.
- A 43-value observation becomes text. OpenJev scores whether "your next action should be" that button.
- He contrasts this with Jev demos that hand pathfinding or tactical choices to the harness.
Watch out
The policy is OpenJev, not the TypeSafe API. Trained on one RTX 5080. The soup count is the author's greedy evaluation.
Questions about this entry
Short answers drawn from the linked project or page.
No controller assigns jobs. Both chefs share weights and choose independently from their own observation, which includes the teammate's position and recent actions. In the recorded rollout they swap roles — one plates at tick 49, the other at tick 353 — without exchanging any messages.
Little, by design. Every decision is one of six native buttons, and there is no planner, pathfinder, action graph, or macro. He contrasts this with Jev demos that hand pathfinding or tactical choices to the harness; here navigation, cooking, and delivery all have to be learned through those buttons.
One RTX 5080, and roughly 52 hours along the successful training lineage. About 1.25% of parameters were updated with rank-16 LoRA on a Qwen3.5 backbone, and rollouts ran at 61.5 agent actions per second with 7.45 GiB peak VRAM. Backpropagation accounted for most of the wall clock.
He reports one directly: the first attempt ran about ten hours without serving a single soup. He restarted from pretrained weights, trained one chef to four soups, used that adapter to initialize both chefs, and reached the six-soup checkpoint after another 32.6 hours. Six soups is one greedy evaluation, with no seed sweep.
OpenJev applies the idea behind Jev: describe a situation, supply possible answers, and receive probabilities. It uses natural-language inference to score whether each proposed answer follows from the context, which lets the same classifier choose among different action spaces.
The wrapper turns each chef's public 43-value observation into plain text: position, inventory, nearby objects, the pot and the teammate, plus game rules and the last eight action outcomes, with no subgoals chosen. It then scores six native buttons and normalizes the entailment probabilities into a policy.
GRPO adapted to game trajectories with discounted reward-to-go, a group baseline and PPO clipping. Adaptive entropy, a small observation-novelty bonus and a repeated no-op penalty drove exploration, starting from a pretrained OpenJev v5 checkpoint on a Qwen3.5 backbone with rank-16 LoRA updating about 1.25% of parameters.
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