NanoJev

NanoJev is an open-source 0.6B-parameter decision model created as a compact reproduction of Jev-style parallel decision making. It takes states and questions as input and returns complete probability distributions without decoding output tokens one by one. The project supports dynamic candidate sets so the available actions can change from one decision to the next. One shared checkpoint has been tested across ViZDoom aiming tasks, maze navigation, and Snake. The repository includes datasets, configurations, research results, demonstrations, and an end-to-end training pipeline. Its experiments compare NanoJev with Jev and untuned Qwen baselines under matched tasks. The project is primarily intended for studying low-latency decision models rather than conversational text generation.