| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3-0.6B-Base |
| tags: |
| - vizdoom |
| - reinforcement-learning-adjacent |
| - decision-model |
| - behavior-cloning |
| pipeline_tag: text-generation |
| --- |
| |
| # JevFlash: Doom Basic Decision Model (0.6B) |
|
|
| A from-scratch replica of [NanoJev](https://github.com/TianyuCodings/NanoJev)'s |
| non-generative "decision model" architecture — instead of generating text, |
| it scores a fixed set of candidate actions (`left` / `right` / `shoot` / |
| `noop`) given a game state, in a single forward pass — fine-tuned on |
| Qwen3-0.6B-Base to play ViZDoom's **basic** scenario (aim and shoot a |
| stationary monster). |
|
|
| **Closed-loop result: 65% episode success rate**, beating a uniform-random |
| baseline (45%) — the first of 10 training attempts to do so. Full, |
| honestly-documented history of everything that didn't work along the way |
| (degenerate collapses, a checkpoint that looked great on paper but turned |
| out to be a memorized shortcut, and the eventual discovery that this |
| specific failure mode was a bad random seed) is in the project repo: |
|
|
| **Code, data, and full write-up**: https://github.com/themaker00001/JevFlash |
|
|
| ## What this model does |
|
|
| Given a text description of the game state (health, ammo, position, and |
| visible target bounding box) and a question ("pick the best action"), it |
| outputs a probability score for each of up to 4 candidate actions in one |
| non-autoregressive forward pass. No chain-of-thought, no token generation |
| at inference time. |
|
|
| ## Training data |
|
|
| ~2,300 decision examples collected via epsilon-greedy exploration (10% of |
| executed actions were random, always labeled with the correct/heuristic |
| action) against a simple proportional aim-and-shoot heuristic — the same |
| technique the original NanoJev project used with a real pretrained RL |
| policy, adapted here to a much simpler, self-authored expert. |
|
|
| ## Files |
|
|
| - `best.safetensors` — model weights (backbone + decision head) |
| - `tokenizer/`, `backbone_config/` — tokenizer and Qwen3 backbone config |
| - `summary.json`, `train_log.json`, `timing.json`, `target_audit.json` — training run artifacts |
|
|
| ## License |
|
|
| Apache-2.0 (inherited from the Qwen3-0.6B-Base backbone). |
|
|
