---
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).