---
license: cc-by-nc-4.0
task_categories: [reinforcement-learning]
tags: [world-model, vlm-gym, bagel, maze2d]
---
# maze2d_easy_native256_noncot_chunk_k1_20260707_perseg

Maze2d (native 256px, JPEG q95; navigation with stop-required success, easy→hard split) — **action-conditioned visual world-model** SFT data (non-CoT action-chunk baseline) for the
BAGEL-7B-MoT feedback-interval study.

- **Format:** gzipped JSONL shards under `training/`, 1 row = 1 packed episode. CoT rows: per-segment
  layout — `<think>` per-step imagined frame (MSE target) `</think>` + committed action chunk, with a
  loss-0 `"Action executed." + real frame` re-grounding turn between chunks. nonCoT rows: bare action
  chunks. Build argv + git rev in `metadata/`.
- Companion checkpoint: `ultrastar112/maze2d_easy_native256_noncot_chunk_k1_world_model_20260707_perseg`
- Full study docs: `0710_train.md` / `0711_mulnode_eval.md` / `0711_hf_release.md` in the study repo.
- License: CC-BY-NC-4.0 (research use).