RNGBench-Game-Trajectories

SFT (supervised fine-tuning) trajectory data accompanying RNGBench · Reconstructive Non-Markov Games — an evaluation framework that tests whether multimodal language models can reconstruct hidden state from memory and act on it in closed-loop environments (the "remember-to-act" setting, where the current observation alone is not enough and the model must recall relevant history before deciding).

• 📄 Paper: https://arxiv.org/abs/2606.19338
• 🌐 Project page: https://internlm.github.io/RNGBench/
• 💻 Code (GitHub): https://github.com/InternLM/RNGBench

Games

The data currently covers the two games in RNGBench:

• Matching Pairs (MP) — a static, categorical hidden-state game: a memory card-matching game where card identities must be recalled by location.
• 3D Maze — a dynamic, spatial hidden-state game: egocentric first-person views that must be assembled into a map to reach the goal.

For each game we release two kinds of multi-turn agent trajectories:

• Optimal — trajectories produced by a rule-based optimal policy.
• Roll — rollout trajectories collected from strong models, namely Kimi-K2.5 and Qwen3.5-397B.

Files

File	Game	Type	Modality
data/MP_Optimal.jsonl	Matching Pairs	Optimal	image + text
data/MP_Roll.jsonl	Matching Pairs	Roll	image + text
data/MP_Roll_text.jsonl	Matching Pairs	Roll	text only
data/3D_Maze_Optimal.jsonl	3D Maze	Optimal	image + text
data/3D_Maze_Roll.jsonl	3D Maze	Roll	image + text

Images for each image+text subset are shipped as a matching archive under images/ (MP_Optimal.zip, MP_Roll.zip, 3D_Maze_Optimal.zip, 3D_Maze_Roll.zip). MP_Roll_text is text-only and has no image archive.

Note: more games / trajectories may be added over time, so this card intentionally omits exact per-split counts.

Format

Each line of a data/*.jsonl file is one trajectory in LLaMA-Factory ShareGPT format:

{
  "conversations": [
    {"from": "human", "value": "Remaining pairs: 4. Score: 0.\nRound 1 start ...\n<image>\nChoose a face-down card ..."},
    {"from": "gpt",   "value": "Thought: ...\nAction: aA"}
  ],
  "images": ["MP_Optimal/2x4_seed_11_composite/round_001_start.jpg", "..."],
  "system": "You are playing a Memory Match card game. ..."
}

• conversations: alternating human / gpt turns. Each <image> placeholder in a turn's value corresponds, in order, to one entry of the top-level images list.
• images: image paths of the form <subset>/<relative_path>, matching the layout inside the corresponding images/<subset>.zip (each archive stores files under a top-level <subset>/ folder).
• system: the system prompt (game rules and output format).
• MP_Roll_text is text-only and has no images field.

Usage

1. Download the repo and unzip every image archive into a single directory:

   mkdir -p images_extracted
   for z in images/*.zip; do unzip -q "$z" -d images_extracted; done
   

   After extraction, an images entry like MP_Optimal/2x4_seed_11_composite/round_001_start.jpg resolves to images_extracted/MP_Optimal/2x4_seed_11_composite/round_001_start.jpg.

2. With LLaMA-Factory, register the subsets via the provided dataset_info.json (point --dataset_dir / media_dir at the directory holding data/ and the extracted images), then train on e.g. rngbench_mp_optimal, rngbench_3d_maze_optimal, rngbench_mp_roll, rngbench_mp_roll_text, rngbench_3d_maze_roll.

License

MIT, following the RNGBench repository.

Citation

@article{rngbench2026,
  title   = {Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games},
  author  = {Ding, Shengyuan and Wei, Xilin and Fang, Xinyu and Duan, Haodong and
             Lin, Dahua and Wang, Jiaqi and Zang, Yuhang},
  journal = {arXiv preprint arXiv:2606.19338},
  year    = {2026},
}