Datasetpublic

CL-From-Nothing/Sokoban-Trajectories

Sokoban-Trajectories Rollout trajectories for the Sokoban (推箱子) environment generated using the RAGEN framework. Models Role Model Teacher Qwen/Qwen2.5-14B-Instruct Student Qwen/Qwen2.5-3B-Instruct Environment Settings Setting Value Room size 6×6 Number of boxes 1 Max turns per episode 10 Actions per turn up to 2 (max_actions_per_turn=2) Max actions per trajectory 20 (10 turns × 2 actions) Note: 1 turn = up… See the full description on the dataset page: https://huggingface.co/datasets/CL-From-Nothing/Sokoban-Trajectories.

sourceHugging Facemitupdated 6mo agoView on Hugging Face

0likes27downloads

Dataset Card

Sokoban-Trajectories

Rollout trajectories for the Sokoban (推箱子) environment generated using the RAGEN framework.

Models

Role	Model
Teacher	Qwen/Qwen2.5-14B-Instruct
Student	Qwen/Qwen2.5-3B-Instruct

Environment Settings

Setting	Value
Room size	6×6
Number of boxes	1
Max turns per episode	10
Actions per turn	up to 2 (`max_actions_per_turn=2`)
Max actions per trajectory	20 (10 turns × 2 actions)

Note: 1 turn = up to 2 actions. The LLM is called once per turn and may output 1 or 2 actions. All turn-based statistics below count LLM calls, not individual actions.

Data Scale

—500 problems × 4 trajectories each = 2000 trajectories per split
—Seeds: val base seed 123 (problems 123–622)

Cutoff

Teacher average turns = 4.21 → cutoff = floor(4.21 / 2) = 2 turns (≤4 actions)

Splits

Split	Description
`teacher`	Full rollouts by teacher (14B) from start to finish
`student`	Full rollouts by student (3B) from start to finish
`student_prefix`	Step 4: Student runs first 2 turns, teacher completes the rest
`teacher_prefix`	Step 5: Teacher runs first 2 turns, student completes the rest

Results

Setting	success	pass@4	avg turns
Student only	0.066	0.160	9.31
Step 4: student→teacher	0.487	0.682	7.47
Step 5: teacher→student	0.223	0.402	8.30
Teacher only	0.655	0.844	4.21

Key finding: teacher completion after student prefix (Step 4) substantially improves over student-only, but the student struggles to finish after a teacher prefix (Step 5), indicating the student's ability to complete partially-solved puzzles is the bottleneck.

Schema

Each row is one trajectory:

Column	Type	Description
`env_id`	int	Unique environment index (0–1999)
`group_id`	int	Problem group index (0–499); 4 trajectories share the same problem
`turn_count`	int	Number of LLM turns taken (1 turn = up to 2 actions)
`messages`	list[dict]	Full conversation: `[{role, content}, ...]`

Usage

python

from datasets import load_dataset

ds = load_dataset("CL-From-Nothing/Sokoban-Trajectories")

# Full teacher rollouts
teacher = ds["teacher"]

# Step 4: student prefix (2 turns) + teacher completion
step4 = ds["student_prefix"]

# Access messages for first trajectory
print(step4[0]["messages"])