FrozenLake-Hard-Trajectories

Rollout trajectories for a hard variant of FrozenLake generated using the RAGEN framework.

Models

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

Environment Settings

Setting	Value
Grid size	8×8 (vs 4×4 in standard FrozenLake)
Frozen tile probability p	0.8 (~13 holes per map)
Movement success rate	0.8 (80% intended, 20% random slip)
Coordinate range	(0, 0) to (7, 7) (zero-indexed)
Max turns per episode	10
Actions per turn	up to 2 (max_actions_per_turn=2)

Note: 1 turn = up to 2 actions. All turn-based statistics count LLM calls.

Data Scale

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

Cutoff

Teacher average turns = 3.08 → cutoff = 2 turns (≤4 actions)

Note: floor(3.08 / 2) = 1, but cutoff was set to 2 to give a more meaningful prefix length given the harder environment.

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	0.068	0.150	4.42
Step 4: student→teacher	0.158	0.330	3.78
Step 5: teacher→student	0.167	0.316	3.37
Teacher only	0.268	0.466	3.08

The hard environment compresses the gap between all settings — even the teacher only achieves 26.8% success, leaving limited room for the continuation methods to recover. Both Step 4 and Step 5 roughly double the student baseline (6.8% → ~16%).

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

from datasets import load_dataset

ds = load_dataset("CL-From-Nothing/FrozenLake-Hard-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"])