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README.md

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+---
+license: mit
+task_categories:
+- reinforcement-learning
+tags:
+- frozen-lake
+- trajectory
+- teacher-student
+- llm-agent
+configs:
+- config_name: default
+  data_files:
+  - split: teacher
+    path: data/teacher.parquet
+  - split: student
+    path: data/student.parquet
+  - split: student_prefix
+    path: data/student_prefix.parquet
+  - split: teacher_prefix
+    path: data/teacher_prefix.parquet
+---
+
+# FrozenLake-Hard-Trajectories
+
+Rollout trajectories for a **hard variant of FrozenLake** generated using the
+[RAGEN](https://github.com/RAGEN-AI/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
+
+```python
+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"])
+```