README.md

---
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- reasoning
- planning
- symbolic-reasoning
- algorithmic-reasoning
- benchmark
- length-generalization
size_categories:
- 100K<n<1M
configs:
- config_name: block_world
  data_files:
  - split: train
    path: "Block World/*_train_1_7.csv"
  - split: test
    path: "Block World/*_test_8_10.csv"
- config_name: checkers_jumping
  data_files:
  - split: train
    path: "Checkers Jumping/*_train_1_7.csv"
  - split: test
    path: "Checkers Jumping/*_test_8_10.csv"
- config_name: tower_of_hanoi
  data_files:
  - split: train
    path: "Tower of Hanoi/*_train_1_7.csv"
  - split: test
    path: "Tower of Hanoi/*_test_8_10.csv"
  - split: all_1_to_20
    path: "Tower of Hanoi/*_all_1_20.csv"
- config_name: river_crossing
  data_files:
  - split: train
    path: "River Crossing/*_train_1_7.csv"
  - split: test
    path: "River Crossing/*_test_8_10.csv"
pretty_name: RecurrReason
---



<div align="center">

# RecurrReason: Recurrent Reasoning on Symbolic Puzzles

**A difficulty-controlled benchmark for evaluating multi-step reasoning in language models**

[![Paper](https://img.shields.io/badge/Paper-ICLR%202026-blue)](https://openreview.net/forum?id=ErgAON9dOW)
[![GitHub](https://img.shields.io/badge/GitHub-Code-green)](https://github.com/gowravmannem/Recurrent-Reasoning-on-Puzzles)
[![License](https://img.shields.io/badge/License-CC%20BY%204.0-yellow.svg)](https://creativecommons.org/licenses/by/4.0/)
[![HuggingFace](https://img.shields.io/badge/🤗-Dataset-orange)](https://huggingface.co/datasets/gmannem/RecurrReason)

</div>

---

## 📋 Table of Contents

- [Overview](#overview)
- [Dataset Structure](#dataset-structure)
- [Puzzles](#puzzles)
- [Quick Start](#quick-start)
- [Citation](#citation)
- [License](#license)

---

## 🎯 Overview

RecurrReason is a benchmark of **four recurrent logic puzzles** with **optimal trajectories** and controlled difficulty scaling (N=1 to 10). It tests whether language models can:

- Find **optimal** (minimal-length) solutions
- Produce **valid** intermediate steps
- **Generalize** to harder out-of-distribution instances

| Metric | Value |
|--------|-------|
| **Total Puzzles** | 10,817 |
| **Total Moves** | 285,933 |
| **Puzzle Types** | 4 |
| **Difficulty Range** | N=1 to 10 |

Current reasoning benchmarks often test only **final answer correctness**. RecurrReason evaluates:

- **Move validity**: Are all intermediate steps legal?
- **Optimality**: Is the solution minimal-length?
- **Length generalization**: Does performance hold on longer sequences?

---

## 📊 Dataset Structure

### Data Splits

| Split | N Range | Purpose |
|-------|---------|---------|
| **Train** | N=1-7 | In-distribution training data |
| **Test (OOD)** | N=8-10 | Out-of-distribution evaluation |

**Note:** We provide train and test splits. Users can create their own validation split from the training data if needed.

### File Structure

```
RecurrReason/
├── Block World/
│   ├── bw_train_1_7.csv
│   └── bw_test_8_10.csv
├── Checkers Jumping/
│   ├── cj_train_1_7.csv
│   └── cj_test_8_10.csv
├── Tower of Hanoi/
│   ├── toh_train_1_7.csv
│   └── toh_test_8_10.csv
├── River Crossing/
│   ├── rc_train_1_7.csv
│   └── rc_test_8_10.csv
└── README.md (this file)
```

---

## 🧩 Puzzles

RecurrReason contains four diverse logic puzzles with different structural properties:

| Puzzle | Difficulty | Solution Length | Transition Locality | Puzzles | Moves |
|--------|-----------|-----------------|---------------------|---------|-------|
| **[Block World](BLOCK_WORLD.md)** | ⭐⭐ | O(N) | O(1) | 849 | 5,827 |
| **[Checkers Jumping](CHECKERS_JUMPING.md)** | ⭐⭐⭐ | (N+1)²−1 | O(N) | 5,700 | 242,494 |
| **[Tower of Hanoi](TOWER_OF_HANOI.md)** | ⭐⭐⭐⭐⭐ | 2^N−1 | O(N) | 60 | 12,216 |
| **[River Crossing](RIVER_CROSSING.md)** | ⭐⭐⭐⭐ | Variable | O(N) global | 4,208 | 25,396 |

Click on each puzzle name for detailed documentation including:
- Puzzle rules and constraints
- State representation format
- Example trajectories
- Column descriptions

### Quick Puzzle Descriptions

<details>
<summary><b>Block World</b> - Rearrange blocks in stacks</summary>

**Goal:** Move blocks from initial configuration to target configuration.

**Rules:**
- Only top block of a stack can be moved
- Can place on empty stack or on top of another block

**Why interesting:** O(1) transition locality makes it learnable and tests dependency reasoning.

[→ Full documentation](BLOCK_WORLD.md)
</details>

<details>
<summary><b>Checkers Jumping</b> - Swap red and blue checkers</summary>

**Goal:** Swap N red and N blue checkers on a 1D board with one empty space between them.

**Rules:**
- Red moves only right, blue only left (and vice versa based on starting configuration)
- Can slide to adjacent empty space or jump over opposite color

**Why interesting:** Quadratic solution length and tests avoiding dead-end configurations.

[→ Full documentation](CHECKERS_JUMPING.md)
</details>

<details>
<summary><b>Tower of Hanoi</b> - Transfer disks between pegs</summary>

**Goal:** Move N disks from source peg to target peg across 3 pegs.

**Rules:**
- Move one disk at a time
- Only topmost disk can be moved
- Larger disk cannot be on top of a smaller disk

**Why interesting:** Exponential solution length (2^N−1). It is a classic recursive problem.

[→ Full documentation](TOWER_OF_HANOI.md)
</details>

<details>
<summary><b>River Crossing</b> - Transport agents safely</summary>

**Goal:** Transport N actor-agent pairs across river using boat with capacity k.

**Rules:**
- Boat holds at most k individuals
- Actor aᵢ cannot be with agent Aⱼ (j≠i) unless agent Aᵢ is present

**Why interesting:** Global O(N) constraint verification and tests constraint satisfaction.

[→ Full documentation](RIVER_CROSSING.md)
</details>

---

## 🚀 Quick Start

### Installation

```bash
pip install datasets
```

### Loading the Dataset

```python
from datasets import load_dataset

# Load a specific puzzle
dataset = load_dataset("gmannem/RecurrReason", "block_world")

# Access splits
train_data = dataset["train"]  # N=1-7
test_data = dataset["test"]    # N=8-10 (OOD)

# Iterate over examples
for example in train_data:
    print(f"Difficulty N={example['N']}")
    print(f"Current state: {example['current_state']}")
    print(f"Next state: {example['next_state']}")
    print(f"Move: {example['move']}")
    print("---")
    break
```

### Loading All Puzzles

```python
from datasets import load_dataset

puzzles = ["block_world", "checkers_jumping", "tower_of_hanoi", "river_crossing"]

datasets = {
    puzzle: load_dataset("gmannem/RecurrReason", puzzle)
    for puzzle in puzzles
}

# Access specific puzzle
bw_train = datasets["block_world"]["train"]
```

### Example: Evaluating a Model

```python
from datasets import load_dataset

# Load test data (OOD, N=8-10)
test_data = load_dataset("gmannem/RecurrReason", "block_world", split="test")

def evaluate_model(model, test_data):
    """
    Evaluate model on RecurrReason benchmark.
    
    Metrics:
    - Success rate: % of puzzles solved correctly
    - Move validity: % of generated moves that are legal
    - Optimality gap: (model_length - optimal_length) / optimal_length
    """
    success_count = 0
    
    for example in test_data:
        # Your model prediction logic here
        predicted_next_state = model.predict(
            current_state=example['current_state'],
            goal_state=example['goal_state']
        )
        
        # Check if prediction matches ground truth
        if predicted_next_state == example['next_state']:
            success_count += 1
    
    success_rate = success_count / len(test_data)
    print(f"Success Rate: {success_rate:.2%}")
    
    return success_rate
```

---

## 📄 Paper & Code

**"Recurrent Reasoning on Symbolic Puzzles with Sequence Models"**  
Gowrav Mannem, Chowdhury Marzia Mahjabin, Jason Chen, Shivank Garg, Kevin Zhu  
*ICLR 2026 Workshop on Logical Reasoning of Large Language Models*

🔗 [Read on OpenReview](https://openreview.net/forum?id=ErgAON9dOW)  

📄 [PDF](https://openreview.net/pdf?id=ErgAON9dOW)

🔗 [GitHub Repository](https://github.com/gowravmannem/Recurrent-Reasoning-on-Puzzles)

---


## 📖 Citation

If you use RecurrReason in your research, please cite the following papers. This benchmark extends the puzzles introduced by Shojaee et al. (2025) with BFS-optimal trajectories, permutation augmentations, and systematic difficulty scaling.

```bibtex
@inproceedings{mannem2026recurrent,
  title={Recurrent Reasoning on Symbolic Puzzles with Sequence Models},
  author={Gowrav Mannem and Chowdhury Marzia Mahjabin and Jason Chen and Shivank Garg and Kevin Zhu},
  booktitle={ICLR 2026 Workshop on Logical Reasoning of Large Language Models},
  year={2026},
  url={https://openreview.net/forum?id=ErgAON9dOW}
}
@article{shojaee2025illusion,
  title={The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity},
  author={Shojaee, Parshin and Mirzadeh, Iman and Alizadeh, Keivan and Horton, Maxwell and Bengio, Samy and Farajtabar, Mehrdad},
  journal={arXiv preprint arXiv:2506.06941},
  year={2025}
}
```
---

## 📜 License

This dataset is licensed under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**.

**You are free to:**
- Share — copy and redistribute the dataset
- Adapt — remix, transform, and build upon the dataset
- For any purpose, even commercially

**Under the following terms:**
- **Attribution** — You must give appropriate credit by citing our paper

Full license text: https://creativecommons.org/licenses/by/4.0/

---

## 🤝 Contributing

Found an issue or have suggestions? Please:

1. Open an issue on [GitHub](https://github.com/gowravmannem/Recurrent-Reasoning-on-Puzzles/issues)
2. Use the "Discussions" tab on HuggingFace
3. Contact us at: gowravmannem@gmail.com

---

<div align="center">
**Built with ❤️ for the AI reasoning research community**
</div>