---
license: cc-by-sa-4.0
language:
  - en
task_categories:
  - text-classification
  - reinforcement-learning
tags:
  - process-reward-models
  - prm
  - reasoning
  - chain-of-thought
  - planning
  - pddl
  - step-level-supervision
  - synthetic-data
  - logical-reasoning
  - reward-modeling
pretty_name: PDDL2PRM
size_categories:
  - 100K<n<1M
---

# PDDL2PRM: Planning-Based Step-Level Supervision for Process Reward Models

**PDDL2PRM** is a large-scale dataset for training and evaluating **Process Reward Models (PRMs)** with fine-grained, step-level supervision derived from symbolic planning problems.

Unlike many PRM datasets that rely on human annotation, LLM judges, or final-answer correctness, PDDL2PRM uses **Planning Domain Definition Language (PDDL)** problems to generate structured reasoning trajectories whose intermediate steps can be evaluated automatically. Each reasoning step is assigned a scalar reward according to rule-based criteria that reflect whether the corresponding action is executable, goal-preserving, efficient, and optimal.

The dataset is introduced in:

**Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards**  
Raffaele Pisano and Roberto Navigli, ACL 2026

🔗 **Project page & paper:** https://babelscape.github.io/prm-meets-planning/
📄 **arXiv:** https://arxiv.org/abs/2604.17957

---

## Why PDDL2PRM?

Process Reward Models aim to evaluate reasoning **step by step**, rather than judging only the final answer. This is crucial because language models can produce a correct final answer while still making invalid, inconsistent, or unsupported intermediate reasoning steps (Turpin et al., 2023; Lightman et al., 2024; Zheng et al., 2025;  Molfese et al., 2026).

However, high-quality step-level supervision is difficult to obtain:
- manual annotation is expensive and hard to scale;
- LLM-based annotation can be noisy and computationally costly;
- many existing PRM datasets focus primarily on mathematics;
- binary or coarse labels provide limited information about *how* a reasoning step fails.

PDDL2PRM addresses these limitations by using planning problems as a source of **precise, scalable, and reproducible supervision**. In PDDL, states, actions, preconditions, effects, and goals are explicitly defined, making it possible to assign rewards to reasoning steps using deterministic rules rather than subjective annotation.

---

## Dataset Overview

PDDL2PRM contains:

| Statistic | Value |
|---|---:|
| Reasoning steps | **984,974** |
| Planning problems | **14,714** |
| Domains | **11** |
| Reward levels | **5** |
| Mean optimal plan length | **7.94** |

Each example consists of a planning problem expressed in natural language, a partial reasoning trajectory, and a reward assigned to an intermediate step.

The dataset covers a diverse set of planning domains, including object manipulation, transportation, navigation, puzzles, and constraint-satisfaction tasks.

---

## Domains

PDDL2PRM includes 11 planning domains:

| Domain | Problems | Total Steps |
|---|---:|---:|
| BlocksWorld-3 | 952 | 51,990 |
| BlocksWorld-4 | 1,796 | 125,402 |
| Ferry | 858 | 65,269 |
| Hanoi | 1,660 | 65,745 |
| Logistics | 669 | 70,238 |
| Elevator | 2,089 | 207,549 |
| N-Puzzle | 598 | 42,027 |
| Rooms | 916 | 37,947 |
| Sokoban | 437 | 58,475 |
| Spanner | 3,563 | 198,942 |
| VisitGrid | 1,176 | 61,390 |
| **Total** | **14,714** | **984,974** |

The domains are grouped into broad reasoning families:
- **Manipulation and rearrangement:** BlocksWorld variants.
- **Transportation:** Ferry, Logistics, Elevator.
- **Puzzles and constraints:** Tower of Hanoi, N-Puzzle.
- **Navigation and exploration:** VisitGrid, Sokoban, Rooms, Spanner.

---

## Reward Signal

Each reasoning step is assigned one of five scalar rewards:

| Reward | Label | Meaning |
|---:|---|---|
| **0.0** | Non-executable | The action cannot be applied because its preconditions are not satisfied. |
| **0.25** | Dead-end | The action leads to a state from which the goal is unreachable. |
| **0.5** | Backtracking | The action leads to a state from which an optimal plan requires revisiting a previous state. |
| **0.75** | Suboptimal | The action is valid but does not belong to any optimal plan. |
| **1.0** | Optimal | The action belongs to at least one optimal plan from the current state. |

This reward structure provides more information than binary correct/incorrect supervision. It distinguishes between different types of flawed reasoning, including invalid actions, irreversible mistakes, inefficient detours, and merely suboptimal decisions.

---

## How the Dataset Was Generated

PDDL2PRM is generated from symbolic planning problems through the following pipeline:

1. **Parse PDDL domains and problems.**  
   Planning instances define states, actions, preconditions, effects, initial states, and goals.

2. **Sample candidate actions at each state.**  
   For each state along a trajectory, multiple candidate actions are considered, including valid, invalid, suboptimal, and optimal actions.

3. **Evaluate each action with planning-based rules.**  
   Actions are labeled according to executability, reachability of the goal, backtracking behavior, and membership in an optimal plan.

4. **Translate actions into natural language reasoning steps.**  
   Each symbolic action is converted into a natural-language reasoning step and paired with its scalar reward.

5. **Continue until the goal is reached.**  
   The procedure produces both correct and incorrect reasoning trajectories, enabling PRMs to learn fine-grained distinctions between different reasoning behaviors.

---

## Key Findings from the Paper

Training with PDDL2PRM improves PRM robustness and generalization.

In the paper, PRMs trained on datasets augmented with PDDL-derived supervision outperform comparable models trained without the PDDL component. Improvements are especially strong on non-mathematical and planning-based reasoning benchmarks.

The results suggest that planning-derived supervision helps PRMs detect not only local execution errors, but also deeper modeling errors such as invalid assumptions, inappropriate reductions, and unjustified constraints.

---

## Dataset Card Note

PDDL2PRM is released under the **CC BY-SA 4.0** license.

Because the dataset is derived from symbolic planning problems and template-based natural-language translations, users should consider it a high-precision source of structured reasoning supervision rather than a replacement for naturally occurring Chain-of-Thought data.

---

## Citation

If you use PDDL2PRM, please cite:

```bibtex
@inproceedings{prmsmeetplanning2026,
  title={Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards},
  author={Pisano, Raffaele and Navigli, Roberto},
  booktitle={Proceedings of ACL 2026},
  year={2026}
}