Update README.md

README.md

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 ---
 license: cc-by-sa-4.0
+language:
+  - en
+task_categories:
+  - text-classification
+  - reinforcement-learning
+  - question-answering
+  - text-generation
+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
+# PDDL2PRM: Planning-Based Step-Level Supervision for Process Reward Models
 
-PDDL2PRM is a large-scale dataset for training Process Reward Models (PRMs) with fine-grained step-level supervision derived from PDDL planning problems.
+**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.
 
-The dataset contains ~1M reasoning steps across 11 domains, where each step is automatically labeled using rule-based rewards reflecting executability, optimality, and progress toward the goal. This enables scalable, precise, and reproducible supervision beyond the mathematical domain.
+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.
 
-🔗 **Project page & paper**: https://pisanoraffaele.github.io/prm_meets_planning/
+The dataset is introduced in:
 
-## Key Features
-- ~1M reasoning steps
-- 14,714 planning problems
-- 5-level reward signal (from invalid to optimal)
-- Rule-based annotation
-- Multi-domain (planning, logic, navigation, puzzles)
+**Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards**  
+Raffaele Pisano and Roberto Navigli, ACL 2026
 
-## Reward Levels
-- 0.0 → Non-executable  
-- 0.25 → Dead-end  
-- 0.5 → Backtracking  
-- 0.75 → Suboptimal  
-- 1.0 → Optimal  
+🔗 **Project page & paper:** https://pisanoraffaele.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.
+
+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** |
+| Supervision type | Rule-based step-level rewards |
+| Data source | PDDL planning problems translated into natural language |
+
+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:
 
-## Note
-The data is structurally precise but generated via templates; it is most effective when combined with natural CoT datasets (e.g., PRM800k, Math-Shepherd).
+| 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 this dataset, please cite:
+If you use PDDL2PRM, please cite:
 
 ```bibtex
 @inproceedings{prmsmeetplanning2026,