Update README.md

README.md

@@ -41,10 +41,9 @@ Raffaele Pisano and Roberto Navigli, ACL 2026
 
 ## 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.
+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;
@@ -65,8 +64,6 @@ PDDL2PRM contains:
 | 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.
 
@@ -94,7 +91,6 @@ PDDL2PRM includes 11 planning domains:
 | **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.