Updated README with generic description of the work

Author: imbesat-rizvi

README.md

@@ -13,6 +13,8 @@
 
 This repository includes the training, inference and evaluation code used in our Arxiv 2025 paper - [SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling](). 
 
+We introduced a principled framework for a single-pass alignment and step-annotation for automatic process supervision. Process Reward Models (SPARE-PRMs) trained based on the proposed annotation scheme outperform baselines such as Self-Consistency and ORM-weighted aggregation on four datasets across mathematical, question-answering and spatial reasoning datasets. The annotation scheme is also competitive while being computationally efficient compared to tree-search based annotation methods.
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