Update README.md

README.md

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 - **Point of Contact:** [Lukas Helff](mailto:helff@cs.tu-darmstadt.de)
 - **License:** [CC BY](https://creativecommons.org/licenses/by/4.0/)
 
-# SLR-Bench: Scalable Logical Reasoning Benchmark for LLMs
+# 🧠 SLR-Bench-German: Scalable Logical Reasoning Benchmark (German Edition)
 [![Eval & Reward Model](https://img.shields.io/badge/%F0%9F%A4%96%20Reward%20Model-HF-blueviolet)](https://huggingface.co/spaces/AIML-TUDA/VerifiableRewardsForScalableLogicalReasoning)
 [![GitHub](https://img.shields.io/badge/Code-GitHub-blue)](https://github.com/ml-research/ScalableLogicalReasoning)
 [![arXiv](https://img.shields.io/badge/arXiv-2506.15787-b31b1b.svg)](https://arxiv.org/abs/2506.15787)
+[![SLR-Bench (English)](https://img.shields.io/badge/SLR--Bench-English-orange)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench)
+[![SLR-Bench-German](https://img.shields.io/badge/SLR--Bench-German-red)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench-German)
 
+**SLR-Bench-German** is the **German-language pendant** of the original [**SLR-Bench**](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench) dataset.
+It follows the same symbolic structure, evaluation framework, and curriculum as the English version but provides all **natural-language task prompts translated into German**.
 
-
-> **🆕 August 2025: Build your own Reasoning Problems with Verifiable Rewards. Source Code is now available!** 👉 [Generate your own Reasoning Task](https://github.com/ml-research/ScalableLogicalReasoning)
-
-> **🆕 June 2024: Evaluation & RLVR Reward Model Released!** 👉 [Demo on Hugging Face Spaces](https://huggingface.co/spaces/AIML-TUDA/VerifiableRewardsForScalableLogicalReasoning)
-
-**SLR-Bench** is a scalable, fully-automated benchmark designed to systematically evaluate and train Large Language Models (LLMs) in logical reasoning via inductive logic programming (ILP) tasks. Built with the [SLR framework](https://github.com/ml-research/ScalableLogicalReasoning), SLR-Bench presents LLMs with open-ended logic problems of progressively increasing difficulty, assesses their solutions via deterministic symbolic evaluation, and supports both curriculum learning and systematic measurement of reasoning performance.
-
+This enables systematic evaluation and training of Large Language Models (LLMs) in logical reasoning in german, supporting both *multilingual reasoning* and *cross-lingual generalization* research.
 
 ## DS Overview
 - **Curriculum:** 20 complexity levels, grouped into 4 broad tiers (basic, easy, medium, hard)