diff --git a/README.md b/README.md
index a33de51a26cccb6a21a2411f88a0706864415039..eadfca8d70310e93725542d014c5840abd58c02d 100644
--- a/README.md
+++ b/README.md
@@ -10,17 +10,10 @@ This repository contains the C++ implementation accompanying the AAAI-25 confere
 The centralized training for decentralized execution paradigm emerged as the state-of-the-art approach to $\epsilon$-optimally solving decentralized partially observable Markov decision processes. However, scalability remains a significant issue.
 
 This paper presents a novel and more scalable alternative, namely the sequential-move centralized training for decentralized execution.
-
-This paradigm further pushes the applicability of \citeauthor{bellman}'s principle of optimality, raising three new properties. 
-
 First, it allows a central planner to reason upon sufficient sequential-move statistics instead of prior simultaneous-move ones.
-
 Next, it proves that $\epsilon$-optimal value functions are piecewise linear and convex in such sufficient sequential-move statistics.
-
 Finally, it drops the complexity of the backup operators from double exponential to polynomial at the expense of longer planning horizons.  
 
-Besides, it makes it easy to use single-agent methods, \eg SARSA algorithm enhanced with these findings, while still preserving convergence guarantees.
-
 Experiments on two- as well as many-agent domains from the literature against $\epsilon$-optimal simultaneous-move solvers confirm the superiority of our novel approach.
 
 This paradigm opens the door for efficient planning and reinforcement learning methods for multi-agent systems.