Update README.md

README.md

@@ -42,6 +42,15 @@ That's why we designed simplified architectures, for incremental transformation
 - **Reactive Transformer** is introducing _Attention-based Memory System_ and adding _Short-Term Memory_ to Transformer language models
 - **Preactor** is adding _Long-Term Memory_ and ability to learn from interactions
 
+## RxLM vs LLM advantages
+Processing single interactions in real-time by **Reactive Language Models** leads to **revolutional** improvements in inference speed/cost:
+- LLM inference costs are increasing exponentially with conversation length (accumulated for each next message), because of full dialog history processing
+- RxLM inference costs are linear, depending only on single interaction tokens (not accumulated) - each next interaction is `number of steps` times cheaper than for LLM
+- same for inference speed - LLM has to process full history, while RxLM only single message (only first interaction could be slower because of encoder/memory attention overhead)
+
+> In example, for a dialog with **DeepSeek R1**, that have overally ~90k tokens, I paid for about 1.5M tokens. With **RxLM** it will cost only that ~90k tokens, so it
+> will be about **15x cheaper**
+
 ### RxT-Alpha Open Research
 We are currently working on **Reactive Transformer Proof-of-Concept - RxT-Alpha**, especially on the new reinforcement learning stage - **Memory Reinforcement Learning**,
 that's required for our reactive models, between the _Supervised Fine-Tuning_ and _Reinforcement Learning from Human Feedback for reactive models (RxRLHF)_. The research