Update README.md

Author: codelion

README.md

@@ -161,33 +161,6 @@ See the [Configuration Guide](configs/default_config.yaml) for a full list of op
 
 See the `examples/` directory for complete examples of using OpenEvolve on various problems:
 
-### 🚀 MLX Fine-tuning Optimization (NEW!)
-
-**OpenEvolve discovered a 17.3x speedup for MLX fine-tuning on Apple Silicon!** This example demonstrates how evolutionary programming can automatically discover performance optimizations that exceed what human engineers typically achieve.
-
-[Explore the MLX Fine-tuning Optimization Example](examples/mlx_finetuning_optimization/)
-
-**Breakthrough Results Achieved:**
-- **17.3x faster training throughput** (120 → 2,207 tokens/sec)
-- **9.4x better memory efficiency** (0.075 → 0.78 tokens/sec/MB)
-- **65% faster training completion** (65.8s → 23.2s)
-- **6.4x more data processed** in the same time
-
-**Key AI-Discovered Optimizations:**
-- Block-diagonal chunked attention (reduces memory complexity)
-- True sequence packing (eliminates padding waste)
-- Aggressive fp16 gradient accumulation (50% memory savings)
-- Coordinated 256-token chunking (Apple Silicon optimized)
-- Ultra-frequent garbage collection (prevents memory pressure)
-
-**Ready-to-Use Integration:**
-```python
-from mlx_optimization_patch import apply_optimizations
-apply_optimizations(your_trainer)  # One line. 17x speedup.
-```
-
-This example parallels AlphaEvolve's Gemini kernel optimization work, where AI discovered a 23% speedup for Google's production training systems. Our MLX optimizations achieve even more dramatic improvements specifically for Apple Silicon fine-tuning.
-
 ### Symbolic Regression
 
 A comprehensive example demonstrating OpenEvolve's application to symbolic regression tasks using the LLM-SRBench benchmark. This example shows how OpenEvolve can evolve simple mathematical expressions (like linear models) into complex symbolic formulas that accurately fit scientific datasets.