Update README.md

README.md

@@ -157,6 +157,14 @@ On LMEB-Dialogue, a compact embedding model paired with our Nano reranker, which
 ![lmeb](./assets/lmeb.jpg)
 ![lmeb_emb](./assets/lmeb_emb.jpg)
 
+#### Ablation on multi-stage training
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+Across all three model sizes and all seven compression ratios, performance on BEIR and MIRACL improves consistently from Stage 1 to Stage 3, demonstrating the effectiveness of our multi-stage training pipeline. Concretely, Stage 1 establishes a robust foundation for document reranking, distillation in Stage 2 substantially improves performance, and Stage 3 yields further modest gains. More importantly, robustness to compression generally improves across the three training stages. For example, from Stage 1 to Stage 3, the performance retention of KaLM-Reranker-V1-Nano at r = 128 relative to r = 2 increases from 92.88% to 93.80% on BEIR and from 90.93% to 92.15% on MIRACL.
+
+![lmeb_emb](./assets/ablation.jpg)
+
+![kalm-reranker-v1-r2 training](./assets/training.jpg)
+
 ### Acknowledgements
 We sincerely thank `jina-reranker-v3` and `Qwen3-Reranker` for their valuable inspiration and contributions to the reranking community, from which we have learned a lot.