Update README.md

README.md

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 **KaLM-Reranker-V1-R2 is a substantially improved checkpoint release of KaLM-Reranker-V1.** It keeps the same fast-but-not-late-interaction (FBNL) architecture and inference interface as the original release, while introducing a stronger multi-stage training recipe for three practical goals:
 
 1. **Stronger compression robustness.** R2 supports Matryoshka embedding pooling (MEP) from **1x to 128x**, extending the maximum validated compression ratio from 32x to 128x. Even at 128x compression, all three model sizes retain at least 92% of their average nDCG@10 at 2x compression on both BEIR and MIRACL.
-2. **Adjustable test-time compute.** A single R2 checkpoint can trade compute for quality by changing the compression ratio. It can also use a coarse-to-fine cascade: cheaply screen all candidates with heavily compressed document representations, then spend more compute only on the most promising candidates.
+2. **Adjustable test-time compute scaling.** A single KaLM-Reranker-V1-R2 checkpoint can trade compute for quality by changing the compression ratio. It can also use a coarse-to-fine cascade: cheaply screen all candidates with heavily compressed document representations, then spend more compute only on the most promising candidates.
 3. **Better multi-domain and multilingual ranking.** At the default 4x compression ratio, every R2 model improves on BEIR, while MIRACL average nDCG@10 increases by **+9.03**, **+7.17**, and **+4.85** points for Nano, Small, and Large, respectively.