Update README.md

README.md

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 ### Key features
 + **Sparse MoE architecture:** 128 routed experts, with only 8 routed experts and 1 shared expert activated per token. This enables broad model capabilities while activating just 1.3B parameters per token; 
 + **Native hybrid linear attention:** Ling-3.0 series adopt a native hybrid linear attention architecture from the very start of pretraining by combining KDA with Gated MLA to enable efficient processing of long-context inputs.
-+ [**Warmup-Stable and Merge**](https://arxiv.org/abs/2507.17634)**: **We replace conventional learning-rate decay with weighted checkpoint merging. By eliminating the decay phase, our Base Model is better suited for continual pretraining and dynamic data expansion, while enabling offline exploration of different decay profiles without rerunning costly experiments for each strategy.
++ **[**Warmup-Stable and Merge](https://arxiv.org/abs/2507.17634):** We replace conventional learning-rate decay with weighted checkpoint merging. By eliminating the decay phase, our Base Model is better suited for continual pretraining and dynamic data expansion, while enabling offline exploration of different decay profiles without rerunning costly experiments for each strategy.
 + **Scale Seamlessly:** Ling-3.0-tiny-base and Ling-3.0-flash-base share the same training recipe, enabling community to experiment on the Ling-3.0-tiny-base first and then scale validated training strategies to the larger Ling-3.0-flash-base.