(docs): update readme.

Author: PanAndy

README.md

@@ -158,7 +158,7 @@ We are continuously working to expand ROLL's capabilities:
 
 ## 🏆 Notable work based on ROLL
 - [EARL](https://arxiv.org/abs/2510.05943): Efficient Agentic RL Systems for LLMs, introducing a dynamic parallelism selector and a layout-aware data dispatcher to boost throughput, reduce memory and data movement bottlenecks, enabling stable large-scale agentic RL without hard context-length limits.
-- [LiveThinking](https://arxiv.org/abs/2510.07685): Real-time reasoning for AI-powered livestreaming by distilling a 670B teacher LLM to a 30B MoE (3B active) via Rejection Sampling Fine-Tuning, then compressing reasoning with GRPO; delivers sub-second latency and ~30x compute reduction, with gains in response correctness (3.3%), helpfulness (21.8%), and GMV in Taobao Live.
+- [LiveThinking](https://arxiv.org/abs/2510.07685): Real-time reasoning for AI-powered livestreaming by distilling a 670B teacher LLM to a 30B MoE (3B active) via Rejection Sampling Fine-Tuning, then compressing reasoning with GRPO; delivers sub-second latency and ~30x compute reduction, with gains in response correctness (3.3%), helpfulness (21.8%), and GMV in Taobao Live Digital Live Service.
 - [TaoSR-AGRL](https://www.arxiv.org/abs/2510.08048): Adaptive Guided Reinforcement Learning for LLM-based e-commerce relevance, introducing Rule-aware Reward Shaping and Adaptive Guided Replay to improve long-horizon reasoning, rule adherence, and training stability in Taobao Search; deployed in main search handling hundreds of millions of users.
 - [RecGPT](https://www.arxiv.org/abs/2507.22879): a next-generation, LLM-driven framework that places user intent at the core of recommender systems, fostering a more sustainable and mutually beneficial ecosystem.
 - [TaoSR1](https://arxiv.org/abs/2508.12365): A novel LLM framework directly deploying Chain-of-Thought (CoT) reasoning for e-commerce query-product relevance prediction, overcoming deployment challenges for superior performance.