(docs): update readme.

Author: PanAndy

README.md

@@ -41,6 +41,7 @@ Leveraging a multi-role distributed architecture with Ray for flexible resource
 | 📣   Updates                                                                                                                                                                                                                                                                                                                            |
 |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | **[09/28/2025]** 🎉 Ascend NPU support — see [usage guide](https://alibaba.github.io/ROLL/docs/English/UserGuide/ascend/ascend_usage).                                                                                                                                                                                          |
+| **[09/25/2025]** 🎉 Our Paper released, see [RollPacker: Mitigating Long-Tail Rollouts for Fast, Synchronous RL Post-Training](https://arxiv.org/abs/2509.21009)                                                                                                                                                                        |
 | **[09/24/2025]** 🎉 Support [Wan2_2 Reward FL pipeline](examples/wan2.2-14B-reward_fl_ds/reward_fl_config.yaml). Explore the new capabilities!                                                                                                                                                                                          |
 | **[09/23/2025]** 🎉 ROLL aligns with GEM environment definition, providing agentic Tool Use training capabilities, [ToolUse docs](docs_roll/docs/English/UserGuide/agentic/Tool_Use.md).                                                                                                                                                |
 | **[09/16/2025]** 🎉 Qwen3-Next model training is supported, refer to [configuration](examples/qwen3-next-80BA3B-rlvr_megatron/rlvr_config.yaml).                                                                                                                                                                                        |
@@ -156,6 +157,9 @@ 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.
+- [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.
 - [AIGB-Pearl](https://www.arxiv.org/abs/2509.15927): a novel auto-bidding method that integrates generative planning and policy optimization, utilizing an LLM-enhanced trajectory evaluator to iteratively refine bidding strategies for state-of-the-art advertising performance.