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|**[10/23/2025]** 🎉 Our Papers released, see [Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning](https://arxiv.org/abs/2510.01656) and [Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization](https://arxiv.org/abs/2510.13554). |
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|**[10/14/2025]** 🎉 Our Paper released, see [Part II: ROLL Flash -- Accelerating RLVR and Agentic Training with Asynchrony](https://arxiv.org/abs/2510.11345), the code will be released soon. |
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|**[09/28/2025]** 🎉 Ascend NPU support — see [usage guide](https://alibaba.github.io/ROLL/docs/English/UserGuide/ascend/ascend_usage). |
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|**[09/25/2025]** 🎉 Our Paper released, see [RollPacker: Mitigating Long-Tail Rollouts for Fast, Synchronous RL Post-Training](https://arxiv.org/abs/2509.21009)|
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## 🏆 Notable work based on ROLL
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-[IPRO](https://arxiv.org/abs/2510.14255): A novel video diffusion framework using reinforcement learning to enhance identity preservation in human-centric I2V generation, optimizing diffusion models with face identity scorer and KL-divergence regularization.
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-[TaoSR-SHE](https://arxiv.org/abs/2510.07972): Stepwise Hybrid Examination Reinforcement Learning Framework for Taobao Search Relevance, with SRPO (hybrid reward model + offline verifier), diversified data filtering, and multi-stage curriculum learning.
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-[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.
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-[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.
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