fix: correct typos and broken link in README

## Summary

Fix two typo and one link rendering failure due to unencoded path in README.md:

- **`DeepSeed` → `DeepSpeed`** (Line 110, Backend section)

- **`StartPO` → `StarPO`** (Line 137, Key Features section)

- **`User Guides` → `User%20Guides`** (Line 46, on-policy distill link)

Author: Galleons2029

README.md

@@ -43,7 +43,7 @@ Leveraging a multi-role distributed architecture with Ray for flexible resource
 
 | 📣   Updates                                                                                                                                                                                                                                                                                                                                                        |
 |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| **[03/06/2026]** 🎉 We support Qwen3.5 [Dense](examples/qwen3.5-35BA3-rlvr_megatron/rlvr_megatron_80GB.yaml) and [MoE](examples/qwen3.5-35BA3-rlvr_megatron/rlvr_megatron_80GB.yaml) series models and [on-policy distill](docs_roll/i18n/zh-Hans/docusaurus-plugin-content-docs/current/User Guides/Pipeline/on_policy_distill_pipeline_start.md). Welcome to use! |
+| **[03/06/2026]** 🎉 We support Qwen3.5 [Dense](examples/qwen3.5-35BA3-rlvr_megatron/rlvr_megatron_80GB.yaml) and [MoE](examples/qwen3.5-35BA3-rlvr_megatron/rlvr_megatron_80GB.yaml) series models and [on-policy distill](docs_roll/i18n/zh-Hans/docusaurus-plugin-content-docs/current/User%20Guides/Pipeline/on_policy_distill_pipeline_start.md). Welcome to use! |
 | **[02/03/2026]** 🎉 We released FSDP2 Strategy, Megatron with LoRA, GPU partial overlapping, Qwen3-Omni supports and other features. For more details, please refer to the release notes. Welcome to use!                                                                                                                                                           |
 | **[01/01/2026]** 🎉 Our [Let It Flow: Agentic Crafting on Rock and Roll](https://arxiv.org/abs/2512.24873) report released! Introducing ALE ecosystem and ROME, an open-source agentic model with novel IPA algorithm.                                                                                                                                              |
 | **[11/08/2025]** 🎉 Our [ROCK: Reinforcement Open Construction Kit](https://github.com/alibaba/ROCK) released, Explore the new capabilities!.                                                                                                                                                                                                                       |
@@ -107,7 +107,7 @@ Leveraging a multi-role distributed architecture with Ray for flexible resource
 [RewardFL](https://alibaba.github.io/ROLL/docs/User%20Guides/Algorithms/Reward_FL)
 
 #### Backend
-[DeepSeed](https://alibaba.github.io/ROLL/docs/User%20Guides/Configuration/deepspeed)  
+[DeepSpeed](https://alibaba.github.io/ROLL/docs/User%20Guides/Configuration/deepspeed)  
 [Megatron](https://alibaba.github.io/ROLL/docs/User%20Guides/Configuration/megatron)   
 [vLLM](https://alibaba.github.io/ROLL/docs/User%20Guides/Configuration/vllm)  
 [SGLang](https://alibaba.github.io/ROLL/docs/User%20Guides/Configuration/sglang)
@@ -134,7 +134,7 @@ Leveraging a multi-role distributed architecture with Ray for flexible resource
     *   Environment-level **asynchronous parallel rollout**.
     *   Supports **asynchronous training**.
     *   Multi-turn interaction rollout supports **local debugging**, improving multi-turn interaction business development efficiency.
-    *   Supports **TrajectoryWise (StartPO)** and **StepWise (GiGPO)** training paradigms.
+    *   Supports **TrajectoryWise (StarPO)** and **StepWise (GiGPO)** training paradigms.
 *   **Algorithm-Friendly:** Provides flexible and rich RL strategy configurations by default.
     *   Over 20 rich reinforcement learning strategy options, such as reward normalization, reward clipping, various advantage estimation methods, etc.
     *   Out-of-the-box support for reinforcement learning algorithms, such as **PPO, GRPO, Reinforce++, TOPR, RAFT++, GSPO**, etc.