May 28, 2026 · Artificial Intelligence

How AutoMoT Leverages Large‑Model Understanding for End‑to‑End Driving Decisions and Trajectory Planning

AutoMoT introduces a unified Vision‑Language‑Action model that combines a 4B Qwen3‑VL understanding expert with a 1.6B action expert via layer‑wise shared attention and asynchronous inference, achieving state‑of‑the‑art results on Bench2Drive and nuScenes while preserving general VLM capabilities.

Asynchronous InferenceAutoMoTAutonomous Driving

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How AutoMoT Leverages Large‑Model Understanding for End‑to‑End Driving Decisions and Trajectory Planning

Baobao Algorithm Notes

Dec 22, 2025 · Artificial Intelligence

Which Agentic RL Framework Wins? A Deep Dive into AReal, Seer, Slime & verl

This article analyzes the training‑efficiency challenges of multi‑turn agentic reinforcement learning and compares four recent open‑source frameworks—AReal (Ant), Seer (Moonshot), Slime (Zhipu) and verl (Bytedance)—examining their asynchronous inference designs, rollout‑train separation, long‑context handling, off‑policy mitigation, and system‑level optimizations to guide framework selection.

Asynchronous InferenceRL SystemsTraining efficiency

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Which Agentic RL Framework Wins? A Deep Dive into AReal, Seer, Slime & verl