October 2025 monthly summary for alibaba/rtp-llm: Key features delivered and reliability improvements focused on real-time weight updates and tensor transport. Delivered a real-time IPC-based weight update and tensor transport system enabling dynamic, low-latency weight updates and efficient inter-process tensor sharing for distributed or reinforcement learning workloads. Implemented JIT-based tensor IPC, batching, and HTTP server support, with integration into a weight manager, tensor cloning, and enhanced logging during transfers. Removed DTensor logic to ensure AMD compatibility and stable shared memory across PyTorch tensors. Completed maintenance enhancements: tooling, packaging, and cleanup for TIPC and Bazel packaging, including pre-commit rule updates and removal of legacy development files. Business impact: enables agile, real-time model updates across distributed training/inference stacks, reduces latency, improves stability on AMD hardware, and lowers CI/build maintenance overhead.