Powering Inference for the Continual Learning Era

This post details the infrastructure built with Trajectory to support continual learning. Key contributions include a pipeline that merges LoRA adapters into base models, performing architecture-aware correctness checks (handling fused projections, MXFP4 quantization, and MoE expert broadcasting). The merged model is packaged with Truss. The serving runtime applies optimizations like quantization and RoPE scaling, with automatic validation of checkpoints against these constraints. A routing layer treats new checkpoints as experimental variants, splitting traffic and partitioning telemetry for comparison. Every request is logged with provenance, closing the loop back to training.