Mudith Jayasekara

The RL post-training moment Over the past year, open-source models have continued to scale their architectures and intelligence. Demand for post-training has surged to specialize these models through supervised finetuning (SFT) and reinforcement learning (RL). As ML teams build more advanced main agents, voice systems, and long-horizon reasoning systems, RL is quickly becoming the default method to achieve frontier-level, task-specific performance.

The problem: emergency documentation is different Emergency department notes aren't like other medical documentation. Information comes from multiple sources simultaneously: the patient, EMS, family members, and prior records. The clinician must document not just what happened but also what didn't happen, in particular by explicitly ruling out dangerous diagnoses.

Today we are excited to share that Parsed is joining Baseten. From the beginning, we started Parsed with a simple belief: AI wasn’t doing nearly enough real work. Models were powerful, but they weren’t specialized, aligned, or connected to the real systems where they could create value. The world didn’t need ever-bigger models; it needed models that understand context deeply, perform reliably, and can be trusted with actual responsibility.