Skill Discovery

TL;DR for operators A system can discover agent “skills” that look coherent to humans and still fail to make an agent more capable. That is the useful result of Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining.1 The authors build a pipeline that cuts GUI interaction histories into segments, clusters those segments into candidate routines, generates explicit skill specifications, and trains a Qwen3-8B policy with Group Relative Policy Optimization, or GRPO, to compose the resulting skills. ...

TL;DR for operators Fleets do not fail only because they forget. They also fail because they remember the wrong thing at the wrong time. That is the practical point of COMAD, a framework for continual offline multi-agent reinforcement learning proposed in Offline Multi-agent Continual Cooperation via Skill Partition and Reuse.1 The paper studies agents that must learn from a stream of offline datasets: first one cooperative task, then another, then another, without interactive trial-and-error and without assuming the required coordination skills stay fixed. That setting is awkward, which is why it is useful. Real deployed systems rarely receive the courtesy of a clean, stationary benchmark and a polite email before the operating conditions change. ...

Automation has a boring failure mode: the moment the world becomes slightly more complicated than the workflow diagram, the system starts asking for a human. That is not because the model lacks vocabulary. It is because the automation system does not know how to grow its own capabilities. Most AI agents are still built around a fixed menu of actions, fixed task definitions, and fixed reward signals. They can optimize, but they rarely expand the set of things they know how to optimize for. Very impressive, in the way a microwave is impressive until you ask it to cook without buttons. ...